Machine Learning Approach for Credit Score Predictions

This paper addresses the problem of managing significant rise in requests for credit products that banking and financial institutions face. The aim is to propose an adaptive, dynamic heterogeneous ensemble model integrates XGBoost Support Vector Machine models improve accuracy reliability risk assessment scoring models. method employs machine learning techniques recognise patterns trends from past data anticipate future occurrences. proposed approach compared with existing score validate its efficacy using five popular evaluation metrics, Accuracy, ROC AUC, Precision, Recall F1_Score. highlights models’ challenges, such as class imbalance, verification latency concept drift. results show outperforms regarding achieving a balance between predictive computational cost. conclusion emphasises significance sector developing robust reliable evaluate creditworthiness their clients.