Making Credit Underwriting Process More Accurate using ML

Fintech, or the use of information technology in finance, is predicted to transform many facets of borrowing and lending in the future, but consumer and mortgage lending have already seen significant technological change. Computerization made it possible for mortgage lenders to speed up loan processing in the 1990s and largely replace human decisions about credit risk with predictions of default based on sophisticated empirical models. Many banks have improved the speed, cost, efficiency, and customer satisfaction of their credit underwriting process, yet they still face the risk of a consumer failing on the credit. Therefore, it is necessary to create a tool that can help banks select the ideal clients. The proposed system, which may be further modified for more reliable underwriting, can solve the issue. Artificial intelligence (AI) has adopted automated decision-making to improve processing capacity and optimization methods, particularly in machine learning (ML). The general concept of data analysis, ML and its use in the credit industry are presented in this paper. The aim of this paper is to develop a scientifically developed driven system to automate decisions and processing for stronger and quicker underwriting.