Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods

Transparency and accuracy are compatible goals: The research demonstrates that fraud detection systems can achieve both high predictive performance (99% accuracy) and explainability, challenging the common assumption that these objectives must be traded off against each other.

Explainability helps with regulatory compliance: The paper emphasizes that transparent AI models make it easier for financial organizations to meet regulatory requirements and demonstrate accountability to stakeholders—a critical concern for responsible deployment in regulated industries.

Multiple explanation techniques provide comprehensive understanding: The framework uses several complementary explainability methods (SHAP, LIME, and Permutation Feature Importance) to show why the model makes specific fraud decisions, rather than treating it as a "black box," which builds stakeholder trust.

Real-world scale validation matters: Testing on over 590,000 actual transaction records from the IEEE-CIS dataset suggests the approach is practical and applicable to real financial systems, not just theoretical exercises.

Responsible AI in finance is achievable: By combining ensemble machine learning with explainability techniques, organizations can deploy fraud detection systems that are both effective at catching fraud and transparent enough for customers, regulators, and employees to understand and trust.