Designing AI for Investment Banking Risk Management a Review, Evaluation and Strategy

Abstract

The technological advancements in artificial intelligence have brought newer opportunities for the investment banking sector. Primarily for the RISK management function which deals with identifying, gauging, reporting and dealing the risks across counterparty credit risk, market risk, operational, supervisory and liquidity risk functions. Given that the RISK management function is data centric and includes building efficient risk models, understanding and predicting market changes using quantum financial models and statistical tools and finally predict future behaviors, this makes it an ideal candidate for its evolution and transformation via artificial intelligence. Among other goals, this research aims at reviewing the verticals and use cases within the risk management and post-trade framework of an investment bank that can be transformed by the applications of artificial intelligence. Furthermore, review the academic research done in this area and bring out the functions that have been inadequately explored and probable areas for additional research. Given this is a strongly regulated industry, we would also review the appetite of financial regulators in accepting optimizations using ML and AI.