AI’s Role in Helping to Detect and Prevent Fraud

Fraud rates continue to rise as bad actors employ advanced technologies and sophisticated tactics, with 60% of financial institutions (FIs) reporting an increase in fraud events in 2024.¹

In response to this, projections for 2025 indicate a shift in fraud prevention strategies, with an overwhelming 85% of U.S. FIs projected to integrate AI technologies into their anti-fraud operations.² This widespread adoption signifies a pivotal moment in the ongoing battle against financial crime.

How AI helps with fraud prevention

AI efficiently improves fraud systems by analyzing patterns, behaviors, and transactions automatically and at scale, all while helping to reduce friction for cardholders. The following four use cases demonstrate how AI can be leveraged in fraud prevention:

• Enhanced anomaly detection: AI algorithms can analyze large amounts of transaction data to identify patterns and anomalies with greater accuracy and speed than human analysts or traditional rule-based systems. These systems continuously learn from new data, improving their ability to detect emerging fraud tactics and subtle deviations from normal behavior over time.³
• Behavioral biometrics: AI-driven behavioral biometrics create unique digital signatures for each user by continuously learning from their interactions with devices. By creating a behavioral baseline, AI can swiftly detect anomalies that may indicate fraudulent activity, such as sudden changes in interaction patterns or unusual device handling.³
• Real-time fraud scoring: AI models transform real-time scoring by rapidly analyzing a vast array of variables and their intricate relationships. This dynamic scoring allows for more nuanced decision-making, reducing false positives and minimizing friction for cardholders. AI-powered fraud scoring systems can quickly adapt to new fraud patterns, ensuring that risk assessments remain accurate even as fraudsters change their tactics.³
• Synthetic identity Detection: AI-driven synthetic identity detection can proactively identify potential threats in the onboarding process, allowing FIs to catch fraud attacks before they occur. These AI algorithms can analyze robust datasets to identify patterns indicative of synthetic identities, allowing these systems to detect subtle inconsistencies and connections that may not be apparent to humans. ³

In keeping with this trend, TransFund’s Defender equips FIs with near real-time transaction monitoring and predictive alerts, strengthening capabilities like anomaly detection, dynamic fraud scoring, and synthetic identity detection. This proactive approach helps FIs intercept fraud before it unfolds— enabling them to effectively combat evolving threats such as Card Not Present (CNP)/online fraud, account takeovers, and synthetic identities.

Similarly, TransFund further supports its clients by working with network partners to potentially provide AI-driven fraud detection tools as needed.

Challenges and considerations

Although the benefits of leveraging AI in fraud prevention are notable, FIs should also work to address potential challenges:

• Model explainability: Many AI fraud detection systems operate as “black boxes,” that are difficult to explain to regulators or cardholders. ⁴ This lack of explainability can lead to an inability to stress test false positives or negatives, operational inefficiencies, and potential attrition. Transparent models are critical for maintaining trust and meeting regulatory requirements.⁴
• Data privacy: AI-driven fraud detection requires processing vast amounts of sensitive personal and financial data, raising concerns about potential data breaches and misuse. FIs must balance the need for detailed analytics with privacy regulations. This requires secure data collection, storage, sharing practices, and transparency. Additionally, cardholders should have the ability to opt out of certain services.⁵

Conclusion

FIs are increasingly turning to AI-driven fraud prevention and detection as fraudsters grow more sophisticated. While AI offers transformative capabilities – ranging from enhanced anomaly detection to behavioral biometrics – its successful deployment hinges on addressing critical challenges such as transparency and privacy. That said, FIs can strengthen their defenses by leveraging TransFund’s fraud suite, which employs advanced technologies like predictive alerts, to stay ahead of evolving threats. The path forward requires a balanced approach: harnessing AI’s power while maintaining oversight to ensure ethical and effective fraud mitigation.

If you’re a current TransFund client and would like to reach the Fraud Services team, please contact TTFraud@transfund.com. Not a current TransFund client? Click here to connect with us.

Sources

1. Alloy, 2025 State of Fraud Report,
2. ArtSmart AI, AI in Finance:40+ Statistics You Need to Know in 2025, 2024
3. Datos Insights, Digital Defense: Top Fraud Threats and Strategic Investments in Banking, 2025
4. Datos Insights, Interpreting the Black Box: Why Explainable AI is Critical for Fraud Detection, 2025
5. Cyber Defense Magazine, AI-Powered Fraud Detection Systems for Enhanced Cybersecurity, 2024
6. TelIntelligence, AI in Fraud Detection and Due Diligence: Top 8 Ethical Implications, 2025