Default Credit Predictive Analytics Model to Enhance Bank Decision Making Using Big Data

In the financial sector, figuring out a person›s creditworthiness may be exceedingly challenging. Individual credit scores or credit report data are a major component of traditional models that determine credit eligibility. If they can receive financial services at all, it will probably be at very high-interest rates and fees. This sort of strategy makes it exceedingly difficult for those with little to no credit to do so. Financial institutions have been looking for methods to include non-traditional data into the credit risk process to provide services to these people at more affordable prices while maintaining the risk at manageable levels. With an accuracy that is acceptable to financial institutions, Machine Learning (ML) could be utilized to estimate a person›s creditworthiness using atypical data. On the data supplied by Home Credit Group, we processed and engineered features. With an AUC of 0.7926 and 92% accuracy, the Light Gradient Boosting Machine (LGBM) model trained using this data can predict the probability of default using 5-fold cross-validation. The system produced promising results and outperformed all other state-of-the-art methods.