As financial crime tactics become increasingly sophisticated, financial institutions (FIs) are gradually turning to artificial intelligence (AI) for solutions to detect potentially fraudulent activities and transactions. Integrating AI can support higher predictive accuracy, but AI models are often opaque—“black boxes” lacking transparency into how the models arrive at their fraud risk predictions.
This lack of interpretability is particularly concerning for fraud detection use cases. Strong model governance and responsible use of AI dictates that FIs must be able to understand how their AI systems work, explain their AI systems-derived risk decisions, and justify interventions such as blocking transactions or accounts. Regulators, auditors, customers, and the FIs themselves all have a vested interest in understanding the reasoning behind these high-stakes fraud outcomes.
Unexplainable fraud detection AI that relies on black-box AI models for fraud detection opens up FIs to major potential risks:
- Regulatory scrutiny: Rules like the U.S. Federal Reserve’s SR 11-7 guidance require that models employed for risk management, including fraud detection, must be able to be comprehended by humans. Lack of transparency can lead to regulatory penalties. As other regulatory requirements—in the U.S. and globally—come down the pipeline, transparency will come even more to the fore.
- False positives and negatives: Without being able to inspect and validate a model’s decision-making logic, FIs could end up with systems that flag too many legitimate transactions (false positives) or fail to catch actual fraud (false negatives). This can lead to a backlog of alerts, overwhelmed operations teams, and poor experiences for good customers.
- Customer friction, distrust, and attrition: If a customer’s transaction is blocked due to suspected fraud with no clear justification provided, it can severely damage trust and drive them to take their business elsewhere.
Challenges of Interpretable Fraud Detection
The demand for explainable AI in fraud detection, while clear, presents significant challenges due to its high-stakes nature and complexity. Building accurate and interpretable models requires addressing several critical challenges. First, the data is highly unbalanced, with legitimate transactions far outnumbering fraudulent ones, requiring models to maintain accuracy despite this skew. Additionally, fraud tactics are constantly evolving, necessitating frequent model updates to catch new patterns without being derailed by concept drift. Privacy considerations add another layer of complexity, as any explanations about fraud predictions must be carefully filtered to protect detection methods and customer information.
Institutions are exploring more transparent and interpretable machine-learning techniques specifically tailored for fraud detection to navigate these challenges. These solutions include interactive visualizations and dashboards that clearly interpret how transactions are assessed for fraud risk based on various data features. Organizations are also implementing approaches that automatically generate human-readable rules and rationale from trained models, allowing fraud experts to inspect and modify them as needed. Moreover, they are deploying neural networks that incorporate attention mechanisms to provide visibility into which input features most significantly influence particular predictions.
Transparent and interpretable fraud detection systems will be critical for FIs to have as fraudsters evolve their tactics and regulators increase scrutiny around AI explainability. Achieving that transparency is challenging, but it’s a necessity for maintaining customer trust, abiding by regulations, and effectively combating financial crime with AI.
For further reading on the future of responsible AI—alongside other challenges and opportunities facing financial crime fighters in 2025—take a look at Datos Insights’ report, Top Trends in Fraud & AML, 2025: Heading Into a Turbulent Year.
