Payment fraud is exploding. So are false positives, customer friction and investigation costs. Unfortunately, as customers continue to pull us down the river of rapid digital transformation, traditional fraud detection systems are being left in the sand.
Many traditional fraud detection systems rely on the interpretation of past payment patterns to assign risk scores and flag suspicious payment transactions. The problem is, what was once deemed to be “unusual account or card activity” is continuing to increase and become mainstream behavior. Many of the machine learning models were not built to process real-time data, or adapt to changes on an individual customer “event-by-event” basis. This means these models are lagging behind in analysis and spitting out way too many false positives. The result is a negative impact on not only profitability and customer experience, but also resource workload and fraud investigation speeds.
When it comes to reducing the impact of false positives on your workload and fraud investigation speeds, you may want to ask yourself:
- Are we experiencing a lot of white noise from over active fraud alerts?
- Are our false positive rates too high due to ill-defined risk scores?
- Are our fraud detection and investigation processes taking too long?
If you answer yes to any of these questions, then it may be time to re-evaluate the accuracy and adaptability of your fraud alerts, risk scoring models and case management workflows.
Reducing the number of false positives
To drive down your false positives and improve the efficiency of your fraud investigations, you need to take the speed and quality of your data points seriously. The more in depth your data is, the more accurate and precise your fraud alerting and risk scoring configurations for each individual customer can be. The faster your data flow is, the quicker your fraud rules and models can re-adjust to the perceived level of risk for each event. In summary, the more you focus on the speed and quality of your transaction data, the better your ability to decrease white noise of overactive fraud alerts, improve the accuracy of transaction flagging and establish proactive tactics around emerging payment fraud attacks.
Speeding up triage and closing cases faster
In addition to reducing false positives, case management best practices can also greatly impact investigation costs and speed. Workflows should be designed to help streamline and automate the payment fraud triage process, increasing operational efficiency and minimizing room for errors. Audit trails contain valuable historical information on which risk factors are deemed suspicious and why, making it easy for your fraud investigation team to speed up the triage process and reduce the average mean-time-to-detect by 65-75%.
So instead of spending hours sifting through thousands of false positives, we encourage you to spend 5 minutes reviewing episode 7 of the “1-2-3 Detect: Tips to Get Your Payment Fraud Strategy In Top Shape”. This mini webinar discusses how INETCO’s real-time fraud alerts and case management capabilities can help you keep up with adjusting customer behaviors, spot emerging new fraud patterns, and flag and block transactions with more precision – while reducing investigation costs and improving operational efficiency.