A new report from the U.S. Department of the Treasury pinpoints the significant opportunities AI presents for financial services in addressing fraud challenges. This supports what Rippleshot has been working with FIs to develop for more than a decade. We’ve put together a few thoughts on the latest report.

➡️ Addressing the growing capability gap: Larger FIs are developing in-house systems. Smaller FIs that we talk with don’t have the data resources to develop or train large language models. It’s essential to quickly integrate third-party solutions to close this capability gap.

➡️ Narrowing the fraud data divide: Gaps exist for FIs who aren’t able to invest the time, resources or people to derive actionable insights from droves of transactional data, particularly as it relates to fraud prevention. Access to a team of card fraud experts and robust consortium data with historical context addresses this divide.

➡️ Regulatory coordination: FIs must collaborate with fraud and data experts who can advocate how AI fraud analytics can enhance fraud protection to support regulatory requirements.

➡️ Explainability for black box AI solutions: FIs must work with data scientists and fraud and data experts who understand the implications of how advanced machine learning models can measurably transform their fraud management practices.

➡️Gaps in human capital: Homegrown spreadsheets and manual analysis are too reactive for today’s fraud environment. Automated AI and ML-driven fraud mitigation tools put FIs in control to proactively stop fraud risks before they escalate.

➡️ A need for a common AI lexicon: All AI solutions are not created equal. FIs need someone who can help understand this complex space. FIs need to understand  how AI, ML and automated decision rules can be developed to fit their specific fraud needs.

➡️ Untangling digital identity solutions: Fraud solutions must be able to detect pattern anomalies to determine the difference between bad actors and legitimate customers. FIs need to work with a partner who can help proactively identify block fraud from occurring.

Read the full report here.

What Can Banks and Credit Unions do to Bolster their Fraud Protection?

Recent research from PYMNTS indicates nearly half of FIs are turning to deep learning systems to fight fraud. The graphic below shows how investments in AI and ML are becoming a competitive differentiator and a revenue-driver.

As fraud evolves, fraud and risk managers are adapting. Their FIs must follow suit. To do so they need automated data analysis, enhanced card risk visibility and faster risk detection. Predictive analytics is where FIs of all sizes are gaining their edge.

We talk with fraud managers every day from banks and credit unions of all sizes about their unique fraud pain points. These conversations are why we develop custom fraud solutions around specific use cases for FIs with different needs.

If you’re evaluating where AI and ML fit into your risk mitigation and fraud prevention strategies, consider the following:

✅ Do you have quality data? All consortiums are not built the same. Automating data analysis is only valuable when based on high quality, comprehensive data.

✅ Does your solution adapt with your needs? Every FI has different fraud pain points; find a partner that can optimize a solution for your card portfolio.

✅ Do you have gaps in your fraud management? You’re far from alone. Evaluate your resources, identify gaps and pinpoint analysis to automate.

✅ Does predictive analytics support your fraud goals? Find an internal champion who can help you advocate for streamlined risk and fraud analysis.

If you can't answer these questions, we're here to help.