Explainable AI for Decision Support in Financial Risk Assessment
Abstract
The adoption of AI in financial risk assessment is often hindered by the lack of transparency in decision-making processes. This paper proposes an Explainable AI (XAI) framework for financial institutions, enabling stakeholders to understand and trust AI-driven risk assessments. The framework combines interpretable machine learning models with visualization techniques to explain predictions, such as credit scoring and fraud detection. Case studies on financial datasets demonstrate that the proposed system maintains high predictive accuracy while providing actionable insights. This research emphasizes the importance of explainability in building ethical and reliable AI systems for the financial sector.
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