From Data Lakes to Smart Decisions: Architecting AI/ML-Enabled Infrastructure for Future-Ready Banking

Abstract

The four pillars of the banking and finance sector, i.e., risk, return, asset quality, and compliance, are enormous and diverse in nature. Traditional research techniques or methods are falling short to process and analyze such data, owing to shear volume and velocity of transactions. AI/ML methods and modeling help fulfill these criteria, and interestingly, banks globally seem to show keen interest in adopting these technologies in their research framework in a big way. In this pursuit, various models are developed with a structured study at different banks, presenting a comprehensive big-data analytics of each insight, so that banks will be ready to take the next big jump in AI/ML-enabled infrastructure.

Big data today plays a vital role in all the industries to enhance their growth in all aspects. Banking, the oldest sector across the globe, is also shifting to big data methodologies to improve governance, operation, and customer satisfaction. Shadow banking, virtual banking, and crypto banking are examples of the latest technologies applied to improve day-to-day banking requirements. Traditional banking research work involves risk, growth return, asset quality, and compliance, but banks struggle to handle big data banking research outcomes]. There are plenty of algorithms, libraries, and data cleaning techniques for intelligent data science models, but direct relevance to banking models is limited. AI and ML technologies seem to be the new set of buzzwords often used to denote the future of technology. However, for banking data, it is still a distant dream owing to huge volume fluctuations, overwhelming data cleaning requirements, and interaction with various legacy systems in multiple formats.

The failure or fraudulent transaction occurrence of any entity reveals a gap in governance and risk management documentation. Enabling the event and transaction data in AI infrastructure can enhance timely actionable knowledge-based monitoring to avoid future failure and fraud. Using testing data and building algorithms may take too much time to monitor real-time data, prompting actors to miss early signs of warning failure or fraud while planning big data handling, data journeys, and governance approaches to fill the gaps.