Fraud analysts and managers are the top line of defense against evolving fraud threats, causing them to spend countless hours fine tuning fraud rule writing and conducting analysis on these efforts.
They’re tasked with mitigating incidents, reducing fraud losses and false declines — all without disrupting cardholders. They wear a lot of hats, and often lack access to enough data or tools to know if their efforts are actually effective.
At smaller financial institutions, fraud analysts and managers often have a small team, or sometimes are a “team” of one. The fraud rule writing processes they manage are tedious, time-consuming and hard to judge if they are actually effective. Frauds can be easily overlooked without the right tools in place. We're here to share the common pain points we hear about fraud rule writing and how you can assess your fraud mitigation efforts.
Common Fraud Detection Rule Writing Pain Points
We hear a lot of common pain points about fraud rule writing. Fraud analysts say they don’t know what fraud risks are coming your way, and they worry about the high-dollar events they can’t see coming. The process takes too long and they often miss too much fraud until after it happens. Many can’t do my job without an entire team of fraud analysts, which makes the process even more expensive.
Traditional fraud rule writing can look a lot like this.
The analyst pulls up recent frauds and hopes to spot a pattern, often worrying what they are missing. Manually spotting patterns is a tall task for even the most advanced fraud analyst. They must compare against the non-fraud transactions to ensure you don’t exceed your FPR threshold.
If this first attempt fails, they have to add another feature to the rule, or start over and look for another pattern. Hours later, there might not be anything to show for all that time and effort.
Meanwhile, there’s the fear of the unknown about fraud patterns existing outside the scope of your data ready to hit the FI. Sound familiar? This burdensome process doesn’t have to be the only recourse.
Checklist: Overcoming Fraud Detection Rule Writing Hurdles
Fraud analysts and managers often struggle to adapt quick enough to emerging fraud risks due to lack of tools, access to enough data, and simply enough hours in the day. Call centers are juggling priorities - creating organizational silos and poor intel into where the greatest fraud risks exist. Below are 5 questions you should be asking yourself as you continue to develop your fraud rule writing processes.
1. Do you have evidence a rule is working?
Effective fraud rule writing is built on data-driven intel that quickly confirms where fraud is occurring, or where that fraud might occur. Access to more and better data will help you understand if a rule is working, and if your analysis is catching all your fraud and potential fraud.
2. Do you have Data-driven Fraud Intel to provide direction on what rules to write?
Fraud rule writing should be accompanied by analytics that precisely pinpoint the frauds you should be writing rules for to achieve your fraud reduction goals. Access to the right types of data is critical.
3. Do you have Access to automated Fraud Analytics To Help Your Analysis Process?
Manual rule writing processes miss data gaps that automated approaches achieve to validate your efforts. Rules analysis is a powerful tool, but only when automated by sophisticated machine learning technology.
4. Are you relying on a proactive or reactive strategy?
Many fraud rule writing processes are responsive to fraud that has already happened or is happening at that moment. Effective fraud rule writing comes when fraud patterns are spotted before the fraud hits your institution, not after the fraud has occurred.
5. Do you have access to your data?Many smaller financial institution’s data is siloed across multiple systems and leaves fraud analysts and managers responding to fraud intel from their call centers and processors. Knowing how to access your data, and how to quickly and accurately make sense of it all matters for understanding true fraud risk.
From Proactive to Reactive: Achieving Better Fraud Detection Rule Writing
Many fraud rules writing processes are filled with numerous pain points: The process takes too long and involves a manual, intensive trial and error work. Many fraud teams don’t have visibility beyond their own institution’s data, or have little to no access to data at all. This leads to a frustrating process that’s more likely to create more fraud spikes and high FPRs.
Rippleshot addresses these pain points with Rules assist by providing an automated fraud analytics solution that uses machine learning to precisely guide teams to the right rules to identify merchants on which to write rules. The solution delivers comprehensive analytics to identify fraud trends, give insights on fraud trends that are coming and identify where data gaps exist in order to detect and stop more fraud.
Rules Assist takes away the drudgery and the hit-or-miss aspects of searching for patterns and to give you concise and comprehensive lists of merchants or acquirers that are causing problems, locally, regionally and not just local to you, but nearby and nationally. The end result of this proactive fraud rule writing process is less effort, and greater fraud reduction impact.