Rule-based fraud detection effectively allows you to set up filters to look for potential cases of fraud. When creating the rules, you use correlation and statistics to determine the transactions that are the most likely to pose a threat. But to be effective, you need to handle this process carefully.
Here are tips on how to optimize a rule-based approach to fraud detection and prevention.
1. Focus on the most significant risks
When determining the rules to use, you need to focus on the most significant risks. Look at your financial institution’s history of fraud and determine what actions are the most likely to signify fraud.
For example, a transaction over a certain amount on a new bank account may be a red flag for fraud. By extension, you need to create a rule that detects that potential risk.
2. Consult with fraud management experts
Don’t necessarily create your rules on your own. Consult with fraud experts. You understand the most significant risks or the history of fraud at your financial institution. But an outside expert can help you contextualize the risks and understand more about new and emergent risks.
3. Improvise as needed
If your fraud detection rules aren’t working, change them. Look at the fraud you’re missing and adjust your rules so that they will catch similar incidents in the future. Rule-based fraud detection systems are based on static rules, meaning they don’t change on their own. But you can still change the rules manually.
4. Don’t advertise your fraud detection rules
Both employees and customers may get to know your fraud detection rules, and then, they can use this information if they decide to commit fraud. People who know your rules can easily commit fraud that they know won’t be detected.
For example, if they know that transactions over a certain amount get flagged for potential fraud, they will focus on transactions under that threshold.
Protect your financial institution by only allowing a very small number of people to know your controls. Limit access to this type of information as much as possible.
5. Change your rules regularly
To reduce the risk of people circumnavigating your rules, you may want to change them on a regular basis. In particular, if someone who knows the rules leaves your organization, you should make sure to change the rules. This is similar to changing codes or locks when someone leaves your organization.
6. Track changes to the rules
Make sure you understand who has access to your anti-fraud dashboards and administrative controls. Whenever possible, track all changes to the rules.
If you don’t track changes, an employee could easily manipulate the rules so their fraudulent transaction doesn’t get detected. Then, they could change the rules back to avoid getting noticed. If you track all changes to your fraud detection rules, you can spot these incidents more easily.
7. Supplement rule-based fraud detection with machine learning
Unfortunately, rule-based fraud detection is not that accurate. It can miss a lot of cases of fraud, and as indicated above, these systems can be relatively easy for employees, customers, and bad actors to manipulate.
To truly optimize your rule-based fraud detection solution, you should complement it with a machine learning solution. These fraud detection-and-prevention tools use algorithms to improve their ability to detect fraud over time. They provide an additional layer of defense that can detect issues missed by your rules.
Machine learning solutions get to know the patterns of legitimate and fraudulent transactions, and they also learn the typical behavior of your customers. This technology allows these systems to detect fraud more accurately than rules on their own, and machine learning also produces a lower number of false positives than static rules.
At SQN Banking Systems, we offer fraud detection and prevention tools and solutions that go beyond static rule-based detection. We leverage machine learning in our solutions, and we would love to help you reduce fraud at your financial institution. Ready to learn more? Then, contact us today.