Risk Estimation for Financial Contracts: Data Imbalance Elimination and Impact of Different Data Rates

Predicting the risk score of financial contracts plays a critical role in financial risk management by enabling companies to make more informed decisions. When predicting risks based on contracts, several challenges arise. In the financial sector, the imbalance of data is particularly common. This study comprehensively investigates the impact of the class imbalance problem on calculating the risk scores of financial contracts. To improve the performance of machine learning methods on imbalanced data, techniques such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach for Imbalanced Learning (ADASYN), Tabular Variational Autoencoder (TVAE), and Conditional Tabular Generative Adversarial Network (CTGAN) have been used for data balancing. Logistic Regression (LR) and Gradient Boosting (GB) models have been employed to evaluate the effect of these techniques on model performance. The main objective of the study is to analyze the performance of machine learning models based on different data imbalance ratios and assess their effectiveness in risk prediction.