Exploring Explainable AI Techniques to Enhance Transparency in Financial Fraud Detection Systems
Keywords:
Explainable Artificial Intelligence, Financial Fraud Detection, Transparency, Interpretable Models, Machine Learning, Regulatory Compliance, Ethical AIAbstract
The integration of Artificial Intelligence (AI) into financial fraud detection has significantly enhanced the ability of institutions to identify and mitigate fraudulent activities. However, the opacity of complex AI models, often referred to as "black boxes," poses challenges in transparency and trust. This paper explores the application of Explainable AI (XAI) techniques to improve transparency in financial fraud detection systems. By employing interpretable models and providing clear, human-understandable explanations for AI-driven decisions, stakeholders can better comprehend the rationale behind fraud detection outcomes. This transparency not only fosters trust among users but also ensures compliance with regulatory standards. The study reviews current XAI methodologies applicable to fraud detection, discusses their effectiveness, and highlights the balance between model interpretability and predictive performance. Findings suggest that integrating XAI into fraud detection frameworks enhances decision-making processes and promotes ethical AI deployment in financial services.
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