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In an era of evolving card fraud threats and data breaches, credit union leaders are constantly looking at ways to protect themselves, and their members. One of the biggest problems credit unions face today is relying on traditional network alerts, which can be costly in terms of fraud loss and member experience. Reducing the time from when an incident occurs to when it’s detected is critical in containing the spread of fraud threats.

Mitigating risk and serving your members starts with determining how sophisticated machine learning and data analytics tools can be used to identify and act on fraud sooner. These tools provide your fraud team with actionable insights into the greatest risks across your card portfolio — and how to prioritize them.

Why Today’s Threats Call for Better Data-Driven Tools

Data breaches and emerging fraud tactics are ramping at new levels, which means your members are at greater risk than ever before. This is especially true as fraud threats focus on PII information. To combat these growing threats, a credit union leader must have the tools to know what their fraud prevention performance looks like on a daily basis.

Rippleshot’s research indicates that early breach detection can stop 60% of fraud losses. Machine learning technology that relies on data pattern analysis empowers a credit union leader to better understand relationships and trends as leading indicators of certain outcomes.

Too often, by the time information about fraud or a breach is realized, the amount of fraud loss and compromised card fraud has already reached high levels. Credit union leaders should consider employing machine learning-driven data algorithms to identify how to achieve better results for themselves and their members. Of course, there are a few key issues that often prevent credit unions from adequately addressing this problem.

  • Lacking Access to Adequate Big Data: Most credit unions don’t have enough data to fight fraud effectively as it is not easily accessible. It’s important to ask more from your processors.
  • Privacy Challenges: Consumer and personal identifiable information is very sensitive to financial institutions.
  • Lacking Resources: Credit unions can be strapped for IT resources, making technology implementation challenging.
  • Making Data Actionable: Data analytics need to be clearly actionable and deliver measurable ROI results.
  • Keeping Pace with Algorithms: Fraud and fraud patterns evolve and change more rapidly than financial institutions can keep pace.


How Machine Learning and Big Data Addresses These Gaps

Fraud and fraud patterns are evolving so rapidly that financial institution leaders are challenged to keep up with the pace of change. Better data analytics tools that rely on machine learning bridge the gap for fraud teams by helping them detect incidents faster, and at their source. The very nature of machine learning is to use the data it is processing and adapt to changing trends or relationships identified by that data. Detecting and mitigating fraud to manage risk involves in-depth data analysis to understand relationships and trends, and to pinpoint where and when the fraud originated. Fraud teams can no longer rely on manual analysis of network alerts to get the job done.

Relationships and trends are becoming leading indicators of outcomes (like fraud). As these leading indicators emerge in new data, outcomes can be predicted and acted upon. A data analytics approach equips issuers with the tools to understand what’s happening across their own card portfolio — and how to detect risk. But you need access to that data — and be able to make sense of it all. Applying sophisticated data analysis, machine learning and algorithms to identify relationships and trends in transaction and fraud data can be used to proactively identify leading indicators of certain outcomes (e.g., How far fraud might have spread, or will potentially spread).

As financial institutions continue down their digitization transformation — and invest in innovative technology — this opens the floodgates for more touch points for fraudsters to breach. Thanks to machine learning, the digitization of data and artificial intelligence, credit union leaders have access to the infrastructure and industry-leading tools necessary to fight fraud — if they’re willing to invest money where it counts.

Canh Tran | Co-Founder and CEO | Rippleshot
Rippleshot is a portfolio company of CMFG Ventures, a subsidiary of CUNA Mutual Group. To learn more about Rippleshot and other Ventures investments, go to
www.cmfgventures.com or contact your CUNA Mutual Group Sales Executive at 1-800-356-2644.