Revolutionizing lending and credit operations using predictive and prescriptive artificial intelligence analytics models

Lending and credit represent important components for the finance and banking sector, foster growth of various industries, and play a vital role in an economy. Predictable models help banks and credit providing organizations to identify different types of customers based on their profiles, behavior, needs, and repayment capabilities, thus reducing default risk and optimizing their lending and credit portfolios in terms of interest rate, credit limit, and tenure. However, traditional predictive analytics or decision modeling approach only explains or predicts “what might happen” in future and lacks the decision insight, thus unable to address the needs of financial institutions related to optimizing as well as improving their decision-making capabilities. Prescriptive analytics is a new emerging research area that goes beyond prediction to provide the decision insight by optimizing the objectives of lending and credit organizations using analytical models. However, within this broad definition of prescriptive analytics, the majority of the existing prescriptive analytics works perform the decision optimization and do not generate the prescriptive insight from the prediction.