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technologyreview.com·2h ago
AI Research
arXiv CS.AI·20d ago·5 min read
Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective
TL;DR
An explainable AI framework was developed to enhance anomaly detection in banking transactions, addressing the challenge of interpretability in automated systems. By integrating machine learning techniques with internal audit processes, the framework identifies suspicious activities while providing clear explanations for its decisions.
✦ Why It Matters
Implementing this explainable AI framework can enhance your bank's fraud detection capabilities and improve audit transparency immediately.
Key Takeaways
- Adopt XAI frameworks for improved anomaly detection in banking.
- Utilize SHAP for transparent feature-level explanations.
- Implement user-friendly dashboards for non-experts.
- Evaluate AI models on synthetic datasets for performance metrics.
How It Works
The framework employs an Isolation Forest model to score anomalies in banking transactions without needing labeled data. SHAP is then used to provide explanations for each transaction, attributing the anomaly score to specific features based on cooperative game theory principles.
This dual approach allows auditors to understand the reasoning behind flagged transactions, enhancing their ability to make informed decisions.