Modernizing Core Banking Infrastructure: The Role of AI/ML in Transforming IT Services

Abstract

Modernizing Core Banking Infrastructure: The Role of AI/ML in Transforming IT Services In today’s rapidly evolving digital landscape, retail banks are increasingly focused on modernizing their core banking infrastructure. As part of this effort, financial institutions are moving forward with a component-based architecture and interoperability layer, supported by data lake and new-age analytical frameworks [1]. Adopting a component-based architecture framework mitigates the high cost of technology replacement and effectively handles scalability and security issues. A component-based architecture with an interoperability layer facilitates easy integration with new outside components, offers rapid access to real-time data, and supports the utilization of new-age analytics frameworks. Significant investments are being made to improve customer relationships, bolster risk frameworks, and optimize governance, budgeting, and strategic planning. Along with the component-based architecture, financial institutions are amassing data lakes that provide centralized storage for unified data management and retail banking analytics. A new-age data science and analytics framework will help parse large volumes of data spread across disparate, multi-structured data sources into structured data formats, allowing organizations to glean actionable business insights [2]. Banks can strategically use these insights, thereby significantly improving customer experience and engagement, agency performance, asset quality, fraud management, and other key operational and business performance metrics. This paper summarizes the role that AI/ML can play in retail banking transformation through modernization of the IT services domain. The objective is to lay out specific AI/ML capabilities that can be harnessed to create a future-ready core banking architecture, either by making process/technology investments or by collaborating with newer, niche solution providers.

Over the past three decades or so, the banking industry has seen several disruptive technology shifts. The journey began with the development of defensive technology shields to protect banks from ravenous competitors; the emergence of the internet age technology paradigm, in which banks were challenged to upgrade their technological infrastructure; the introduction of innovative customer-facing tab-based visualizations and interactivity, which obligated banks to significantly transform their offerings; and the mainstreaming of smart phones, which necessitated a paradigm change in consumer engagement. The most recent disruption to engulf the industry, ‘fintech’, is fundamentally altering core banking technology, services, and operating models, ultimately threatening banks’ relevance. In response, banks are investing massively in IT services and technology to evolve beyond traditional high-margin service providers toward digital transparency,

extensibility, and scalability with a move to an ‘API economy’, digital rating platforms, cloud-based third-party innovation ecosystems. Banks need to respond to disruptive external threats by building a resilient, agile, cohesive, and responsive core banking engine-as-a-service operating infrastructure and structural deposit collection, risk management, and profitability-optimizing architectures. In this new core banking technology and services landscape, banks will find it difficult to remain relevant. Cognitive AI will be necessary to extract quality insight from the vast amounts of data generated by business processes, consumer engagement, risk modeling, regulation, and compliance. Native AI, a part of the core banking service structure, will need to be a differentiator offering real-time insights during consumer engagement, risk detection, and evolving the compliance architecture. AI-enabled IT Ecosystems, powered by blockchain technology, will need to establish credibility across domains, geographies, and industries to be successful in creating pre-emptive, quality insight and oversight.