Resilient Banking IT Infrastructure: Integrating AI/ML Models for Scalable, Secure, and Intelligent Operations

Abstract

Banking & Financial Services are very much core to the smooth functioning of any economy and therefore need to be reliable and trustworthy. A lot of systems and processes are involved in carrying out their smooth functioning. As the volume of transactions and the interconnectedness between the systems and processes have grown, the IT Infrastructure has also been transformed. Legacy systems have been replaced by state-of-the-art technologies that have brought efficiency, resiliency, and most importantly flexibility. Banking IT Infrastructure can now be scaled up and down based on the needs using Cloud Architecture. These technologies have also created new avenues for transaction channels that offer the customer a much more easy and efficient means of conducting transactions. However, along with the above advantages, this cyber transformation has opened up the IT infrastructure to vulnerabilities. Hence with financial institutions now facing the threat/repercussions of the cyberattacks, the pressure is on banks and regulators to build more resilient systems and to offer damage mitigating strategies. AI/ML /DL-based models are now providing the means to detect anomalies and malicious attacks. These models are also being built-in B2B financial systems to detect fraudulent transactions and money laundering. In this backdrop, this research work intends to study and analyze various models that can be integrated or embedded in the Banking IT Infrastructure for secure and intelligent operations.