AI-Driven Financial Risk Assessment and Credit Decision-Making: An Analysis of Predictive Accuracy, Default Risk, and Lending Efficiency.

Abstract

Studying how artificial intelligence (AI) impacts the financial sector and more specifically credit assessments, is essential for predicting and understanding future trends. This research analyzes advanced risk and credit assessment, focusing on automated systems that enhance credit assessment and optimize portfolio management. Deep learning methods, such as feedforward and recurrent networks, are powerful in predicting financial outcomes and defauls. Defaults are potentially loss-making situations, and early identification allows for mitigation. The data used in this research consists of 1,200 loan applications, and 187 of these applications represent situations where the loans ended in a default situation. Various algorithms, such as logistic regression, decision trees, random forests, and boosted trees, are used to predict default situations. Logistic regression and random forests were the best performers. Income, credit, and debt, as well as loan to value situations, utilization of credit, and prior delinquencies, were the most important variables for predicting default situations. AI increased the capacity and reduced the time needed to review a case for credit, and therefore reduced the time required to process loans. Fairness and interpretability were critical when integrating AI with loan processing. A combination of phasing and flexibility when integrating AI with banking and financial services was suggested.