Machine Learning-Based Credit Default Prediction: Challenges, Concerns, and Model Comparisons

the digital era keeps leveraging machine learning (ML) techniques to enhance profitability within the financial sector. With the integration of cutting-edge technologies and services enabling financial institutions to extend microcredit to individuals with lower incomes. This digitization has streamlined various aspects of the loan application process, such as underwriting and electronic signatures, resulting in quicker loan disbursal while upholding conventional underwriting and compliance procedures. Traditional credit scoring models face constraints when adapting to big data technology for risk modeling. Consequently, ML-based credit risk models have emerged as a more efficient approach for forecasting defaults. Thus, this paper introduces ML model for predicting possible credit failure challenges and evolving issues using ML tools. The developed model used kaggle dataset for the study. The model employed decision Tree and Random Forest algorithm to efficiently develop a classification model to anticipate loan defaults before granting the loan.