Fraud detection is evolving rapidly, with the use of Artificial Intelligence (AI) and Machine Learning in anti-fraud programs expected to nearly triple over the next two years, according to the 2024 ACFE Anti-Fraud Technology Benchmarking Report. But can AI alone effectively detect fraud?
At Eye2Scan, we believe that combining rule-based controls with Machine Learning-driven controls creates a more robust, efficient, and adaptive anti-fraud strategy. Let’s explore the strengths and weaknesses of each approach:
Rule-Based Controls
PROS:
- Ideal for known risks: Detects suspicious patterns based on predefined rules, such as transaction thresholds, process breaches, or flagged keywords.
- Full control and transparency: Easily explainable to auditors and regulators, ensuring compliance and accountability.
- Consistency and compliance: Ensures consistent application of rules across all monitored areas.
- Quick to implement and respond: Provides clear alerts for predictable risks, enabling rapid intervention.
CONS:
- Requires frequent updates: Rules must be updated to keep pace with evolving fraud tactics, regulations, or organizational changes. Without updates, they risk becoming outdated.
- Limited adaptability: Struggles to detect new or unexpected fraud patterns, often requiring increasingly complex rules to minimize false positives.
- Reactive rather than proactive: Rules address known risks, allowing fraudsters to exploit gaps before updates are implemented.
- Requires strong expertise: Developing effective controls tailored to specific organizational risks demands significant expertise and a deep understanding of fraud dynamics.
Machine Learning-Driven Controls
PROS:
- Dynamic and continuously learning: Machine learning adapts to new data, identifying subtle patterns and anomalies that static rules may overlook. It self-learns and becomes more accurate over time, evolving to counter emerging fraud tactics.
- Predictive and proactive: AI models analyze historical and current data to identify trends, anticipate fraud scenarios, and detect anomalies before they escalate.
- Scalable and multi-dimensional: Capable of processing vast amounts of data while incorporating new perspectives, addressing diverse and emerging threats effectively.
CONS:
- Lack of interpretability: The complexity of AI algorithms can make it difficult to explain why specific anomalies are flagged, creating challenges in justifying findings to authorities.
- Potential for false positives: Supervised models rely on labeled data, limiting their scope, while unsupervised models may flag patterns that are harder to interpret, requiring manual review.
- High resource investment: Implementing AI-driven controls requires substantial resources, including development costs, time to label data, algorithm training, and ongoing maintenance.
- Infrastructure requirements: Requires robust systems to monitor performance and maintain stability.
Key Considerations for Implementing AI Controls
- Robust datasets: Large and diverse datasets are essential for training AI models effectively and ensuring accurate fraud detection.
- Data integrity: Ensure datasets do not include hidden fraud patterns that could be misinterpreted as normal behavior, potentially normalizing fraudulent activities.
- Model selection: Choose AI models that align with organizational goals to minimize false positives and enhance interpretability, making flagged anomalies easier to justify.
In Summary
The pros and cons of these approaches will continue to evolve, but it’s not about choosing one over the other. The most effective anti-fraud strategy begins with solid rule-based controls to address foundational risks quickly and ensure compliance. Once these are in place, Machine Learning can complement them by tackling more complex and dynamic challenges, providing a long-term, adaptive solution.
At Eye2Scan, we’ve observed a closing of the loop with our clients: Machine Learning identifies new risks by analyzing patterns and atypical behaviors, which in turn inspire the creation of additional rule-based controls.
Want to know more about how Eye2Scan combines these controls to maximize efficiency and compliance? Contact us today.