In the past, the banking sector has handled customer data primarily within their internal systems like Core Banking System or Loan Origination System. Today, the quantum of data that they can leverage is so enormous and can be overwhelming to make a distinction between good and bad data and to process it towards good decisioning. Even in pre-Covid times, we were using AI to identify NPAs by helping bankers identify large risky accounts and flagging it off for action.
A black swan event like Covid-19 has accelerated the use of AI, Predictive Analytics, Big Data in the banking sector through FinTechs. Bankers now need to quickly measure the impact of a pandemic on their growth and recovery. To depend on even the best of human analytical expertise, this could take months or years to comprehend.
It has opened-up the black-box approach to risk management, offering more accountability and predictability in decision making in the banking system. Today, accessing data is simplified even more through digital sources, by and large, like Credit Bureau information, real-time alerts on corporate actions etc., that synthesize huge volumes of structured and unstructured data sources, thereby making technology inevitable in decision making. This further helped in moving away from orthodox methods to AI based analytics to quickly predict fallouts, quantify data and build recovery measures.