A Predictive Analytics Approach For Financial Risk Management Using The Catboost Algorithm

Abstract

Financial risk prediction is a crucial element of financial risk management inside financial institutions. The efficacy of machine learning models, particularly CatBoost, in accurately predicting financial risk has not been thoroughly examined, however it has promise. This study aims to compare Catboost with other machine learning (ML) models, including Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost, and Logistic (LR) Regression, in terms of their efficacy in forecasting financial risks. The Financial Risk Dataset comprised transaction details, loan information, and client data. Cross-validation was employed to train models, while precision, accuracy, F1-score, recall, and AUC were utilized to evaluate the performance of the trained models. The CatBoost model exhibited superior performance across all models, achieving an 95.93% accuracy, 95% recall and precision, and 0.98 AUC. The model exhibited superior performance when category information was included with minimal pre-processing. XGBoost and RF also had a good performance, but were slightly less accurate than CatBoost. The worst performance was obtained by KNN, where the performance was the lowest in all the metrics. CatBoost outperformed the other models in financial risk prediction. It is good at dealing with categorical data and has a powerful gradient boosting (GBoost) mechanism, which helps it to be very predictive. It would be interesting to see how it can be used in other financial fields and with extra functionalities such as real-time data and time-series analysis in future studies.