Unlocking Transparency in Credit Scoring: Leveraging XGBoost with XAI for Informed Business Decision-Making

Abstract

Credit score analysis is vital to modern banking systems, allowing banks and other financial institutions to determine a borrower's creditworthiness. In such a situation, accurate and robust prediction models are vital because they allow lenders to make rational decisions regarding loan approvals and risk management. This paper provides an overview of using XGBoost, a sophisticated machine learning algorithm, to improve credit score evaluation, and the XAI model, LIME, to describe the black box machine learning algorithm. XGBoost, a gradient boosting-based ensemble learning algorithm, has gained prominence for its capacity to give improved predicted accuracy while handling vast and complicated datasets. Its algorithmic characteristics, including regularization, parallel processing, and decision tree optimisation, make it especially well-suited for credit scoring problems. Because of its complexity, implementing XAI is critical since it will help lenders grasp the reasons for the result of the XGBoost. The results show how the XAI model, LIME, helps simplify the complexity of these models. It is critical to integrate XAI models since they will improve lender decision-making. The fundamental goal of this research is to evaluate the XAI model, LIME, and determine how well the XAI model explains the findings of our experimental tests. Furthermore, it illustrates the possibility of incorporating LIME into credit score analysis, resulting in more efficient lending procedures, enhanced risk management, and better decision-making. Finally, this paper emphasizes the importance of using advanced machine learning techniques such as XGBoost in credit scoring analysis, which has the potential to transform the way banks and other financial institutions assess credit risk, as well as include LIME for a better understanding of the results.