FIs Can’t Forget Rules in the Age of AI

Machine learning models and rules work together for fraud and financial crime prevention, much like city planners and building codes. AI and machine learning models set the grand vision—mapping out how to identify risk and adapt over time—while rules turn that vision into everyday safeguards that keep transactions flowing smoothly and securely. Smarter rules-based AI management is critical to delivering a seamless customer experience and stopping fraud.

Unfortunately, as easy as they are to create and set, rules are just as easy to forget. These days, with AI and machine learning systems capturing so much attention, it’s easy to assume that traditional rules might fade into the background without consequence. The truth? They’ve never been more critical—which is why banks and financial institutions must craft a plan to maintain their roster of fraud prevention rules.

The Trouble with Rules: Easy to Create, Easier to Forget

In some respects, rules have become akin to subscription-based services. It’s easy to sign up and use them. But before you realize it, you’ve got more than you realized and are trying to figure out why you signed up in the first place. 

Fraud managers and risk analysts can encounter the same problem. If not carefully maintained, banks can become overwhelmed by rules that no longer make sense. But because the rule may be tied to a specific business case, it’s too risky to remove it.

Top Rules Maintenance Pain Points for Banks and Acquirers

An extensive backlog of rules and confusion over their purpose and who created them creates challenges for banks and financial institutions. 

Among the top pain points: 

Operational Rule Challenges

Banks face significant operational difficulties in effectively managing a rules-based approach to fraud prevention. Many teams may hesitate to modify or remove some rules due to the risk of disrupting business operations. Without clear evidence of harm (e.g., lost revenue or fraud not being prevented), many rules remain untouched, with analysts unable or willing to review them. 

This hesitance to review, address, or adjust rules may leave outdated or unnecessary rules in place. For example, some rules may still work as intended but no longer align with current business needs. As such, they contribute to a high rate of false positives and unnecessary alerts.

Communication Gaps on Rule Upkeep

Maintenance of rules is just one part of the challenges. A lack of communication around who should own and adjust rules is another. If there is no process to gather analysts’ insights into rules regularly, oversight can become fragmented. This may lead to analysis paralysis when a rule needs to be updated or adjusted, especially rules that need closer review by human experts.

Lack of Oversight Hurts Rule Management

Strategic oversight is essential to proactive rule management. If banks do not have adequate visibility into the purpose of a rule or clear documentation about its specific purpose or outcomes, evaluating its performance is challenging. This is especially true of broad rules, such as rules that apply to high-value transactions or region-specific activity, that do not address nuanced scenarios.

Lack of Rule Ownership 

Finally, these challenges will snowball if no dedicated person or team oversees rule management. If no one is assigned ownership or optimization of rules, inefficiencies will persist because rules are not regularly audited or optimized.

6 Strategies for Strengthening Fraud Prevention with Rules-based AI

There’s a better way to manage your organization’s rules-based systems. Implementing the following strategies can enhance your rules and strengthen rules-based AI and machine learning models by making rules more straightforward to track and adjust.

1.Make ‘Set-It-And-Forget It’ a Thing of the Past

Rules are easy to create but often neglected once they’re in place. Overly broad, outdated rules linger, causing unnecessary alerts and blocking legitimate transactions. 

To avoid this, schedule periodic reviews—quarterly is a good starting point—to ensure each rule still serves its intended purpose after human intervention. Think of it as routine maintenance for your city’s infrastructure: essential to keeping the system functional, safe, and efficient.

2. Adopt a Rule Governance Framework

Due to a lack of documentation or historical context, teams often hesitate to disable legacy rules for fear of letting undetected fraud slip through. However, implementing a structured “rule governance” framework turns this real fear into an irrational worry.

I recommend that your rule governance documentation include each rule’s purpose and expected outcomes from the start, assigning an owner responsible for its upkeep, and scheduling periodic reviews. With clear records, you can confidently retire or adjust outdated rules without worrying you’ll miss hidden threats.

3. Open Feedback Loops With Your Analysts

A significant challenge is the disconnect between analysts who must handle alerts and those setting the rules. Without direct communication and feedback, organizations might address symptoms—like overwhelming alert volumes—by hiring more analysts rather than addressing underlying rule inefficiencies.

Empowering analysts to provide input on which rules create needless work can significantly reduce false positives and improve overall efficiency. It’s also a great way to keep the team connected and motivated—especially if you have remote or dispersed teams.

4. Focus on Quality Over Quantity

Many organizations measure success by how quickly alerts are closed, but that’s like a city judging its health only by how fast traffic moves. Consider quality metrics: How often are you catching genuine fraud? How many legitimate transactions get flagged? Balancing these metrics ensures that you’re not just closing alerts quickly—you’re closing the right ones. 

Aligning performance metrics with quality—such as measuring the ratio of genuine fraud caught versus legitimate transactions incorrectly blocked—ensures that rules reduce fraud and support a positive customer experience and healthy revenue flow.

5. Use Dynamic Lists and Attributes

Rules don’t exist in a vacuum. Dynamic list management—regularly updating which emails, cards, or addresses are blocked or allowed—keeps rules agile. These updates ensure that temporary fixes don’t become permanent barriers for good customers. Over time, this responsiveness maintains smooth traffic flow without building unnecessary roadblocks.

6. Promote Career Growth and Continuous Improvement

Involving analysts in refining rules helps them develop valuable analytical skills and fosters a culture of root-cause problem-solving. Over time, your team becomes more proactive, adapting rules to fit new threats rather than reacting to them. It’s like training city inspectors who know the building code and can suggest better, safer materials.

By pairing cutting-edge models with well-managed rules, you’re not just building a city but creating a thriving metropolis of safe, customer-friendly transactions. Additionally, you’ll give your analysts the tools they need to focus on critical tasks instead of manual processes. Your organization will be in a better position to stay ahead of the latest fraud threats, keep customers happy with fewer false positives, and merge two powerful assets—rules and models—into an effective fraud prevention operation that your team will appreciate.