You already have rules in place to combat fraud. The issue is most of these rules are designed to react to the latest fraud trends. In other words, they are only useful after the fraud has taken place. So while they may be key to preventing the fraud from spreading, the damage is already done in terms of fraudulent charges and customer friction.
However, what if your rules could prevent fraud before it hits your card portfolio? What if you could write rules that could see fraud coming months before it actually occurs? This is precisely what happens when a financial institution builds a well-thought-out rules writing framework that proactively leverages data to fight evolving fraud threats.
There are anywhere between 12-24 MILLION eCommerce websites currently processing card transactions on the internet every single day. Unfortunately, it is nearly impossible for the average consumer to detect which merchants are legit and which are high-risk. Therefore, it is your job to set up a rule-writing framework that identifies these high-risk merchants. This framework will block your members from making purchases that put them at risk.
Now, just as it is nearly impossible for a consumer to identify a high-risk merchant, it is equally difficult for a data analyst to do the same. This is due to the sheer amount of data they would have to manually pour through every week just to keep up. Being able to only analyze a limited amount of data means you are equally limited to the amount of fraud you can detect.
However, a solution like Rules Assist analyzes daily fraud trends from over 4,000 financial institutions through the power of machine learning. As a result, you are presented with a new list of high-risk merchants each week to write your rules around.
As mentioned above, a Data Analyst is limited in the amount of data analysis they can perform manually. Limited resources means a cap on the amount of data being reviewed, resulting in high-risk merchants slipping through the cracks. Automation is the key to leveling the playing field. When you automate this daily process, you save considerable time and significantly improve consistency in your rule writing.
The top benefit of automation is the daily reports it will provide. Each day, your data analysts will be supplied a report identifying key fraud statistics such as transactions, false positive ratios, and the merchants they occurred at. These reports can be segmented into fraud that happened in the last 7, 14, and 21 days. Instead of spending time analyzing, your team can quickly and efficiently write effective rules to prevent fraud proactively.
While responding to evolving fraud threats is crucial, it is only half the battle. An effective rule writing strategy must be built on a solid foundation on evergreen rules as well. Evergreen rules are written based on long-term behavior gathered throughout the year. A tool like Rules Assist supplements your decision rules by analyzing over 40 million card transactions a day and providing rules based on fraud trends in the market.
These evergreen rules can be implemented to prevent similar fraud from taking place at your institution. Since these rules are based on a more extensive set of data, they can be left in place for the long term. As a result, a typical financial institution will only need to implement between 10-25 evergreen rules and tweak them every quarter.
Adding a wall rule and 10-25 evergreen rules to your rule writing strategy provides a powerful way to proactively prevent fraud and continually respond to evolving fraud trends..
Since 2013, Rippleshot has delivered innovative solutions to your complex card fraud problems.
Rules Assist has helped financial institutions just like yours build comprehensive and effective rule writing strategies. Together, we can prevent fraud from impacting your customers.
If you are interested in learning how our solutions can help you in building your own rule writing infrastructure, please click the button below to schedule your demo.