What are we talking about?

Card fraud is a threat that financial institutions have to consistently battle. However, what is new is how and where the fraud takes place. Fraudulent charges are rapidly evolving in both origin and occurrence. Bottom line: Even the most effective decision rules have a shelf life.

Writing rules is no longer just about evergreen rules, a set of core rules that are stable and prevent every day fraud, that you set and forget. Your rules need to evolve faster than the fraud it is trying to prevent. In order to avoid a major hit in both  losses and customer loyalty, your rule writing process must keep up with the latest fraud threats by leveraging the power of fraud intelligence and machine learning.

Align With Current Fraud Trends

Unfortunately, most fraud management happens after a data breach or illegal transaction has taken place. This means you are scrambling to limit the damage and control the fallout amongst your customers.  One way to shift from a reactive stance to a proactive approach is to implement decision rules that align with current fraud trends.

Fraud trends include increased activity in specific regions, merchant category codes, suspicious merchants, or dollar ranges. The right automated rule writing tool uses the power of machine learning to analyze millions of data points in a short period of time. These insights allow you to identify fraud threats before they infiltrate your card portfolio. As a result, your institution's reputation and customer relationships remain intact. 

Measure The Effectiveness of Your Rules

Financial institutions spend a considerable amount of time and resources to write rules to prevent card fraud. However, most fraud analysts will admit they have limited insight into how those rules perform. Essentially, they spend valuable time manually writing and implementing safeguards  and may not have the time or resources to measure their fraud prevention success rate.

For example, how well is that rule performing in terms of fraud capture and FPR?  Is the effectiveness of the rule decreasing over time? If so, should it be replaced? Are there specific rules that overlap and should be combined to increase efficiency? Is my portfolio being protected by the most optimal, data-driven rules available?

This is where machine learning and automation come into play. An automated solution not only writes rules based on relevant data but then goes to work reviewing their success rate. If they are not performing well, it will automatically make adjustments. Conversely, if they perform well, the system uses that data to influence future rule writing decisions.

Decrease Your False Positive Ratio

Having your card declined and purchase interrupted is aggravating to a consumer. However, it is understandable if it protects them from a high-risk merchant. What is highly less acceptable is having your card declined during a perfectly legal transaction. This is often the result of poorly written decision rules that produce a high rate of false-positive transactions.

No rule writing solution will get it right 100% of the time. Unfortunately, false positives are the price of doing business in a world with ever-changing fraud threats. However, an automation tool like Rules Assist leverages  transaction data and fraud trends from over 4,500 financial institutions to decrease your FPR significantly. Once again, you can implement smart, data-driven rules to keep your customers safe without having to impact their daily lives. 

About Rippleshot and Rules Assist

Since 2013, Rippleshot has delivered innovative solutions to your complex card fraud problems.

Rules Assist is our signature product that taps into the power of automation and machine learning to analyze data and trends to write the types of rules you need to fight fraud proactively.

If you are interested in learning how our solutions can keep your institution and its customers safe and secure, please click here to book a free demo.