The fight against fraud never ends. Every day fraudsters are finding new ways to infiltrate your card portfolio and charge illegal transactions on behalf of your customers. Since the threat never stops evolving, that means your tactics need to grow and advance as well. This is where machine learning and automation can be a game changer for your fraud operations
The reason so many financial institutions are turning to machine learning and automation is simple. It drastically improves the performance of their entire fraud team and strategy. Today, we will talk about how these two tools, when used in unison, will save your company time, money and keep up with the competition when it comes to fraud detection.
When fighting an enemy, you must think like them before defeating them. Fraudsters are leveraging the latest technology in a never-ending attempt to steal your customers' card numbers. Therefore, your team must be taking the same approach. Unfortunately, most fraud teams are still manually pulling and reviewing data to detect the most basic fraud patterns. Machine learning levels the playing field.
Unlike traditional analysis methods, machine learning can analyze millions of data points and thousands of variables in minutes. Not only can machine learning ingest large amounts of data but it does this consistently every day without taking a break. This approach allows it to detect more complex fraud patterns and create more effective rules to mitigate fraud. As a result, your team is provided with consistent, comprehensive analysis to help them stay ahead of the latest fraud threats before they occur.
Automating machine learning can do the heavy lifting and daily analysis that is currently swamping your fraud managers, giving them a significant amount of time and resources back. Instead of spending valuable hours manually combing through data, they can shift their attention to more pressing matters and use the data to be more proactive with their existing fraud strategy.
The best way to detect fraud trends is to review daily transactions for patterns. When you automate this process, your team can spend less time analyzing the data and more time strategizing how to best use it. The right automation tool will deliver a daily report highlighting the latest high-risk merchants and fraud over the last 7, 14, or 21 days. Faster data delivery means your team can quickly write and implement the rules needed to prevent future fraud.
Machine learning and automation are all about enhancing the fraud-fighting strategy you already have in place. They should be thought of as weapons for your data analysts to make the daily battle against fraudsters a bit easier. When combining these two tactics, you get a solution that provides a consistent approach to fraud detection and rule writing.
Currently, many financial institutions have limited insights into how well (or not well) their current rules are performing. Automation allows you to pull back the curtain and determine what works well and needs to be adjusted. As a result, your team will implement the most optimal rules possible. Together, this combination will help you stay on top of evolving fraud threats and analyze significantly more data without having to increase the size of your fraud team.
Since 2013, Rippleshot has been leveraging the power of machine learning and automation to protect your customers from card fraud.
Rules Assist works with financial institutions like yours to build comprehensive and effective rule writing strategies. Together, we can prevent fraud from damaging both your customer relationships and brand reputation.
To learn more about how machine learning and automation can solve your fraud challenges, please click the button below.