Explainable AI Models in Financial Risk Prediction: Bridging Accuracy and Interpretability in Modern Finance

Abstract

The modern situation in the field of financial technology evolving of the advanced machine learning methods in the field of financial risk prediction models has brought great changes to the overall model precision and scale. Many areas of application have popularized these models in credit scoring, loan default, and market volatility forecasting applications. Nevertheless, the hygienic nature of complicated algorithms, specifically the ensemble algorithms and deep learning models, creates significant doubts about their training interpretability, transparency, and regulatory conformity. The research offers an in-depth framework in which the components of Explainable Artificial Intelligence (XAI) are embedded into the pipelines of financial risks prediction in order to create a gap between predictive performance and interpretability. Precisely, SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Counterfactual Explanations are injected to the output of well known learning models, XGBoost, Random Forest, and Multi-Layer Perceptron Neural Networks. Experimentational assessments are carried out with standardized datasets like the FICO credit peril dataset and German Credit dataset, and past historical information of S&P 500 volatility. The obtained results prove that the models including XAI, apart from high predictive accuracy (up to 0.92 in AUC), improve the clarity and trustworthiness of the models with the help of explainable insights. To illustrate that point, the SHAP explanation based on the global explanation always reveals the most important financial indicators, e.g., a debt-income ratio and credit history, as opposed to LIME and counterfactuals, which are focusing granular on individual risk prediction. With these explanations, financial analysts and regulatory auditors can have a clearer picture of model outcomes and decisions they should base them on to be fair and, at the same time, data-driven but answerable. The results highlight the possibility of XAI techniques in the context of making the AI system fairer and more auditable in strictly regulated environments in the context of financial services. The proposed study can also be used to support the literature with the increasingly diverse argument in the favor of socially responsible application of AI to finance and to illustrate the importance of interpretation in generating human-readable, compliant, and trustworthy financial decision-making.