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Credit Risk Scoring: A Stacking Generalization Approach
Forecasting the creditworthiness of customers in new and existing loan con-tracts is a central issue of lenders' activity. Credit scoring involves the use of analytical methods to transform historical loan application and loan perfor-mance data into credit scores that signal creditworthiness, inform, and deter-mine credit decisions, determine credit limits and loan rates, and assist in fraud detection, delinquency intervention, or loss mitigation. The standard approach to credit scoring is to pursue a “winner-take-all” perspective by which, for each dataset, a single believed to be the “best” statistical learning or machine learning classifier is selected from a set of candidate approaches using some method or criteria often neglecting model uncertainty. This paper empirically investigates the predictive accuracy of single-based classifiers against the stacking generalization approach in credit risk modeling using re-al-world peer-to-peer lending data. The findings show that stacking ensem-bles consistently outperform most traditional individual credit scoring models in predicting the default probability. Moreover, the findings show that adopt-ing a feature selection process and hyperparameter tuning contributes to im-proving the performance of individual credit risk models and the super-learner scoring algorithm, helping models to be simpler, more comprehen-sive, and with lower classification error rates. Improving credit scoring mod-els to better identify loan delinquency can substantially contribute to reduc-ing loan impairments and losses leading to an improvement in the financial performance of credit institutions.