In an interaction with CIOTechOutlook magazine, Mike Yesudas, CTO, SunTec Business Solutions, shared his insights on various aspects of how new-age technologies, such as AI and ML, have had a major impact on data processing and consumption in the BFSI sector, how has the evolution of high-performance systems changed, how are companies integrating AI technologies into their operational functions and more.Mike has diverse background in software development, performance, and security. Formerly a CTO for AI at IBM and an IBM Distinguished Engineer. His expertise lies in enterprise AI and shaping technical direction.

Tell us how new-age technologies, such as AI and ML, have had a major impact on data processing and consumption in the BFSI sector.

The banking, financial services, and insurance (BFSI) industry has long been recognized as one of the most data-rich industries in the world. With millions of transactions happening every day, the BFSI industry is sitting on a gold mine of data that, if used effectively, can transform customer experience, risk management, and operational efficiency. AI and ML have brought a significant transformation to the BFSI sector by revolutionizing how data is processed and consumed. Unlike traditional code-based systems, AI relies on data patterns and assumptions, which are derived from analyzing vast amounts of data. Financial institutions have benefited from a data-driven approach. It helps them gain valuable­ insights into customer behavior, personalize­ their services, and anticipate­ potential challenges. For instance, by analyzing withdrawal patterns, a machine learning model can predict when a customer is likely to run low on cash and proactively send an SMS message with information about the nearest ATM. Despite the potential benefits of such predictive services, many banks have yet to implement them due to conservatism and regulatory constraints.

Moreover, AI and ML can be used to detect anomalies in financial transactions, promptly notifying customers and supervisors about suspicious activities, thus acting as a digital twin for account holders. However, the BFSI sector has been slow to fully leverage its data-rich environment due to a reluctance to adopt SaaS solutions and the complexity of transitioning to AI and ML technologies.

How has the evolution of high-performance systems changed over the last decade?

The BFSI sector has undergone a gradual transformation over thepast decade. While consumer AI, with advancements in facial recognition and voice­ recognition, has made significant strides, enterprise AI lags. The challenge lies in dealing with low data volumes specific to business processes, making it difficult to create accurate machine learning models.

In the BFSI sector, some areas have witnessed improvements, notably in chatbots and customer service functions. However, for other segments of the industry, incorporating low data machine learning remains a formidable challenge. Startups are exploring solutions, and the advent of large language models capable of generating synthetic data holds promise for addressing this issue.

The integration of AI in high-performance systems is crucial as the nature of business data evolves over time. The ability to adapt and update models in response to changing data is essential to maintaining the accuracy and effectiveness of AI applications. The cost-effectiveness of utilizing SaaS solutions in this context becomes apparent. By doing so, organizations can optimize the utilization of high-performance computing resources only when necessary.

How does the integration of SaaS impact the cost-effectiveness of operations in the BFSI sector?

The integration of Software as a Service (SaaS) has the potential to significantly impact the BFSI sector in terms of cost-effectiveness. SaaS offers a flexible and cost-efficient model, eliminating the need for businesses to own and maintain infrastructure. This not only reduces expenses but also enhances adaptability. This is particularly beneficial for AI and ML applications, which often demand substantial computing resources.

SaaS allows BFSI companies to access highperformance computing resources as needed, without the burden of maintaining dedicated hardware. This costsaving approach is crucial for AI and ML, where periodic model generation and data analysis require substantial computing power. Instead of investing in expensive infrastructure, companies can opt for a pay-as-you-go model, reducing operational costs.

However, SaaS integration in the BFSI sector faces challenges such as regulatory compliance, data privacy, and secure data handling. Nevertheless, the costsaving benefits and agility make SaaS a compelling choice for the industry's future.

How are companies integrating AI technologies into their operational functions?

Companies in the BFSI sector are increasingly integrating AI technologies into their operational functions. The most common application is in customer service, where AI-driven systems can enhance customer interactions. For example, AI can identify customers, provide information about recent transactions, and offer suggestions to call center representatives during customer calls. This proactive support improves the efficiency and effectiveness of customer service operations.

AI can also assist in back-end operations within the BFSI sector, although this adoption is still in its early stages. As AI technology advances, companies are beginning to explore how predictive AI can improve business processes and streamline operations. In the realm of strategic decision-making, Predictive­ AI emerges as a valuable tool that provides insights into customer needs, financial trends, and risk assessment. This objective technology offers a comprehensive understanding of crucial factors for businesses.

The BFSI sector has yet to realize the full potential of AI in its operational functions, but as the technology matures and data analysis capabilities improve, we can expect more widespread integration across various business areas.

The Future Competitive Landscape in the Tech Industry

The future competitive landscape in the technology industry, especially within the BFSI sector, holds several exciting prospects. Key developments are expected in the following areas:

Low Data Machine Learning: Breakthroughs in low data machine learning will allow BFSI companies to create accurate models with limited data. This innovation will enable better predictive analytics and improve decision-making in scenarios with insufficient historical data.

Virtual Banking: The BFSI sector is moving towards offering more personalized and proactive services. AI-driven virtual banking assistants will become a common feature, providing real-time financial advice, alerts, and support to customers.

Advanced Back-End Operations: The BFSI sector will continue to explore the use of AI in optimizing back-end processes. Predictive AI will assist in risk assessment, fraud detection, and operational efficiency, ultimately driving better decision-making.

Regulatory Challenges: As AI integration grows, regulatory bodies will need to adapt and establish guidelines for the responsible use of AI in the BFSI sector. Data privacy and security will remain critical concerns.

The integration of AI and ML technologies into the BFSI sector is transforming the way data is processed, leading to more personalized and efficient customer experiences. SaaS solutions are becoming increasingly crucial for cost-effective AI adoption, while predictive AI is poised to revolutionize customer service and back-end operations. As the BFSI sector navigates the evolving competitive landscape, embracing these technological advancements will be critical to staying at the forefront of the industry and providing customers with the best possible financial services. The future holds exciting opportunities for innovation and growth in this dynamic sector.