Insights – JurisTech

Why Banking CIOs are Investing in GenAI Solutions Now

JurisTech — Wed, 21 Aug 2024 11:13:13 +0000

In an industry where milliseconds can mean millions, the difference between leading the market and lagging behind often hinges on a single question: How effectively is your organisation leveraging advanced technologies? For CIOs in the banking industry, the urgency to adopt Generative AI (GenAI) solutions has never been greater. According to the 2024 Gartner CIO and Technology Executive Survey, 42% of banking CIOs have already deployed or are currently deploying GenAI within their organisations. This statistic isn’t just a number; it’s a clear indicator of a pivotal shift that’s reshaping the industry. For example, banks can strategically adopt GenAI solutions in digital customer onboarding, credit risk assessment, customer service and support and more. Thus, the question is no longer whether to adopt GenAI solutions, but how to leverage it effectively to stay ahead in a competitive industry.

Strategic Benefits of GenAI for Banks

Meeting Rising Customer Expectations

Traditional banking models, with their one-size-fits-all approach, are no longer sufficient for an era where customers demand seamless and personalised experiences. This is where GenAI steps in. GenAI solutions provide banks with the tools to deliver tailored services that enhance customer satisfaction. For instance, the internal GenAI chatbot can assist banks in analysing customer data to offer highly personalised product recommendations that are both relevant and engaging. Thanks to GenAI’s capability to process and analyse unstructured data which allows banks to gain deeper insights into customer behaviour. This means that banks can anticipate customer needs and respond proactively, further enhancing the customer experience. As a result, banks that effectively implement GenAI solutions stand to gain a competitive edge in attracting and retaining customers.

According to the 2023 Gartner Financial Service Research Panel Survey, while many use cases for GenAI are initially focused on internal operations as banking leaders evaluate its benefits and risks, the potential for customer-facing applications is substantial. For example, the customer-facing GenAI chatbots can handle a wide range of customer inquiries, from checking account balances to providing tailored financial advice. Unlike human agents, who may have limitations in terms of availability and consistency, GenAI chatbots are available 24/7, ensuring that customers receive prompt and accurate responses at any time of day. This level of service not only enhances customer satisfaction but also builds loyalty.

Enhancing Operational Efficiency

Source: Frimufilms

Beyond customer-facing benefits, GenAI solutions are also revolutionising the internal operations of banks. One of the most significant advantages of the GenAI solution is its ability to automate routine tasks, thereby reducing operational costs and increasing efficiency. In an industry where time is money, this is a game-changer.

For example, GenAI solutions are transforming fraud detection by analysing vast amounts of transaction data in real-time to uncover suspicious activities. The GenAI solutions leverage machine learning algorithms to identify patterns and anomalies indicative of fraudulent behaviour. SouthState Bank illustrates this benefit by employing the enterprise version of OpenAI’s ChatGPT to assist staff with various internal tasks, including composing emails, querying banking policies, and analysing both suspicious activities and potential fraud. By automating these processes, banks can drastically cut down on the time and resources previously required for manual fraud detection, enabling them to concentrate on more strategic initiatives and drive overall operational efficiency.

Elevating Intelligent Decision-Making

According to a report by the McKinsey Global Institute, the global banking industry could see an increase in value between $200 billion and $340 billion annually through the effective use of GenAI. This value is not just from improved decision-making but also from enhanced customer satisfaction and reduced risks through better fraud monitoring. In a highly competitive and complex industry like banking, the ability to make informed decisions quickly can be a significant competitive advantage. Hence, banking CIOs need to adopt GenAI solutions as it enables them to analyse large datasets, identify trends, and make data-driven decisions with confidence.

For example, GenAI solutions can enhance credit decisions by integrating traditional credit data, alternative data, and synthetic data. While traditional and alternative data provide a solid foundation for assessing credit risk and customer behaviour, synthetic data generated by GenAI solutions simulate various scenarios and customer profiles. This allows banks to test and refine their credit models under diverse conditions, leading to more precise assessments and tailored lending solutions. By leveraging these data sources, GenAI solutions help banks make smarter, more informed decisions, improving risk management and customer satisfaction.

Why the Clock is Ticking for GenAI Investment

Competitive Pressures and Market Shifts

The banking industry is undergoing rapid transformation, and GenAI solutions are at the forefront of this change. Early adopters of GenAI solutions are already reaping the benefits, gaining market share, and strengthening customer loyalty. For those who lag behind, the consequences could be dire. But what’s driving this urgency, and why should you care?

The competitive landscape is evolving quickly, with new fintech startups and tech giants entering the financial services space. These new players are not burdened by legacy systems and can adopt GenAI more quickly, giving them an edge over traditional banks. To remain competitive, banking CIOs must act swiftly to integrate GenAI into their operations. Banks that are slow to adopt GenAI solutions risk being left behind as customers gravitate toward more innovative and customer-centric organisations. In a market where customer loyalty is increasingly tied to the quality of digital experiences, the cost of delay could be substantial.

Cost of Delay

Delaying investments in GenAI not only puts banks at a competitive disadvantage but also exposes them to increased operational inefficiencies and missed opportunities. How much longer can you afford to wait before these missed opportunities become permanent losses? The longer a bank waits to adopt GenAI solutions, the more it risks falling behind its competitors in terms of both technology and customer satisfaction. For instance, a bank that fails to implement GenAI-powered fraud detection may continue to rely on outdated manual processes, leading to higher fraud losses and increased operational costs. Similarly, banks that do not leverage GenAI solutions for personalised customer experiences may lose customers to competitors that offer more tailored services. This clearly shows that banks that are slow to embrace AI may find themselves struggling to catch up, facing higher costs to implement AI solutions later, and losing market share to more agile competitors.

The Future of GenAI in Banking

Anticipated Growth and Adoption

Source: Statista Research Department, 2024

As we look to the future, the adoption of GenAI in banking is expected to accelerate. Gartner predicts that by 2026, over 80% of banks will have adopted GenAI. Furthermore, Statista Research Department also highlights that the banking industry is set to boost its spending on generative AI, projected to reach $84.99 billion by 2030, fueled by a remarkable 55.55% annual growth rate. In other words, the integration of GenAI into banking is not just a trend; it’s a long-term strategic shift. Banks that invest in AI today are positioning themselves to lead the industry in the years to come. As GenAI technology continues to evolve, it will open up new opportunities for banks to innovate and differentiate themselves in an increasingly competitive industry.

Preparing for Change

For banking CIOs, the key to successfully navigating the future of GenAI in banking is agility. As AI technology continues to evolve, so too must the strategies of the banks that use it. This means continuously updating digital strategies, investing in AI talent, and staying informed about the latest advancements in AI technology. Additionally, banking CIOs must be prepared to adapt to the changing regulatory landscape. As AI becomes more deeply integrated into banking operations, regulators are likely to impose new rules and guidelines to ensure that AI is used responsibly and ethically. Banks that are proactive in addressing these challenges will be better positioned to capitalise on the benefits of GenAI.

Conclusion

The message is clear: the time to invest in GenAI solutions is now. For banking CIOs, the decision to invest in GenAI solutions is not just about staying competitive; it’s about leading their organisations into the future of banking. As we’ve explored, the strategic benefits of GenAI are vast, from enhancing customer experiences to improving operational efficiency and decision-making. However, the cost of delay is equally significant, with potential losses in market share, customer loyalty, and operational efficiency.

To stay ahead of the curve, banking CIOs must act now, investing in GenAI solutions and integrating it into their digital strategies. By doing so, they will not only meet the demands of today’s customers but also position their banks to thrive in the rapidly evolving banking industry.

If you’re ready to explore how GenAI solutions can transform your bank, explore how JurisTech’s Composite AI platform can transform your operations and set your bank ahead of the competition. Now is the time to lead, not follow.

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Should Banks Watch Out For The Viral “Hype” In Hyperautomation?

JurisTech — Tue, 13 Aug 2024 08:00:08 +0000

This article “Should Banks Watch Out For The Viral “Hype” In Hyperautomation?” is authored in conjunction with iMoney. For further information regarding iMoney’s services, please contact them via their website or drop an email at marketing@imoney.my.

Automation has been a key foundation technology for the banking and finance industry, touting its ability to reduce human errors, cut down overall costs, drive profitability, and ensure your business remains future-proof.

But when you’ve finally managed to wrap your head around automation, now comes hyperautomation being hyped as a key strategic technology and is projected to become a $135 billion industry by 2032.

Those are big claims for an emerging technology and you might wonder if there’s any substance to all the viral hype that hyperautomation has been getting.

In layman terms, it’s basically taking automation to the next level. This is now possible with the big leaps in generative AI and machine learning in the past four years.

It is this combination of generative AI and machine learning together with the earlier robotic process automation that has coined this new hype of hyperautomation.

So, what is hyperautomation? What are the risks and challenges? And most importantly, how does it apply to the banking and financial industry?

In this article, we’re going to take a deeper dive into hyperautomation, and some of its real-world implications, and hopefully give you a better understanding of this emerging technology and why you should watch out for it.

Understanding Hyperautomation and its Challenges

In the words of Gartner, hyperautomation is a — business-driven, disciplined approach that organisations use to rapidly identify, vet and automate as many business and IT processes as possible.

Basically, it’s a way to integrate continuous automation with intelligence into various aspects of your processes, allowing you to take on more complicated tasks without the need for human intervention.

This might sound very sci-fi but if you’re familiar with automation, odds are you’ve come across the technologies that make up hyperautomation.

For example, a robot vacuum that can manoeuvre around the corners of your apartment while cleaning when you are asleep is automation at work. Same with a virtual assistant like Siri or Alexa sending a birthday text greeting to your brother if you have set it up to do so.

Now imagine if Siri can instruct your robot vacuum to clean when the floor gets dirty, remind you to stock up on eggs when your supply is running low or alert you if your gas stove was not turned off.

For the automation available in the individual devices to be integrated and handle many different complex tasks without needing human intervention requires a combination of different technologies which are now available.

As for the technology that’s utilised, let’s take a quick look at each of them:

Robotic Process Automation (RPA): Robotic Process Software is software that is often used to automate and deal with high volume, low complexity tasks and scenarios.
Artificial Intelligence (AI): Artificial Intelligence is typically paired with RPA to create machines that can take on more complicated tasks by emulating human logical thinking.
Machine Learning (ML): Machine Learning is the technology that teaches machines to learn and perform complex tasks without additional human programming.
Natural Language Processing (NLP): Modern Natural Language Processing enables machines and computers to understand human language by including semantics and context.
Intelligent Document Processing (IDP): Intelligent Document Processing is the combination of AI and ML technology to translate pictures of typed, handwritten, or printed text into machine-encoded text.

While other advanced technologies can be put under hyperautomation, such as Big Data and Chatbots, the main pillars that most organisations will need to keep in mind are the ones listed above.

Is Hyperautomation just Automation with “Fancier” AI?

In some ways, hyperautomation can be just boiled down to automation with better AI. However, there are still key distinctions between automation and hyperautomation in terms of its scope and capabilities.

To use a simple analogy, automation is akin to a solo musician performing a song, while hyperautomation is a full band or orchestra performing. Both let you hear the music but one offers a fuller and wider experience.

Essentially, you’re taking the simple, single-task, and focused software of automation and further streamlining it through the complex interplay between automation technologies to automate complex processes from end to end.

The Challenges of Hyperautomation that Many Still Face

While hyperautomation provides plenty of opportunity for organisations to grow and become lean profit-driven machines, there is still potential for challenges and roadblocks.

Some of those challenges include:

Resistance to change: Adapting to the culture of interacting with multiple technologies on hyperautomation platforms can be difficult for employees, which can lead to a slower adoption process.
Complex integration issues: Integration and implementation of advanced technologies within hyperautomation can be a complex and time-consuming process as it requires a deep understanding of how they work with existing systems.
High costs and expenses: While hyperautomation can reduce your costs in the long run, the initial investment might still be costly due to additional expenses such as maintenance and training for employees.

How Hyperautomation is Utilised in the Real World

We’ve laid out the parts that make up hyperautomation and how it differentiates itself from automation. And while there are challenges to implementing it, the potential far outweighs it.

But you’re probably still wondering. “How has hyperautomation been used in the real world and how did it perform?”

Industries that require the processing of complex multiple data points and decisioning on the repetitive and mega-scale would be the obvious early adopters of hyperautomation. They include healthcare, insurance, manufacturing as well as finance and banking.

In terms of banking and financing, the major usages of hyperautomation and its potential can be focused towards three main avenues: customer onboarding, loan application/origination, and debt collection.

Digital Onboarding Systems that Make the Process Easier

Based on the Use-Case Comparison: hyperautomation for Banking by Gartner, digital onboarding sits between Calculated Risks (high business value but low feasibility) and Likely Wins (great combination of high business value).

Source: Gartner

If banks can utilise hyperautomation effectively as part of their digital onboarding systems, there is a possibility for high-performing results that otherwise might be harder to achieve.

Such was the case for Axis Bank which was missing a key ingredient to push improvement towards their customer experience. Through the smart utilisation of hyperautomation, Axis Bank was able to reduce the average service turnaround time byto 90%.

This is on top of other key results that they’ve managed to achieve, such as a 90% reduction in rework during the sales process and a 70% improvement in sales turnaround time, when they’ve implemented hyperautomation into their digital onboarding process.

Reducing the Costs and Driving Improvements in Loan Processing

The amount of work needed in the loan origination/application process is a time-consuming and labour-intensive process due to the manual handling of loan documents such as promissory notes.

Through hyperautomation, a time-consuming process that would take days can be completed in a matter of minutes.

Kohler SDMO faced a similar challenge with its labour-intensive tasks which were prone to errors and required multiple workarounds. By implementing OCR engine and bot to fully automate their invoice processing systems, they were able to achieve 36x faster invoice verification time and £1.2 million savings within the first year.

Optimising Debt Collection by Integrating Hyperautomation

When it comes to debt collection, the major focus should be to implement automation on manual tasks such as (changes in financial indicators, delays in payments, etc.) and provide intelligence-powered portals that make the process as seamless as possible for debtors.

However, the amount of paperwork and manual labour involved makes debt collection susceptible to errors and longer processing times that will impact the customer experience.

A telecom operator company in the US realised that their current manual process cost them market share trends and the ability to reach out to new customers.

By creating a unified, event-driven, data-driven and redesigned portal while automating several back-office processes, they were able to reduce customer acquisition costs by 45% and increase 36% in revenue year-over-year.

It’s More than Just Hype, it’s an Essential Part of Your Organisation

Automation is a must at this point for banks and financial institutions and hyperautomation is the next logical step towards digital transformation. From improving customer experience to driving up performance, it’s more than evident that hyperautomation is more than just viral or “hype” technology.

If your organisation is slow to integrate it and make it part of your key strategic technology, you’re missing out on the tangible benefits it brings.

JurisTech, Your Preferred Partner

At JurisTech, we lead the charge in digital transformation, providing exceptional support tailored to your banking needs. Recognised as a top partner in the Asia Pacific, our solutions, including our digital onboarding platform, loan origination system, and debt collection system, have earned Visa accreditation. This prestigious endorsement reaffirms our commitment to delivering top-tier, competitive solutions in the digital lending and recovery landscape.

With Visa certification, JurisTech ensures a secure, rapid connection to technology providers globally. Our award-winning expertise in digital customer onboarding and seamless system integration enhances your banking experience, driving performance and efficiency.

Our expert team, blending technical prowess with business consultancy, is dedicated to guiding you through every step of your digital transformation. We offer comprehensive training and support before, during, and after implementation, ensuring your system meets all business requirements while making the transition smooth and enjoyable.

Experience the future of banking with JurisTech. Contact us today for a free demo and discover how our innovative solutions can transform your financial services.

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Amazing Value With Intelligent Document Processing In Digital Onboarding

JurisTech — Tue, 30 Jul 2024 09:23:12 +0000

Welcome to the future of digital onboarding! In today’s fast-paced world, traditional methods of onboarding are becoming obsolete. Enter Generative AI and Intelligent Document Processing (IDP) – the dynamic duo revolutionising the way businesses handle onboarding. Whether you’re a financial institution or a startup, embracing this tech is no longer optional; it’s essential.

The Importance of Digital Onboarding

Digital onboarding is a game-changer. It speeds up the process, reduces errors, and enhances the customer experience. No more waiting in long queues or filling out endless paperwork. Everything is streamlined and efficient.

However, digital onboarding isn’t without its hurdles. Security concerns, data privacy issues, and the need for seamless integration can pose significant challenges. But with the right strategy and tools, these can be effectively managed.

What is Gen AI?

Gen AI, or Generative Artificial Intelligence, refers to AI systems capable of creating content, making decisions, and learning from data. It’s a step beyond traditional AI, offering more advanced capabilities.

Over the years, AI has evolved from simple rule-based systems to complex neural networks capable of mimicking human intelligence. Gen AI represents the pinnacle of this evolution, bringing unprecedented possibilities to the table.

Read here to find out how Generative AI is Emerging in the Banking and Financial Industries.

What is Intelligent Document Processing (IDP)?

IDP is the technology that enables automated extraction, processing, and management of data from documents. It’s the backbone of digital onboarding, ensuring that information is accurately captured and processed.

In a world where data is king, IDP plays a crucial role. It not only speeds up the onboarding process but also ensures accuracy and compliance with regulatory requirements.

Benefits of Intelligent Document Processing in Digital Onboarding

Enhanced Efficiency and Speed

IDP, powered by technologies such as Optical Character Recognition (OCR) and machine learning, automates data extraction, significantly reducing the need for manual data entry. This automation accelerates processing times and minimises errors, allowing banks to streamline workflows and eliminate bottlenecks. In an industry where speed can make or break customer relationships, quick and efficient onboarding provides a distinct competitive edge, helping banks attract and retain more customers. According to Saxon AI, IDP can swiftly transform bank forms into digital documents, making processes more efficient and less prone to human error.

Improved Accuracy and Compliance

Automation through IDP reduces human error, ensuring that customer information is accurate and reliable. This accuracy is crucial for banks to make informed decisions and maintain regulatory compliance. Automated compliance checks help mitigate risks and enhance the bank’s reputation by ensuring adherence to regulatory requirements. This not only protects the bank from potential fines and legal issues but also builds trust with customers and stakeholders.

Cost Savings

Implementing IDP results in significant operational cost savings. By decreasing the reliance on manual labour, banks can reallocate resources to more strategic initiatives, such as customer service enhancements or new product development. The scalability of these automated solutions allows banks to handle increased document volumes during peak onboarding periods without compromising efficiency. This scalability ensures that banks can maintain a high level of service, even during times of increased demand, thereby optimising resource utilisation and reducing operational costs.

Enhanced Customer Experience

A faster onboarding process directly translates to improved customer satisfaction. According to Brickendon, customers value quick and efficient service, which can lead to higher retention rates. Generative AI enables personalised interactions by analysing data to tailor services to individual customer needs. Additionally, AI-driven chatbots enhance the customer experience by providing instant support and resolving queries promptly. These personalised and responsive interactions help build strong customer relationships, fostering loyalty and long-term engagement.

Advanced Document Management

Efficient document retrieval and management are essential for smooth banking operations. IDP ensures that documents are well-organised and easily accessible, saving time and effort. Furthermore, the management of contracts and financial agreements becomes more efficient with automated analysis and processing. This reduces the risk of errors, ensures compliance with all terms and conditions, and allows for quicker decision-making processes.

Read here to discover the Exciting Opportunities with Generative AI in Digital Onboarding.

In A Nutshell

The integration of Intelligent Document Processing and generative AI in digital onboarding platforms offers numerous business benefits for banks and financial institutions. From enhanced efficiency and accuracy to significant cost savings and improved customer experience, these technologies are revolutionising the banking sector. As the future unfolds, the adoption of IDP and generative AI will become increasingly vital for banks to stay competitive and compliant.

For financial institutions aiming to lead in this evolving landscape, now is the time to embrace these innovations. By leveraging the power of IDP and generative AI, banks can transform their digital onboarding processes, ensuring a seamless, efficient, and compliant customer experience. The future of banking is here, marked by intelligence, automation, and a customer-centric approach.

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Exciting Opportunities With Generative AI in Digital Onboarding

JurisTech — Tue, 23 Jul 2024 03:56:12 +0000

In today’s fast-paced digital era, the banking sector is experiencing a profound transformation, driven by the integration of generative AI (Gen AI) in digital onboarding processes. This cutting-edge technology isn’t just streamlining operations—it’s revolutionising how banks engage with customers.

Gen AI is a subset of artificial intelligence that employs algorithms capable of creating new content, ranging from text and images to even financial models, by learning from existing data. This cutting-edge technology is set to revolutionise how financial institutions function, offering unparalleled opportunities for increased efficiency, accuracy, and innovation.

Read here to find out more on how Generative AI is Emerging in the Banking and Financial Services Industry.

Gen AI’s Impact on Business Operations in the Banking and Financial Sectors

According to NVIDIA, banks and financial institutions are increasingly adopting AI to enhance their operations. The most prevalent AI workload is data analytics, utilised by 69% of companies. This underscores the industry’s focus on extracting actionable insights from vast datasets to drive decision-making and strategic initiatives. Following closely is data processing at 57%, highlighting the need for efficient management and manipulation of large volumes of data.

Natural language processing (NLP), used by 47% of companies, showcases the sector’s emphasis on improving customer interactions and automating communication. Large language models (LLMs) are also gaining traction, with 46% of firms leveraging these advanced models to understand and generate human-like text, thereby enhancing customer service and support. Finally, 43% of companies are utilising Gen AI, reflecting the growing interest in AI’s creative and adaptive capabilities to develop innovative solutions and streamline operations.

The AI Workloads Financial Services Companies are Utilising. Source: State of AI in Financial Services :2024 Trends, NVIDIA

The integration of Gen AI into business operations has proven to be transformative, as evidenced by the same NVIDIA report. A significant 43% of companies report that Gen AI has created operational efficiencies, streamlining processes and reducing manual workloads. This efficiency translates into a competitive advantage for 42% of firms, enabling them to stay ahead in a rapidly evolving market.

Improving customer experience is another critical benefit, with 27% of companies noting enhancements in this area. Gen AI’s ability to personalise interactions and provide real-time support plays a vital role here. Additionally, 27% of firms have seen more accurate models, driven by AI’s advanced data analysis and predictive capabilities.

Gen AI also opens new business opportunities, with 23% of companies exploring innovative solutions and services made possible by AI. Finally, 14% of firms have reduced their total cost of ownership, thanks to the efficiencies and automation brought by Gen AI. This cost reduction allows for reinvestment in other strategic areas, further propelling business growth and innovation.

The Benefits Gen AI Brings to Companies in Financial Services. Source: State of AI in Financial Services: 2024 Trends, NVIDIA

Key Applications of Generative AI in Digital Onboarding

Document Verification and KYC

One of the most critical aspects of digital customer onboarding is document verification and Know Your Customer (KYC) processes. Generative AI in digital onboarding simplifies this by automatically extracting data from customer documents. No more tedious data entry or manual verification – AI handles it all. Additionally, AI-powered biometric verification enhances security, ensuring that the person opening the account is who they claim to be. This leads to improved accuracy and a significant reduction in manual effort, allowing banks to onboard customers swiftly and securely.

Read here to find out more on how to Elevate your Digital Onboarding Process with eKYC.

Streamlined Credit Underwriting

Credit underwriting can be a cumbersome process, but Gen AI is changing the game. By quickly verifying creditworthiness data, AI accelerates the approval process for business loans. This means faster onboarding for loan products, giving customers quicker access to the funds they need. Banks benefit from reduced processing times and increased customer satisfaction.

Personalised Onboarding Experiences

Today’s customers expect personalised experiences, and Gen AI delivers just that. AI tailors the onboarding journey based on individual preferences, suggesting relevant products and services. It customises the user interface, making it intuitive and user-friendly. More impressively, AI can proactively anticipate customer needs, offering solutions before customers even realise they need them. This level of personalisation sets banks apart in a competitive market.

Read here to find out more about the Monumental Price Banks Pay for Overlooking Hyper-Personalisation.

24/7 Virtual Assistance

Gone are the days of waiting in queues or navigating complex phone menus. AI-powered chatbots and virtual assistants provide around-the-clock support, handle questions and transactions, and offer guidance whenever customers need it. This 24/7 availability enhances customer satisfaction and ensures that help is always at hand, without the need for human intervention.

Challenges and Considerations While Implementing Generative AI in Digital Onboarding Platforms

Data Privacy and Security

With the integration of Gen AI, ensuring the protection of sensitive customer information is paramount. Robust security measures must be in place when processing data to prevent breaches and maintain trust. Banks must stay vigilant and continually update their security protocols to safeguard customer data.

Algorithmic Bias

AI algorithms, while powerful, can sometimes exhibit biases. It’s crucial to mitigate potential biases to ensure fair and unbiased decision-making. Continuous monitoring and refining of AI models are necessary to achieve equitable outcomes for all customers.

Human Expertise and Service

Despite the benefits of AI-driven personalisation, the human touch remains irreplaceable. Balancing automation with personal interaction is key to maintaining customer satisfaction. Banks must find the right mix of AI and human expertise to offer a seamless yet personable experience.

In a Nutshell

The adoption of Gen AI in digital onboarding is on the rise, with banks already reaping significant benefits. Faster processes, enhanced security, and personalised experiences give banks a competitive edge. As AI technology continues to evolve, its transformative impact on digital onboarding will only grow, paving the way for a new era of banking that is efficient, secure, and customer-centric. Gen AI is not just an upgrade; it’s a revolution in how banks welcome and serve their customers.

JurisTech, Your Preferred Partner

At JurisTech, we are dedicated to providing unparalleled support for your digital transformation projects. As a leading partner in the Asia Pacific region, we are proud to announce that Visa has accredited five of our software solutions, including our state-of-the-art digital onboarding platform. This accreditation underscores our commitment to delivering the most effective and competitive solutions in the digital lending and recovery industry.

The Visa certification highlights JurisTech’s dedication to offering customers a swift and secure connection to technology providers worldwide. Enhance your banking experience with JurisTech’s exceptional solutions, which guarantee award-winning expertise in digital customer onboarding and seamless integration.

Don’t just take our word for it, read here to explore The Proven Results of JurisTech’s Digital Onboarding Platform!

Our team is here to help you navigate your system, ensuring it meets all your business requirements. Our members bring extensive industry experience and excel as both technical experts and business consultants. We provide comprehensive training before, during, and after project implementation to ensure all your support needs are met. We prioritise mutual understanding of performance expectations and strive to make the digital transformation process enjoyable for our clients.

Contact us today for a free demo to learn how JurisTech’s digital onboarding platform can enhance customer satisfaction and optimise your onboarding process.

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Everything You Need To Know About Composite AI In The Financial Industry

JurisTech — Fri, 05 Jul 2024 06:33:07 +0000

Introduction

In an era where artificial intelligence (AI) is transforming industries globally, the financial sector is at the forefront of leveraging these advancements. Traditional AI models have laid the groundwork, but the emergence of Composite AI is set to revolutionise the landscape. According to Yahoo! Finance, the global Composite AI market was valued at US$ 900 million in 2023 and projected to reach US$ 5.8 billion by 2030, growing at a CAGR of 36.7%. This article delves into its concept, its rise in the financial industry, and how it stands as a game-changer compared to traditional AI models. We will explore its underlying benefits, untapped areas, and how JurisTech is at the helm of this transformation with its innovative Composite AI solutions. Additionally, we will discuss the integration process and look ahead to the future prospects of Composite AI in finance.

What is Composite AI?

As defined by Gartner, Composite AI refers to the combined application (or fusion) of different AI techniques to improve the efficiency of learning to broaden the level of knowledge representations. It holds the ability to create AI applications by combining and orchestrating multiple modular AI components, such as machine learning models, natural language processing (NLP) agents, robotic process automation (RPA), and more. This modular approach allows for greater flexibility, scalability, and customisation compared to traditional monolithic AI systems.

Defining the Concept

Composite AI builds on the principles of modularity, where each AI component or service is designed to perform a specific function. These components can be independently developed, deployed, and updated without affecting the overall system. This allows organisations to mix and match various AI modules to create bespoke solutions tailored to their unique requirements.

Historical Context and Evolution

The concept of Composite AI has evolved from the broader trend of modular software development and microservices architecture. As AI technology advanced, the need for more flexible and adaptable AI systems became evident. Traditional AI models, while powerful, were often rigid and difficult to modify once deployed. It emerged as a solution to these limitations, offering a more dynamic and customisable approach to AI deployment.

The Rise of Composite AI in the Financial Industry

The financial industry has always been data-intensive, making it a prime candidate for AI-driven transformation. Composite AI has gained traction due to its ability to address complex, multifaceted problems by integrating various AI components seamlessly. Financial institutions are increasingly adopting Composite AI to enhance operational efficiency, improve customer experiences, and gain competitive advantages.

Driving Factors

Several factors have contributed to the rise of Composite AI in finance:

Data Explosion: The volume of financial data has grown exponentially, necessitating advanced AI tools to manage and analyse this information effectively.
Regulatory Pressure: Financial institutions face stringent regulatory requirements that demand accurate and timely reporting. Composite AI helps automate compliance processes and ensure adherence to regulations.
Customer Expectations: Modern customers expect personalised, seamless experiences. Composite AI enables financial institutions to meet these expectations by leveraging customer data to tailor services and products.

Composite AI vs. Traditional AI Models

Traditional AI models are often rigid and monolithic, making them difficult to adapt to changing business needs. Composite AI, on the other hand, offers a flexible architecture where AI components can be mixed and matched to create tailored solutions. This adaptability is crucial in the fast-paced financial sector, where agility and responsiveness are key.

Key Differences

Flexibility: Composite AI allows for the integration of diverse AI components, enabling financial institutions to build solutions that precisely meet their needs.
Scalability: Components can be added or removed without disrupting the entire system, allowing for easy scaling.
Customisation: Financial institutions can tailor AI solutions to specific use cases, improving efficiency and effectiveness.
Modularity: Each component operates independently, making it easier to update or replace parts of the system without impacting overall functionality.

Comparative Advantages

Composite AI offers several advantages over traditional AI models:

Rapid Innovation: Financial institutions can quickly experiment with new AI technologies and integrate them into existing systems.
Cost Efficiency: By reusing and repurposing AI components, organisations can reduce development costs and time to market.
Resilience: Modularity enhances system resilience, as failures in one component do not necessarily compromise the entire system.

Benefits of Composite AI in Finance

Composite AI brings numerous benefits to the financial industry, making it a growing and preferred choice for many institutions.

Operational Efficiency

By automating repetitive tasks and optimising workflows, Composite AI reduces operational costs and increases productivity. Financial institutions can handle higher volumes of transactions and processes without a proportional increase in operational resources. This efficiency is crucial in maintaining profitability in a highly competitive industry.

Building Intelligent Applications

A report by Gartner mentions how organisations are overlooking massive opportunities to leverage Composite AI to address important business problems. Investing in new data science processes by applying Composite AI would mean building intelligent applications that would not only produce better outcomes in terms of business decisions, but outpace the market to impact products and services.

Enhanced Decision-Making

Advanced data analysis and predictive modelling capabilities enable more informed and timely decision-making. Composite AI can analyse vast amounts of data from various sources, providing financial professionals with deeper insights and more accurate forecasts. This leads to better risk management, investment strategies, and customer service.

Personalisation and Customer Experience

AI-driven insights help financial institutions offer personalised services and products, enhancing customer satisfaction and loyalty. By understanding individual customer preferences and behaviours, financial institutions can create tailored experiences that meet specific needs and expectations. Personalised financial advice, customised investment portfolios, and proactive customer support are just a few examples of how Composite AI enhances customer experience.

Untapped Areas in Composite AI

Despite its growing adoption, several areas within Composite AI remain underexplored, presenting significant opportunities for innovation.

New Frontiers and Opportunities

Areas such as decentralised finance (DeFi), real-time risk assessment, and AI-driven compliance solutions are ripe for exploration. These areas offer the potential to fundamentally change how financial services are delivered and managed. For instance, Composite AI can enhance DeFi platforms by providing real-time analytics and decision-making tools, making these platforms more robust and user-friendly.

Research and Development Focus

Continued investment in R&D will drive advancements in Composite AI, opening new possibilities for its application in finance. Innovations in AI algorithms, data processing techniques, and integration methods will enhance the capabilities of Composite AI systems. Collaborative efforts between academia, industry, and technology providers will be essential in pushing the boundaries of what Composite AI can achieve.

How JurisTech Utilises Composite AI

At JurisTech, we are pioneering the use of Composite AI by combining multiple AI agents to create powerful, integrated solutions. Our approach includes:

Document OCR

Automating data extraction from documents to streamline processes. This technology significantly reduces the time and effort required for manual data entry, improving accuracy and efficiency. Document OCR is particularly useful in areas such as loan processing, where large volumes of documents need to be reviewed and processed quickly.

Decisioning Engine

Leveraging AI to support decision-making in areas such as credit scoring and risk assessment. Our decisioning engine integrates multiple data sources and uses advanced algorithms to provide real-time, data-driven insights. This enhances the accuracy and objectivity of decisions, leading to better outcomes for both the institution and its customers.

Data Mining

Extracting valuable insights from large datasets to inform strategy and operations. Our data mining solutions help financial institutions identify trends, detect anomalies, and make better strategic decisions. By uncovering hidden patterns in data, organisations can gain a deeper understanding of market dynamics and customer behaviour.

Large Language Model (LLM)Agents

Utilising large language models to enhance customer interactions and support. Our LLM agents can understand and respond to customer inquiries, provide financial advice, and generate reports, improving customer service efficiency. These agents can handle complex queries and provide personalised responses, enhancing the overall customer experience.

Robotic Process Automation (RPA)

Automating routine tasks to improve efficiency and reduce human error. Our RPA solutions handle repetitive processes, allowing human employees to focus on more strategic activities. This not only increases productivity but also enhances job satisfaction by eliminating mundane tasks.

Natural Language Query (NLQ) Reporting

Natural Language Query reporting provides intuitive, AI-driven business intelligence. This component enables users to interact with data using natural language, making data insights accessible to all levels of the organisation. By democratising access to data, NLQ reporting empowers employees to make informed decisions based on real-time information.

Challenges and Solutions in Implementing Composite AI

Implementing Composite AI comes with its own set of challenges. However, these can be effectively managed with strategic planning and the right technology partners.

Integration with Legacy Systems

One of the significant challenges is integrating Composite AI components with existing legacy systems. Financial institutions often rely on outdated infrastructure that can be incompatible with modern AI technologies. The building blocks of a good composable architecture should be one that represents a departure from monolithic architectures, adopting a modular design philosophy. To address this, JurisTech offers seamless integration solutions such as low code/ no code APIs that ensure smooth interoperability between old and new systems.

Data Privacy and Security

With the increasing reliance on AI, ensuring data privacy and security is paramount. Composite AI systems must comply with stringent regulatory requirements and safeguard sensitive information. JurisTech incorporates robust encryption, secure data storage, and compliance management tools to protect data integrity and confidentiality.

Skillset Requirements

The successful implementation of Composite AI requires a workforce skilled in AI, data science, and related fields. Financial institutions must invest in training and development to build a capable team. JurisTech has deep knowledge of banking and AI with subject-matter-experts that provide comprehensive training programs and support to help clients develop the necessary skills and expertise.

Future Prospects of Composite AI in Finance

The future of Composite AI in the financial industry looks promising, with several emerging trends set to shape its trajectory.

Emerging Trends

AI and Blockchain Integration: Combining the strengths of AI and blockchain technology can enhance transparency, security, and efficiency in financial transactions.
Ethical AI: Developing AI systems that prioritise ethical considerations, such as fairness, accountability, and transparency, will be crucial in gaining customer trust.
AI-as-a-Service (AIaaS): Offering AI capabilities as a service will make advanced AI tools more accessible to smaller financial institutions, driving broader adoption.

Potential Innovations

AI-Driven Financial Advisory: AI can provide personalised financial advice, helping customers make better investment decisions and achieve their financial goals.
Real-Time Fraud Detection: Advanced AI algorithms can continuously monitor transactions to detect and prevent fraudulent activities in real-time.
Hyper-Personalised Banking Experiences: AI can analyse customer data to offer highly personalised banking products and services, enhancing customer loyalty and satisfaction.

Industry Impact Predictions

Improved Regulatory Compliance: AI-driven compliance solutions will help financial institutions navigate complex regulatory environments more efficiently.
Enhanced Customer Trust: By leveraging AI to provide transparent and fair services, financial institutions can build stronger relationships with their customers.
Proactive Financial Management: AI will enable financial institutions to anticipate market trends and customer needs, allowing for more proactive and strategic management.

Integration Strategies for Composite AI

Successful implementation of Composite AI requires a well-thought-out integration strategy. Here are some key considerations:

Assessing Current Infrastructure

Before implementing Composite AI, financial institutions need to assess their current infrastructure to identify potential compatibility issues. This involves evaluating existing systems, data sources, and workflows to determine the best approach for integration.

Choosing the Right Components

Selecting the appropriate AI components is crucial for the success of a Composite AI strategy. Financial institutions should prioritise components that align with their business objectives and address specific pain points. JurisTech offers a range of AI modules that can be customised to meet diverse needs.

Ensuring Data Quality and Governance

High-quality data is essential for effective AI applications. Financial institutions must implement robust data governance practices to ensure data accuracy, consistency, and security. This includes establishing data standards, performing regular audits, and implementing data validation processes.

Training and Change Management

Adopting Composite AI requires a shift in organisational culture and mindset. Financial institutions should invest in training programs to equip employees with the necessary skills and knowledge. Additionally, change management initiatives can help ensure a smooth transition and foster acceptance of new technologies.

Ethical Considerations in Composite AI

As financial institutions increasingly rely on AI, ethical considerations become paramount. Here are some key ethical issues to address:

Bias and Fairness

AI systems can inadvertently perpetuate biases present in the training data. Financial institutions must implement measures to identify and mitigate biases in AI models to ensure fair and equitable outcomes. This includes using diverse datasets, conducting regular bias audits, and developing transparent algorithms.

Transparency and Accountability

Ensuring transparency in AI decision-making processes is crucial for building trust with customers and regulators. Financial institutions should provide clear explanations of how AI models work and the factors influencing their decisions. Additionally, establishing accountability frameworks can help address potential issues and ensure responsible AI use.

Data Privacy

Protecting customer data is a top priority for financial institutions. Composite AI systems must comply with data privacy regulations and implement robust security measures to safeguard sensitive information. This includes encryption, access controls, and regular security assessments.

Conclusion

Composite AI represents a transformative advancement in the financial industry, offering unparalleled flexibility, scalability, and customisation. As financial institutions continue to navigate an increasingly complex landscape, the adoption of Composite AI will be crucial in maintaining competitiveness and driving innovation. JurisTech is committed to leading this charge, providing cutting-edge Composite AI solutions that empower financial institutions to achieve their goals. The future of AI and finance is Composite, and the journey has just begun.

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Why the Financial Industry Uses Low-code/No-code APIs Now

JurisTech — Fri, 21 Jun 2024 08:54:49 +0000

Application programming interfaces (API) have emerged as a pivotal force in an era where digital transformation is no longer a luxury but a necessity. Thanks to APIs, banks and financial institutions (FIs) can now easily integrate new applications with their existing software systems, expanding their digital banking capabilities, and empowering consumers with a more diverse range of financial tools and services. According to Gartner, a recent report states that 70% of Gen Z and millennial consumers switch banks based on digital capabilities. In other words, banks and FIs need to transform digitally to remain competitive as technological advancements and changing consumer expectations are reshaping the financial industry. Moreover, terms like open banking, financial inclusion, microservice architecture, composable banking, and embedded finance have become buzzwords, reflecting the industry’s shift towards more agile, customer-centric approaches. Low-code/no-code APIs, in particular, have played a crucial role in turning these widely discussed concepts into reality. This technology has revolutionised how banks and FIs operate and deliver services, enabling them to adapt quickly to market demands and enhance customer experiences.

The Rise of APIs in the Financial Industry

According to Allied Market Research, the global API banking market is expected to grow at a compound annual growth rate (CAGR) of 24.7% from 2023 to 2032, with an anticipated value of $217.3 billion by 2032. This growth highlights the increasing trend of banks opening up their APIs, facilitating open banking, financial inclusion, and embedded finance. This is because APIs enable different software systems to communicate and interact with each other, allowing banks and FIs to share data securely and efficiently. By opening up their APIs, banks and FIs can create more financial products and services that benefit both themselves and their customers.

As open banking, embedded finance, and composable banking gain more attention, it is crucial to focus on the “bridge” that turns these concepts into reality—APIs. Given that APIs are the driving force behind these innovations, it raises important questions: “Can low-code/no-code APIs replace traditional APIs?” and “Why should banks and FIs consider this replacement?”

Why Low-code/No-code APIs are Replacing Traditional APIs

With the global API banking market on the rise, banks and FIs might be facing various challenges in using traditional APIs. Specifically, it would result in lengthy development cycles and higher costs due to specialised skills and extensive manual coding. Other than that, it would also lead to maintenance difficulties in keeping APIs updated with changing regulations and market demands, and integration issues with legacy systems. These challenges hinder the agility and efficiency required for financial institutions to innovate and meet the dynamic needs of their customers in today’s digital landscape.

1) Lengthy Development Cycles

Traditional API development often involves extensive coding, rigorous testing, and multiple iterations, which can be time-consuming. Developers must write and debug thousands of lines of code, and each phase from design to deployment requires meticulous work to ensure functionality and security. This lengthy process delays the launch of new services, hindering the ability of financial institutions to respond swiftly to market demands.

2) Higher Costs

Developing traditional APIs is expensive due to the need for highly skilled developers proficient in multiple programming languages and system architectures. These specialised skills come at a premium, significantly inflating development costs. Additionally, extensive manual coding increases the likelihood of errors, leading to further expenditures on debugging and quality assurance. Continuous maintenance and updates to accommodate new regulations and market needs also add to the financial burden.

3) Maintenance Challenges

Maintaining traditional APIs is an ongoing challenge, particularly with evolving regulatory requirements and market demands. Financial regulations frequently change, necessitating continuous updates to ensure compliance. Recent research has found that most banks in the US and UK acknowledge that their financial control processes lack robustness and flexibility to adapt to increased regulatory changes or scrutiny. Traditional APIs, with their complex codebases, require significant effort to modify and test for each regulatory change. This constant need for maintenance can detract from the bank’s and financial institution’s ability to innovate and improve other areas of the business.

4) Integration with Legacy Systems

Integrating traditional APIs with legacy systems poses significant challenges. Legacy systems often run on outdated technologies that aren’t easily compatible with modern APIs, requiring extensive custom coding and resources. This process is plagued by compatibility issues, increased complexity, and a higher likelihood of errors. Additionally, legacy systems create data silos, isolating data within specific departments, which hampers seamless data flow and real-time decision-making. These complexities highlight the need for more flexible and accessible solutions, driven by low-code/no-code APIs.

Why JurisTech Uses Low-code/No-code APIs and How It Benefits Your Business

1) Accelerated Time-to-Market

Low-code/no-code APIs significantly shorten development cycles by leveraging visual interfaces and configuration rather than extensive manual coding. This enables rapid prototyping and development, allowing financial institutions to quickly iterate on new features and services. The reduced dependency on developers means that business analysts and citizen developers can contribute to the API development process, speeding up time-to-market. This agility is crucial in the financial industry, where the ability to swiftly deploy new solutions can provide a competitive edge.

2) Seamless Integration

Low-code/no-code APIs facilitate seamless integration with existing systems, third-party applications, and legacy systems. By avoiding hard-coded integrations, these APIs enhance interoperability, allowing different software components to communicate effortlessly. This ease of integration helps financial institutions streamline operations, improve data flow, and reduce the complexity and cost associated with maintaining custom code for each integration point.

3) Enhanced Scalability and Flexibility

Low-code/no-code APIs support dynamic scaling, enabling applications to automatically adjust to varying loads, which enhances performance and reliability. This scalability ensures that banks and FIs can handle increased user activity without compromising service quality. Additionally, the flexibility of low-code/no-code APIs allows banks and FIs to quickly adapt to market changes and new business requirements, fostering innovation and responsiveness in a fast-paced financial industry.

4) Cost Efficiency

The use of low-code/no-code APIs reduces development costs by minimising the need for extensive manual coding and specialised development skills. This approach lowers the overall cost of API development and maintenance, making it more accessible for banks and FIs of all sizes. Simplified maintenance and updates further reduce costs, as changes can be implemented with fewer resources and less time, allowing banks and FIs to allocate funds to other strategic initiatives.

5) Improved Customer Experience

Low-code/no-code APIs enable banks and FIs to respond more quickly to market needs, facilitating the rapid deployment of new features and services that enhance customer satisfaction and competitiveness. The ability to integrate artificial intelligence (AI) and other advanced technologies via low-code/no-code AP also supports hyper-personalisation, creating tailored services and experiences for customers. This focus on personalised customer interactions can drive loyalty and differentiation in the financial industry.

How Low-code/No-code APIs are Related to the Emerging Financial Industry Trends

Emerging technologies, such as AI and machine learning, are set to further revolutionise the financial industry by seamlessly integrating with existing systems through low-code/no-code APIs. These technologies have the potential to enhance automation, streamline data analysis, and elevate customer engagement, creating new avenues for innovation and operational efficiency. For example, an increasing number of banks and financial institutions are adopting generative AI (GenAI) to enhance decision-making, improve risk management, and elevate customer service.

For instance, OCBC is among the first banks globally to deploy a GenAI chatbot to its 30,000 employees for risk management, customer service and sales. Other than that, Wells Fargo has also introduced and integrated its virtual assistant into their Wells Fargo Mobile app, providing users with a personalised, helpful and simplified banking experience. These case studies clearly show that the financial industry is embracing advanced AI technologies to maintain competitiveness. To achieve this, banks and FIs must prioritise operational speed and development agility, where low-code/no-code APIs play a pivotal role in enabling rapid innovation and responsiveness.

Conclusion

The adoption of low-code/no-code APIs is essential for banks and FIs seeking to stay competitive and meet evolving market demands. These APIs offer a more accessible, efficient, and cost-effective way to develop and deploy APIs, addressing the challenges associated with traditional methods. By leveraging low-code/no-code APIs, banks and FIs can enhance their agility, innovation, and customer experiences, positioning themselves at the forefront of the digital transformation in the financial industry. JurisTech’s composable software solutions aim to deliver transformational technology that provides superior value to our customers and stakeholders by expanding your digital capabilities. Embracing low-code/no-code APIs is not just a strategic advantage; it is a necessity for those looking to shape the future of finance.

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The Monumental Price Banks Pay For Overlooking Hyper-Personalisation

JurisTech — Wed, 12 Jun 2024 08:25:35 +0000

This article is authored in conjunction with iMoney. For further information regarding iMoney’s services, please contact them via their website or drop an email at marketing@imoney.my.

From computer software to the streaming industry, the successful implementation of hyper-personalisation has seen companies reaping big rewards such as Netflix adding 9 million subscribers in the first quarter of this year.

Customers and consumers today want that personalised “Netflix” experience as part of their daily services; exceptionally relevant product suggestions that are provided in real-time.

The same goes for financial services that we use as part of our daily life to save, invest or apply for a loan. This begs the question, “Why aren’t more key decision-makers in the banking and finance industry integrating it into their operations?”

In this article, we will explain and explore a few of the benefits and challenges that those in the banking and finance industry face when it comes to implementing hyper-personalisation solutions.

Ultimately, banks must integrate and nurture hyper-personalisation into their architecture as the cost of ignoring can be too big for banks and financial institutions (FI) to ignore.

What is Hyper-personalisation in Banking?

Hyper-personalisation in banking is the utilisation of machine learning (ML) and artificial intelligence (AI) technology to thoroughly understand and create highly customised financial services and products tailored to the needs and preferences of individual customers.

Using customer data, such as demographic information and transaction histories, banks can build detailed profiles of their customers to offer personalised and bespoke banking experiences.

While traditional AI has set the stage for personalisation in banking, Generative AI (GenAI) advances this with more dynamic and intuitive features. It understands customer needs better. It creates more engaging and personalised content. It provides scalable solutions that adapt to changing financial environments. This leads to a more personalised and satisfying banking experience for customers. Read here to find out more on how GenAI is emerging in the Banking and Financial Services industries.

Building Detailed KYC/customer Personas

Banks can further hone their customer personas and strengthen their know-your-customer (KYC) standards by using big data combined with ML and AI technology.

This provides a deeper understanding of their customers, going beyond just the standard demographic data. By pulling from external sources such as social media and utilising past interactions, banks can create customer personas that will help them deliver personalised experiences.

Netflix is a prime example of how using the vast data they have acquired through the years to build a customer persona that allows them to provide personalised recommendations and improve their user retention.

Analysing and Understanding Customer Data

To fully utilise these personas will require a strong foundation in the bank’s abilities to harness, analyse, and understand big data. With data, banks can pull key insights that help build their customer personas. These insights can be broken down into the following three:

Descriptive insights – Visualises and explains the behaviour/nuances of customers’ spending patterns, transactions, assets, etc.
Diagnostic insights – Providing the answers to the ‘hows’ and ‘whys’ of customers to better understand customer behaviours.
Predictive insights – To help banks foresee a customer’s financial health and to alert them on potential issues such as cash flow, unplanned large payments, or penalties. This allows banks to anticipate their customers’ needs and create customised solutions when needed.

By better understanding the data acquired and the type of available insights, banks and FIs can make better decisions on creating and developing experiences that will resonate with their customers.

Creating Customised/Personalised Banking Experiences

Hyper-personalisation is not just big data. It moves beyond ‘data-driven’ and focuses on better understanding customers’ emotional state and the context behind it, allowing you to respond/nudge the customer suitably.

The combination of convenience, customer engagement, and emotional engagement can lead to stronger loyalty in customers, which will eventually lead to higher conversions. Plus customers are 80% more likely to purchase from a company that offers such personalised experiences.

With the advent of GenAI, hyper-personalisation in banking has reached new heights. Recent surveys show that 91% of financial services companies are either evaluating or using AI technologies. A significant number focus on GenAI to drive innovation, improve efficiency, and enhance customer experiences. Additionally, 63% of European financial services leaders are optimistic about GenAI’s potential to transform their operations. This advanced technology enhances user engagement and greatly boosts customer retention by offering a more intuitive and personalised banking experience. Here are some compelling use cases showing the transformative impact of GenAI in banking:

Advanced Automated Customer Interaction: A GenAI-powered chatbot is capable of enhancing customer service by providing accurate, context-aware responses, leading to improved customer satisfaction and retention.
Real-Time Data Analytics: GenAI analyses real-time transaction data to predict and suggest personalised financial products to customers, such as customised loan options based on their current spending patterns and financial behaviour.
Customised Product and Service Development: GenAI helps banks develop new financial products tailored to specific customer segments, such as bespoke investment packages for young professionals or retirement plans for seniors.
Enhanced Fraud Detection and Security: GenAI monitors transactions in real-time, learning and adapting to detect and respond to fraudulent activities specific to each customer’s transaction habits, improving security measures.

How Failing to Utilise Hyper-personalisation Can Be Costly for Banks

While the opportunities for growth and improving the performance of banks and FIs can be limitless with hyper-personalisation, the cost of ignoring or failing to integrate it on time can be devasting.

Unsatisfactory customer experiences for banks have led to customer defections, with the lack of personalised experiences being a key point. The study by Standard & Poor highlights how customer loyalty will waver without these personalised experiences with responses by consumers who are willing to switch from one bank to another if they provide “better mobile app experiences” (39%) and “better customer experience” (38%).

But beyond losing customers, banks that fail to utilise hyper-personalisation will face significant business impacts. A study by Forrester highlights the major costs that banks faced such as increased costs (62%), slowed business agility (60%), poor customer experience (56%), and lost operational resilience (54%).

Ignoring or failing to use hyper-personalisation can be detrimental to your growth and profitability. At the same time, it’s also important to understand why banks and FIs are failing so that leaders can avoid making the same mistakes.

Reasons Why Most Banks Fail to Utilise Hyper-personalisation

The banking industry is particularly suited to adopt hyper-personalisation given its large customer bases and high amount of data per customer. However, banks and FIs are not fully harnessing what hyper-personalisation has to offer due to a myriad of reasons.

Legacy Technology and Systems

The World Retail Banking Report 2022 pointed out the fact that legacy technology and systems remain the biggest hurdle for banks and FIs. 95% of its surveyed executives believe their outdated legacy systems and technological capabilities make it hard to fully optimise their data for customer-centric growth strategies.

Legacy technology and systems have made it difficult for banks to access the potential goldmine of customer data, which has seen banks needing the ability to fully harness data analytics or behavioural science to increase the utility of their products.

Strict Data Regulations and Compliances

In addition to facing legacy technology issues, banks also face strict regulations in the form of stringent customer protection and data privacy and security regulations, which also act as perceived constraints towards the adoption of hyper-personalisation.

Given that hyper-personalisation relies on huge amounts of data collection, there are raising concerns among users regarding their privacy and how it is protected. Add to that privacy regulations such as the GDPR and CCPA, banks and FIs have to ensure that they are collecting and using data in a compliant manner.

Taking the Initiative to Integrate Hyper-personalisation

So, how do banks overcome these costly challenges and start taking advantage of hyper-personalisation?

It all starts with investing and setting up a comprehensive data infrastructure. With the right technology, banks can ensure that the right personalised data is captured and is shareable across all other systems.

From investing in the right platform and building up a data-centric vision to integrating hyper-personalisation, there are numerous ways that banks and FIs can approach this.

Hyper-personalisation in JurisTech’s Digital Onboarding Platform

A survey conducted by Gladly showed that 59% of customers value personalisation when it comes to their onboarding/customer service experience over speed. For banks and FIs, a hyper-personalised digital onboarding platform is a necessity to keep up with the growing needs and demands of its customers.

JurisTech’s digital onboarding platform tackles these demands through two major approaches:

Personalised Application Journeys:
1. Tailor intuitive application journeys for various financial products, such as car loans, personal loans, auto-financing, and mortgages.
2. Incorporates pre-approval steps, virtual viewing options, and personalised product recommendations to enhance customer experience.
AI-Powered Capabilities:
1. Reduces costs by improving customer conversion rates and lowering acquisition costs through enhanced sales processes.
2. Increases revenue by providing AI-driven product recommendations, cross-selling, and upselling opportunities.
3. Offers valuable insights into customer behaviour, enabling predictive analytics for churn prediction and Loan-to-Value (LTV) assessment.

Implementation of JurisTech’s digital onboarding platform has led to proven results within the financial industries with corporations and financial institutions being able to improve client experiences and simplify corporate processes, such as fast and precise customer onboarding with straight-through processing, enhanced security measures for you and your customers’ peace of mind, ease of use with low-code/no-code technology, and fast scalability through compatibility with all standard APIs.

Hyper-personalisation in JurisTech’s Debt Collection System

Basic personalisation is not enough in the modern collection space. Ensuring that banks and FIs can clear accounts in a scalable manner and not impact customer retention will require a personalised experience at every step of the journey.

As such, JurisTech’s debt collection system offers personalised collection strategies with the following capabilities:

Profiles customers into different segments based on criteria such as product type, risk value, geographic location, and days past due (DPD).
Creates personalised collection strategies for each segment to maximise effectiveness and increase collection rates.

In addition, JurisTech’s debt collection system takes full advantage of AI-driven predictive analytics and offers the following benefits:

Predicts self-curing accounts and potential non-performing loans (NPL) customers using behavioural scoring and AI analytics, improving revenue forecasting accuracy.
Maximises revenue collection through the Whiz strategy manager, which simplifies strategy management and allows experimentation with different approaches.

Attract, Keep and Expand Customer Loyalty With Hyper-personalisation

As part of the digital transformation movement, banks and FIs will need to commit to adopting and integrating hyper-personalisation in their key services, which can act as a key differentiator for success.

By utilising solutions and platforms such as JurisTech’s debt collection system and digital onboarding platform, they will take another step towards taking full advantage of what hyper-personalisation has to offer and maximising growth and profitability.

The post The Monumental Price Banks Pay For Overlooking Hyper-Personalisation appeared first on JurisTech.

Emerging Generative AI in the Banking and Financial Services Industry

JurisTech — Fri, 31 May 2024 08:48:39 +0000

Generative AI (Gen AI) is revolutionising various industries, and the banking and financial sector is no exception. As the world becomes increasingly digital, the importance of AI technologies in streamlining operations, enhancing customer service, and preventing fraud cannot be overstated.

It is a subset of artificial intelligence that employs algorithms capable of creating new content, ranging from text and images to even financial models, by learning from existing data. This cutting-edge technology is set to revolutionise how financial institutions function, offering unparalleled opportunities for increased efficiency, accuracy, and innovation.

The McKinsey Global Institute estimates that Gen AI could add between $2.6 trillion and $4.4 trillion in annual value across the 63 use cases it analysed globally. Among industry sectors, banking is expected to reap some of the largest benefits, with an annual potential of $200 billion to $340 billion, equivalent to 9 to 15% of operating profits, primarily from increased productivity. This economic impact will likely benefit all banking segments and functions, with the greatest gains in the corporate and retail sectors, estimated at $56 billion and $54 billion respectively.

Notably, while banks have initially focused on productivity in their Gen AI pilots due to broader economic pressures, the technology could significantly transform job functions and customer interactions with banks. It might even pave the way for entirely new business models.

Source: McKinsey & Company

Understanding Generative AI

Definition and Key Concepts of Generative AI

Gen AI typically involves machine learning models that can generate new data or content similar to the input they’ve been trained on. Think of it as a super-intelligent assistant capable of learning from past data and predicting future outcomes with remarkable accuracy.

How Does Generative AI Work?

At its core, Gen AI employs neural networks, especially Generative Adversarial Networks (GANs), to analyse and generate new content. These networks have two components: a generator, which creates data, and a discriminator, which assesses it. This dynamic duo learns and improves continuously over time, resulting in highly accurate outputs.

How is Generative AI Different from Traditional AI in Banking?

While traditional AI systems in banking are designed to recognise patterns and make decisions based on predefined rules and existing data, Gen AI goes a step further by using these patterns to create new, original scenarios and solutions, offering a more proactive and innovative approach. This distinction is vital for areas such as personalised banking and fraud detection, where Gen AI can anticipate and respond to customer needs or fraudulent activities in real-time

Use Cases of Generative AI in Banking

Enhancing Fraud Detection and Risk Management

Gen AI excels at analysing vast amounts of data to identify patterns and anomalies that traditional systems might overlook. This capability is crucial for robust risk management and fraud detection. By generating synthetic data that simulates various market conditions, banks can better predict and mitigate potential risks. This proactive approach aids in developing strategies to address credit, fraud, and market risks effectively.

Optimising Customer Experience with Personalised Financial Services

In today’s competitive market, customer satisfaction is paramount. Gen AI allows banks to provide hyper-personalised services by analysing customer data and interactions in real-time. This leads to tailored financial solutions, quicker response times, and an enhanced overall customer experience. For instance, Gen AI-powered virtual assistants such as chatbots can offer personalised customer service by understanding and responding to queries in natural language. These virtual assistants can handle a wide range of tasks, from answering frequently asked questions to processing transactions, thereby boosting customer satisfaction and reducing operational costs.

Furthermore, by capitalising on the wealth of data that banks have on customers, both as individuals and groups with common preferences, Gen AI can provide personalised financial advice and services. This includes tailored investment recommendations, customised loan offers, and bespoke financial planning, all of which can enhance customer engagement and loyalty.

AI-Generated Financial Reports

Financial reporting is key for transparency and decision-making. Gen AI can automate the creation of financial reports, ensuring both accuracy and consistency. By learning from past reports and data, Gen AI can generate thorough financial statements, performance analyses, and forecasts. This automation doesn’t just save time but also boosts the reliability of the financial information shared with stakeholders.

By pulling together data from various sources and using advanced natural language processing skills, Gen AI can produce detailed and well-structured reports that comply with regulatory standards and aid strategic business decisions.

Automated Trading and Investment Strategies

Gen AI can develop automated trading algorithms that execute trades based on real-time market data and predictions. These strategies help refine investment portfolios, reduce risks, and enhance returns, providing a competitive advantage in the financial sector.

Using machine learning and advanced data analysis, Gen AI identifies profitable trades and acts at optimal times. It can quickly analyse and react to market changes, enabling more efficient and effective trading strategies. Moreover, Gen AI continuously learns and adapts to new data, keeping trading algorithms robust and responsive to market dynamics.

Use Cases of Generative AI in Finance

Market Prediction

Gen AI models can use past market data to forecast future trends. These predictions help financial institutions make savvy investment choices, keeping them ahead of market changes and letting them take advantage of new opportunities. By using predictive analytics, Gen AI can handle huge amounts of historical and real-time data, spotting patterns that predict future market movements.

Financial Forecasting

Gen AI can improve the accuracy of financial forecasts by analysing a wide range of economic data. These forecasts aid strategic planning, budgeting, and financial analysis, enabling organisations to make informed decisions. With its ability to combine various data sources and perform complex analyses, Gen AI produces detailed and accurate financial projections that strengthen long-term business strategies.

Sentiment Analysis

By analysing social media posts, news articles, and other textual data, Gen AI can assess market sentiment. This analysis offers crucial insights into market trends, investor behaviour, and possible market disruptions. Understanding these sentiments allows financial institutions to foresee market shifts and make proactive decisions, thus improving their strategic position.

Challenges and Risks of Using Generative AI

The use of Gen AI raises ethical issues, such as the potential for misuse and the impact on employment. Ensuring that AI systems are used responsibly and transparently is crucial to addressing these concerns. Ethical frameworks and guidelines must be established to govern the development and deployment of AI technologies, ensuring that they are aligned with societal values and ethical principles.

Data Privacy Issues

Gen AI systems require a lot of data, which brings up serious privacy issues. To keep the trust of customers and to meet regulatory rules, handling customer data safely and ethically is key. Using techniques to anonymise data, applying encryption, and setting strict access limits are essential steps to protect sensitive data and prevent possible data breaches.

Algorithmic Bias

Artificial intelligence (AI) models can sometimes mirror the biases in their training data, leading to unfair or biased outcomes. To tackle this, it’s essential to constantly strive to identify and correct these biases in AI systems. This requires thorough examination and modification of both the training data and the machine learning techniques to enhance fairness. Making AI systems fairer is key to developing technology that treats everyone equally.

Additionally, the use of Explainable AI (XAI) plays a major role in overcoming these problems by making AI’s decision-making processes clearer and more comprehensible. XAI helps us look into the workings of AI systems, understanding how and why certain decisions are made. This enables the detection and correction of any biases, ensuring that AI systems are fairer and more reliable.

Regulatory Compliance

Banks and other financial institutions face a tricky set of rules when they use AI that creates new content. They must make sure they follow data protection laws, financial rules, and industry standards. This is important to stay out of legal trouble and keep a good reputation. They need to keep up with changes in the law and set up strong plans to handle the risks that come with using AI technology.

Case Studies of Generative AI in Banking and Finance

JPMorgan Chase

JPMorgan Chase has leveraged the capabilities of Gen AI to greatly enhance its fraud detection systems and refine its product and experience personalisation. With its vast data repository of 500 petabytes across 300 use cases, JPMorgan utilises Gen AI to enhance data value and operational efficiency by training AI models on extensive datasets to extract meaningful insights and improve decision-making. Additionally, Gen AI is employed to deliver personalised products and experiences to customers, as evidenced in the Commercial Bank division, where AI-driven growth signals and product suggestions have already generated $100 million in financial benefits.

Goldman Sachs

Goldman Sachs is leveraging Gen AI to enhance their risk management and regulatory compliance monitoring. They are developing advanced risk management systems that analyse vast datasets to predict and mitigate potential risks more effectively. Additionally, they are employing Gen AI to improve regulatory compliance monitoring, identifying gaps and areas at risk of violations by training AI models on regulatory requirements and financial data. Through these applications, Goldman Sachs aims to improve the accuracy and efficiency of their risk management and compliance efforts, thereby enhancing overall operational resilience and regulatory adherence.

HSBC

HSBC is utilising Gen AI to enhance their operations across customer service, risk management, and compliance. They are developing a new chatbot to answer customer questions and assist with tasks like money transfers and bill payments, aiming for more natural and engaging interactions. In risk management and fraud detection, HSBC uses Gen AI to create synthetic fraud data for training machine learning models, improving the detection of fraudulent activities and credit risk assessment. Additionally, they are enhancing compliance monitoring, particularly in money laundering detection, by training AI models on extensive datasets to identify suspicious activities and ensure regulatory compliance. Through these applications, HSBC seeks to improve efficiency, accuracy, and customer satisfaction while strengthening their risk management and compliance capabilities.

In a Nutshell

The Impact of Generative AI on Banking

Gen AI is undeniably reshaping the banking and financial sector, introducing cutting-edge solutions that enhance customer experiences, streamline operations, and transform financial services. By leveraging AI, financial institutions can remain competitive, boost efficiency, and offer personalised services that cater to the evolving needs of their clients.

The Path Forward

As AI technology advances, addressing ethical considerations and challenges is crucial to fully unlocking its potential in banking and finance. The future of this industry lies in the strategic integration of AI technologies, leading to a more efficient, customer-focused, and innovative financial ecosystem.

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Composable Architecture in Banking and Finance: Digital Onboarding

JurisTech — Fri, 10 May 2024 10:33:07 +0000

In today’s ever-changing banking and finance industry, embracing digital transformation is essential for staying competitive. Composable architecture stands out as a game changer, transforming how software solutions are created and deployed. This forward-thinking approach provides unparalleled adaptability, scalability, and effectiveness, completely transforming how customers interact with financial services. As client demands evolve, traditional onboarding methods are falling short. Enter digital onboarding platforms: indispensable tools for ramping up customer satisfaction, simplifying procedures, and boosting productivity.

Understanding Digital Onboarding Platforms

What are Digital Onboarding Platforms, and Why are they Important?

Digital onboarding platforms are the gateway to a seamless and convenient customer experience in the banking and financial sectors. They allow customers to open accounts, apply for loans, and access financial services online, without the need for physical interaction. In today’s digital-first world, these platforms are crucial for attracting and retaining customers.

Evolution of Digital Onboarding Platforms in Banking and Finance

Over the years, digital onboarding platforms have evolved from static, monolithic systems to dynamic, agile solutions. Traditional approaches often suffer from rigid architectures, making it difficult for banks and financial institutions to innovate quickly. Any changes or updates to the system require extensive coding and testing, leading to delays and increased costs.

Another challenge with traditional onboarding processes is the poor customer experience they offer. Lengthy forms, cumbersome verification procedures, and lack of personalisation can frustrate customers and drive them away to your competitors.

Read here for more information on The Proven Results of JurisTech’s Digital Onboarding Platform.

Understanding Composable Architecture

Composable architecture is a modular approach to system design that emphasises the assembly of independent, interchangeable components. These components, known as microservices, are loosely coupled and can be easily combined and reconfigured to meet specific business needs.

Core Principles of Composable Architecture

According to Gartner, at the heart of composable architecture lie four core principles:

Modularity: Composable architecture encourages the breaking down of large, complex systems into smaller, reusable services or modules, each of which is in charge of carrying out particular business operations. This modular design enables greater flexibility, as individual components can be developed, deployed, and scaled independently, without impacting the entire system.

Autonomy: Allowing these modular parts to function separately and integrate with other parts with ease.

Orchestration: Using APIs and other integration tools, coordinating these independent components’ interactions and integration.

Discovery: Making it simple to find and use pertinent parts from both internal and external sources.

Key Components of Composable Architecture

API-driven Approach

At the heart of composable architecture is an API-driven approach. APIs (Application Programming Interfaces) allow different components of the onboarding platform to communicate with each other seamlessly. This modular approach makes it easier to integrate new services and functionalities, all without throwing a spanner in the works of the existing infrastructure.

Microservices Architecture

Composable architecture relies on a microservices architecture, where applications are broken down into smaller, independent services. Each service performs a specific function and can be developed, deployed, and scaled independently. This makes the system more resilient and easier to maintain.

Containerisation

Containerisation is a key aspect of composable architecture, enabling the packaging of applications and their dependencies into standardised units called containers. This ensures consistency across different environments and facilitates easier deployment and management of software components.

Read here for more information on Composable Architecture in Banking.

Benefits of Composable Architecture in Digital Onboarding

Enhanced Flexibility and Scalability

The inherent scalability of microservices enables digital onboarding platforms to scale horizontally, adding or removing instances of services dynamically to accommodate fluctuations in user demand. This flexibility guarantees top-notch performance and dependability, even during peak times, crafting a silky-smooth onboarding journey for customers.

Enhanced Security and Compliance:

Composable architecture facilitates granular control over security measures, allowing organisations to implement fine-grained access controls and encryption mechanisms at the component level. This layered security strategy bolsters the overall defences of digital onboarding platforms, safeguarding sensitive customer data and upholding compliance with regulatory standards.

Cost Efficiency

Although the upfront cost of adopting composable architecture may raise eyebrows, the long-term benefits are worth every penny. By axing needless complexity and smoothing out operations, banks can slash their IT expenses, boost efficiency, and fast-track the launch of fresh products and services.

Accelerated Time-to-Market

The modular nature of composable architecture facilitates rapid development and deployment cycles. This means organisations can roll out new features and upgrades to their onboarding platforms at lightning speeds, seizing the competitive edge and grabbing hold of fresh opportunities in the market.

Implementing Composable Architecture in Digital Onboarding for Banking and Financial Institutions

Identifying Use Cases

Prior to implementing composable architecture, organisations must pinpoint specific use cases and business scenarios where it can deliver the most value. Common use cases include digital account opening, loan origination, KYC (Know Your Customer) processes, and risk assessment.

Best Practices for Implementation

Successful implementation of composable architecture requires careful planning, execution, and governance. Key best practices include:

Start Small, Scale Gradually: Begin with a small, well-defined project and expand iteratively, incorporating lessons learned along the way.

Focus on Business Value: Prioritise features and functionalities that align with business goals and customer needs.

Embrace DevOps Culture: Cultivate collaboration and communication between development and operations teams, smoothing the delivery process and boosting agility.

Monitor and Measure Performance: Implement robust monitoring and analytics tools to track system performance, spot bottlenecks, and optimise resource usage.

Overcoming Implementation Challenges

Despite its many advantages, implementing composable architecture can come with its fair share of hurdles, such as:

Organisational Resistance: Some stakeholders may resist the shift from traditional development practices they’re accustomed to.

Technical Complexity: Managing a large number of microservices and ensuring their seamless integration can be technically challenging.

Security and Compliance Concerns: Safeguarding data and meeting compliance standards in a dispersed, microservices-driven setup demands thorough planning and robust controls.

Case Study: Success Story in the Financial Sector

Chinese Bank WeBank Embracing Composable Architecture

WeBank’s core banking system is built on a microservices architecture, enabling swift adaptation to market shifts. This setup empowers WeBank to roll out fresh solutions, products, and services in under 20 days, thanks to the reuse and integration of existing components.

As a frontrunner in composable banking on the global stage, WeBank has swiftly risen to become China’s largest digital bank.

Building a Roadmap: Planning for Composable Architecture Adoption in Digital Onboarding

Assessing Organisational Readiness

Before embarking on the composable architecture journey, organisations should assess their readiness in terms of:

Technical Capabilities: Evaluate existing IT infrastructure, skills, and capabilities to determine readiness for adopting composable architecture.

Organisational Culture: Assess the cultural readiness for openness to change, teamwork, and innovation.

Strategic Alignment: Ensure alignment between composable architecture initiatives and broader business objectives and priorities.

Embracing the Future with Composable Architecture

As we navigate the ever-changing landscape of the banking and financial industry, one thing remains clear: embracing composable architecture in digital onboarding platforms is not just a trend, but a necessity. By realising its full potential, organisations can stimulate innovation, grab new opportunities, and stay ahead of the competition in the market.

JurisTech, Your Preferred Partner

At JurisTech, we are committed to giving your digital transformation initiatives the best possible support. In order to ensure that we provide the most competitive and effective solutions for the fintech landscape, Visa, a reliable partner in the Asia Pacific region, has accredited five of our software solutions, including our digital onboarding platform.

The validation of Visa highlights our dedication to providing consumers with quick and safe access to international technology suppliers. Our award-winning proficiency in digital customer onboarding and seamless integration enables your customers to have an exceptional customer onboarding experience with your company.

Built on a solid microservices architecture, our digital onboarding platform boasts integration capabilities via APIs and is fundamentally designed to be both scalable and flexible. Not only that, but we employ containerisation of our products via Continuous Integration and Continuous Deployment (CI/CD) using Gitlab and Jenkins, just to name a few.

You can rely on our team to help you navigate your system’s journey and precisely match it with your business requirements. Our members have a wealth of industry experience and combine technical expertise with business acumen. In addition, we offer thorough training prior to, during, and following project implementations to guarantee that all of your support requirements are met.

We place a high value on mutual understanding of performance expectations and go above and beyond to ensure that our clients have a positive experience during the digital transformation process.

For a sneak peek into our offerings, contact us today for a free demo. Find out how to improve customer satisfaction and streamline your onboarding process with JurisTech’s digital onboarding platform.

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Composable Architecture in Banking

JurisTech — Fri, 03 May 2024 08:25:30 +0000

Traditional monolithic architectures are showing to be less and less effective in the fast-paced world of modern banking, where innovation and agility are crucial. More flexible and dynamic architectural paradigms are being adopted as a result of the need for quick adaptation to shifting market dynamics, customer demands, and regulatory requirements. One such paradigm gaining traction in the banking industry is composable architecture.

Composable architecture represents a departure from traditional monolithic architectures, which are characterised by tightly coupled and interdependent components. Rather, it adopts a modular design philosophy, in which programmes are constructed as a group of independently deployable, loosely coupled services or components. The fundamental concept of composable architecture is composability, which emphasises the capacity to dynamically assemble and reassemble business capabilities in response to changing demands.

Recent years have seen a dramatic shift in the banking architecture landscape due to technological advancements, changing customer expectations, and heightened competition. In this context, modular and flexible system and process architecture, or composable architecture, becomes a strategic approach that helps banks foster innovation, scalability, and agility.

What is Composable Architecture?

Core Principles of Composable Architecture

Fundamentally, composable architecture consists of four essential elements that set it apart from conventional methods. According to Gartner, these include:

Autonomy: Allowing these modular parts to function separately and integrate with other parts with ease.

Orchestration: Using APIs and other integration tools, coordinating these independent components’ interactions and integration.

Discovery: Making it simple to find and use pertinent parts from both internal and external sources.

Key Components of Composable Architecture

Composable architecture comprises various elements, including:

Microservices: Small, independently deployable services that encapsulate specific business functionalities.

API Gateway: Acts as a single entry point for clients to access multiple microservices.

Event-driven Architecture: Facilitates asynchronous communication and decoupling between services.

Containerisation: Packaging microservices into lightweight, portable containers for easy deployment and management.

Why is Composable Architecture Good in Banking?

Composable architecture adoption provides banks looking to modernise their IT operations and infrastructure with a number of strong advantages. The digital onboarding, loan origination, and debt collection phases of the credit management lifecycle will be the main topics of discussion in this article.

How is Composable Architecture Good for Digital Onboarding?

Financial institutions can benefit from compositable architecture in digital onboarding platforms as it can help them become more agile, innovate more quickly, and offer a more individualised customer experience.

Agility and Speed to Market: Financial institutions can launch new products in a matter of months thanks to composable architecture, greatly reducing time-to-market. This is accomplished by merging pre-existing, modular components and making sure that no legacy technology is interfered with.
Hyper-personalisation of the Customer Experience: By utilising APIs to link different services and systems, composable banking technology can assist banks in providing a more personalised customer experience. With this strategy, banks can leverage more internal data sets for contextual engagements while providing a smooth and integrated experience for customers.
Data and Customer-Centric Proposition The idea behind composable banking is to enable integrated data sets and a single source of truth, which enables the provision of individualised experiences and the use of advanced analytics to facilitate more informed decision-making.
Low Maintenance Costs and Automatic Upgrades: Composable banking requires fewer resources, no major capital investments, and comes with low maintenance costs and automatic upgrades.

Read here for more information on Composable Architecture in Digital Onboarding.

How is Composable Architecture Good for Loan Origination?

Composable architecture in loan origination platforms for banking offers several benefits that can help financial institutions increase agility, innovate faster, and provide a more personalised customer experience.

Enhanced Customisation for Lenders and Clients: Lenders can tailor the origination process to their particular requirements when using a composable process. For instance, they might decide to automate the underwriting process by using a machine learning-based credit decision engine. Or they might decide to use a third-party data provider to validate borrower information. They can also easily incorporate the latest and greatest technology features specific to their customers’ unique needs.
Reusability: Developers can create new functionality more often by utilising pre-existing components in multiple applications thanks to composable architecture. This guarantees that applications are built with a high degree of consistency and reliability while also accelerating the development time.
Scalability: Financial institutions can scale their applications by adding or removing components as needed without affecting the system as a whole thanks to composable architecture. Hence, without compromising effectiveness or performance, businesses can readily adapt to shifting traffic volumes and user demands.
Unlock Greater Innovation and Return on Investment: Composable architecture enables banks to create once, then share, connect, and repurpose their digital assets and data in novel ways to spur innovation, address challenges, and accomplish a variety of goals. It is a brilliant, effective method of getting more use out of their technology investment and a higher return on investment.

How is Composable Architecture Good for Debt Collection?

Financial institutions can benefit from several advantages provided by composable architecture in debt collection platforms, including increased efficiency, lower costs, and better customer satisfaction.

Personalisation and Customer Experience: By employing data analytics and customer segmentation to customise messages to each individual’s needs and preferences, banks can personalise their communication with debtors thanks to composable architecture. This strategy can improve customer relationships and raise the likelihood of a successful debt recovery.
Agility and Adaptability: Composable architecture allows banks to respond quickly to changing market conditions and customer demands. A bank, for example, can incorporate a new payment method or cryptocurrency module into its platform if it detects a growing demand for such services.
Digital Engagement and Automation: Digital tools and automation can be integrated with composable architecture to provide debtors with hyper-personalised interactions with banks. Self-service applications that manage payments, cheque balances, and request payment rearrangements are examples of this. These apps have the potential to increase customer satisfaction and recovery rates.
Cost Efficiency and Innovation: Composable architecture can help banks reduce costs by allowing them to use best-in-class solutions and services rather than relying on a single vendor. This approach can also promote innovation by allowing banks to introduce new fintech features and products alongside their core systems.

Challenges and Risks for Composite Architecture in Banking

Even though composable architecture has many advantages, there are risks and obstacles associated with its adoption.

Integration Complexities: It can be difficult and complex to integrate various services and systems in a composable architecture environment, especially when working with legacy infrastructure or external dependencies. Banks need to make significant investments in solid integration frameworks and technologies in order to guarantee smooth component interoperability and communication.
Security Concerns: As APIs and microservices proliferate, so do the attack surface and potential points of entry for cyber threats. This presents new security considerations and vulnerabilities associated with composing architecture. To protect sensitive data and transactions in a composable environment, banks must put strong security measures in place, such as authentication, authorisation, encryption, and monitoring.
Component Identification, Individual Development, and Interoperability Challenges: Organisations can easily accelerate software development processes by simply enabling the reuse of code and eliminating (or at least reducing) the need to develop code from scratch. However, identifying the appropriate components, developing them independently, and ensuring interoperability can be difficult.

Implementation Strategies for Composite Architecture in Banking

Careful planning, execution, and governance are necessary for the successful adoption of modular architecture. Depending on their unique requirements, capacities, and priorities, banks can use a variety of implementation strategies.

Banks can choose to implement composable architecture in stages, beginning with isolated projects or initiatives and gradually expanding its scope and footprint throughout the organisation. Or, in a more drastic move, banks might decide to completely restructure their entire IT environment from the ground up by starting a rigorous transformation journey.

Regardless of the adoption approach, banks should adhere to certain best practices to maximise their chances of success. These include developing cross-functional cooperation and communication, defining precise business objectives and metrics, coordinating technology choices with strategic objectives, and utilising automation and DevOps techniques to expedite development and operations.

Case Studies of Banks Embracing Composable Architecture

Composable architecture has been adopted by a large number of banks and financial institutions globally in order to promote innovation, agility, and competitiveness. Let us look at a few interesting case studies to show how composable architecture is being used in banking.

Several leading banks and financial institutions have embraced composable architecture to drive innovation, agility, and customer-centricity. For instance, the multinational Spanish banking group BBVA has accelerated digital transformation and modernised its IT infrastructure through the use of a composable approach, which has shortened the time to market for new goods and services.

For its core banking system, New-Jersey based Cross River Bank has also embraced an open banking framework that facilitates the quick development and implementation of new services, as well as a modular and flexible architecture. Cross River Bank has effectively changed into a technology-driven community bank that provides a broad range of cutting-edge financial services while adhering to its community-focused roots by utilising composable banking solutions.

Regulatory Considerations for Implementing Composite Architecture in Banking

Adherence to regulatory mandates is crucial in the heavily regulated banking sector. Banks must manage a complicated web of rules and guidelines controlling data security, privacy, risk management, and financial reporting as they make the switch to composable architecture.

Banks have to make sure that their modular architecture conforms to all applicable regulations, such as GDPR, PSD2, PCI DSS, Basel III, and KYC/AML regulations. This means putting in place strong data protection protocols, carrying out frequent evaluations and audits, and keeping thorough records to prove compliance.

Regulatory frameworks and guidelines play a significant role in shaping the design and implementation of composable architecture in banking. Open banking initiatives, like the European Union’s Revised Payment Services Directive (PSD2), require banks to make their APIs available to third-party developers. This encourages the use of composable architectures, which in turn promote API-driven innovation and integration.

Banks can use a variety of tactics to stay compliant in a composable architecture environment, such as putting secure coding practices into place, enforcing data encryption and access controls, performing frequent security audits and assessments, and working with industry associations and regulators to stay on top of changing standards and best practices.

Future Trends for Composite Architecture in Banking

Future developments for composable architecture in banking are expected to be influenced by a number of up-and-coming trends, including:

The evolution of composable architecture is being driven by emerging technologies like distributed ledger technology (DLT), cloud computing, and serverless computing. These advancements allow banks to create more inventive, scalable, and resilient systems and services.

The banking industry is expected to adopt modular architecture due to the growing demand for agility, innovation, and cost-efficiency in a digitally-driven and fiercely competitive environment. Composable architecture will allow banks to work together more effectively and create new opportunities for value creation. This will change the banking ecosystem and the way customers interact with banks.

Although composable architecture seems to have a bright future, banks still face a number of obstacles and unknowns, such as regulatory scrutiny, complicated technology, a talent shortage, and pressure from competitors. However, banks can turn these difficulties into chances for sustainability, growth, and differentiation if they have the proper leadership, execution skills, and strategic vision.

In a Nutshell

In summary, banks are designing, constructing, and running their IT systems and services differently now thanks to composable architecture. In a world that is becoming more digital and dynamic, banks can seize new chances for innovation, agility, and competitiveness by embracing modularity, flexibility, and interoperability. Although the transition to composable architecture may present obstacles and hazards, banks that are prepared to embrace change and shape the direction of banking architecture stand to gain a great deal.

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