ABA Banking Journal: Rippleshot CEO on Combating Emerging Fraud Trends

The push for greater digital transformation across the financial services market isn't just changing the face of the front-end customer experience, it's rapidly-impacting how banks invest in back-end technologies that detect and mitigate emerging fraud threats.

The ABA Banking Journal's Risk and Compliance team recently interviewed Rippleshot's CEO Canh Tran about the greatest fraud threats impacting financial institutions in 2020, and how fraud teams can  proactively mitigate these risks.

“Today, the real trend for both fraudsters and bank fraud managers is the use of technology to be more effective and efficient,” Tran said, “Digital transformation, data aggregation, machine learning, predictive algorithms, and cloud computing to be more effective—and unfortunately the fraudsters are more advanced.”

Bottom line? As banks get smarter, so do fraudsters.

Emerging Fraud Trends: How Financial Institutions Can Proactively Combat Threats

The Rippleshot team has gathered actionable tips for financial institution leaders to consider as they weigh what solutions are needed to combat rising fraud costs as new trends enter the market. This is particularly useful for smaller financial institutions as fraudsters are being driven down the value chains to go after small and mid sized banks that often have less sophisticated tools and processes to combat these types of fraud schemes.

Rely On Streamlined Data-Driven Solutions

Banks must demand more in their card fraud compromise detection solutions. Risk mitigation solutions must be able to streamline the data feed process, automate the analytics process, offer continuous model refresh, and deliver actionable results within hours — and continue to do so on a daily basis. For instance, machine learning-driven solutions for automating the tasks of gathering and checking data, detecting fraud, and validating the results.

Understand How CNP Fraud Trends Are Evolving

Traditional card fraud is quickly shifting to new, digital channels. For example, card-present and counterfeit fraud is down, but bank losses from card-not-present continues to rise sharply as there are more touch points for card data to be stolen from and later monetized on the dark web. Digital wallet fraud is another trend that is increasing as fraudsters have found new methods to exploit gaps in technology. Other trends like new account fraud and Synthetic ID fraud are continuing to gain attention as the volume of exposed PII rises.

Focus On Earlier Detection

The key to any risk mitigation approach is early detection. This can drastically reduce losses. Reactive strategies after a fraud incident has occurred is costly to banks and their customers. Machine learning and big data analysis tools better arm bank leaders with the insight necessary to understand the relationships and trends in fraud analysis that allows for a proactive approach to curbing fraud impact and losses.

Leverage Technology That Relies On Pattern Analysis

Many community banks and credit union are simply relying on tools that don’t spot fraud fast enough, and lack the ability to spot patterns to predict what the impact/fallout is from a fraudulent incident. Big banks are proactively working to get ahead of fraudsters with investments in predictive AI fraud mitigation tools. Unfortunately, many smaller banks are still relying on time-consuming, manual methods to spot fraud patterns — or count on their call centers to alert them when fraud occurs. This approach involves a lot of upfront time, money and analysis, only to fall short in being able to accurately pinpoint where a bank’s biggest risks exist and how to quickly address them.

Read more from the ABA Banking Journal

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