You're managing a transaction monitoring program, dealing with a growing alert queue, and preparing for an examiner's scrutiny. The debate over whether to prioritize machine learning or rules-based scenarios is a common challenge for compliance officers. Both approaches have valid points, and neither is inherently wrong.
Understanding the Compliance Challenge
Under the Bank Secrecy Act and the USA PATRIOT Act, with FinCEN regulations guiding daily operations, your institution must monitor customer activity and file Suspicious Activity Reports (SARs) when necessary. The legal obligation is clear, but how you fulfill it is flexible.
The stakes are high. Non-compliance penalties can range from thousands to billions of dollars, depending on the severity. Beyond fines, there's the risk of reputational damage and loss of customer trust. So, should your detection engine rely on deterministic, rules-based scenarios, or should you incorporate machine learning models to identify anomalies that rules might miss?
Advantages of Rules-Based Monitoring
Proponents of rules-based monitoring present strong arguments.
Explainability. When an examiner questions why an alert was triggered or missed, a rule provides a clear answer. "Cash deposits aggregating above a defined threshold within a set window" is straightforward to document and defend. The FFIEC BSA/AML Examination Manual expects you to understand and justify your monitoring logic. A specific threshold is easier to explain than a model score.
Typology Coverage. Structuring (Smurfing) is a well-defined behavior: multiple deposits just below a reporting threshold, spread across accounts or days. A scenario built for this pattern is transparent, and you can demonstrate coverage against a known typology. Rules allow for a direct link from risk assessment to scenario to alert.
Governance Maturity. Model validation, change control, and threshold tuning for rules are familiar processes. Your second line and internal audit team know how to test them. Independent testing is essential, and rules make this testing straightforward.
The downside: rules only catch what is explicitly defined. They generate high false positives when thresholds are conservative and miss novel behavior outside the logic.
Advantages of Machine Learning
Advocates for machine learning argue that criminals adapt to static thresholds, so detection must adapt too.
Unscripted Detection. Behavioral models establish a baseline for a customer or peer group and flag deviations without predefined scenarios. This is crucial for networks of accounts acting together, where no single transaction triggers a rule, but the overall pattern is suspicious.
Improved Alert Quality. Teams overwhelmed by low-value alerts waste time on noise while genuine risks wait. Models that prioritize or suppress likely false positives can focus investigator efforts on significant cases. Many institutions now use automated customer identification and transaction monitoring systems to enhance their programs and reduce risk.
Scalability. As transaction volumes and data sources grow, manually tuning thresholds becomes impractical. Models can process more variables than any rule set can.
The downside: opacity. If you can't explain a model's output to an examiner or show that its training data isn't flawed, you've traded one risk for another. Model risk management becomes a program in itself.
The Practical Approach
Few institutions use a purely rules-based or machine learning approach. The common strategy is a layered one.
Rules remain the backbone for known typologies and scenarios tied to specific regulatory expectations. They're a defensible foundation. Machine learning is added on top, usually in one of three roles: prioritizing and scoring alerts generated by rules, running in parallel to identify activity rules miss, or supporting entity-level analysis like linking customers into networks.
Successful teams consistently:
- Run Models in Parallel First. Compare model output against existing alerts before implementing changes, and document the results.
- Maintain a Rules Foundation. Ensure models aren't the sole control for critical typologies.
- Document Everything. Treat feature selection, training data, and validation as examinable.
- Preserve Human Judgment. Keep SAR decisions with trained investigators, supported by tools.
Customer due diligence is crucial. Regardless of the engine you choose, monitoring is only as effective as the customer risk data it uses. Weak identity verification, outdated information, or poorly managed Politically Exposed Person (PEP) designations undermine both approaches. The same applies to Watchlist Screening quality, which is a separate control that no scoring model should absorb.
Our Recommendation
Don't view this as a competition. Instead, ask which control manages which risk and whether you can prove it.
Our advice: use rules as your defensible base and gradually incorporate machine learning. Start where the benefits are clear and the explainability burden is light, typically in alert prioritization and false-positive reduction, since the underlying alert still comes from a documented scenario. Move to anomaly detection and network analytics once your model governance can withstand examination.
The tradeoff is real. A rules-heavy program is easier to defend but slower to adapt and noisier. A model-heavy program adapts faster and can sharpen investigator focus, but it requires stronger validation, better data discipline, and a team that can explain outputs clearly. If your model risk function is underdeveloped, rely on rules longer. If your alert backlog is a bigger issue, invest in governance sooner.
Ultimately, the test is the same one your examiner will apply: can you show that your monitoring matches your risk and explain every choice in between?
