An Interpretable Machine Learning Framework for Predictive Analysis in High-Risk Financial Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P104Keywords:
Interpretable Machine Learning, Predictive Analysis, Financial Systems, Explainable AI (XAI), Feature Engineering, SHAP, LIME, High-Risk Systems, Fraud DetectionAbstract
Predictive modeling in high-risk financial sectors faces significant challenges due to non-linear variable interactions, systemic risk, and the inherent instability of financial datasets. While black-box models often prioritize predictive efficiency over transparency, regulatory requirements in heavily regulated industries necessitate auditable and interpretable frameworks. This paper proposes a novel, interpretable Machine Learning (ML) framework designed for predictive analysis in high-stakes financial environments. The architecture integrates a federated learning system to preserve data privacy across distributed client devices while employing local and global model validation techniques. Our methodology incorporates advanced feature engineering, ensemble learning, and post-hoc interpretability tools, including SHAP (Shapley Additive explanations) and LIME (Local Interpretable Model-agnostic Explanations). Furthermore, we introduce specialized evaluation metrics, such as the Model Transparency Index and Conditional Value at Risk (CVaR) prediction accuracy, to better align model performance with financial stability goals. Empirical results using real-world bankruptcy and fraud datasets demonstrate that the proposed framework achieves F1-scores and AUC-ROC values comparable to state-of-the-art deep neural networks and Gradient Boosting Machines (GBMs). Specifically, the framework demonstrates a 15% performance improvement over existing rule-based methods like RuleFit. This work provides a strategic guideline for balancing the accuracy-interpretability trade-off, facilitating the legal and ethical deployment of AI in regulated financial systems.
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