Patriot Federal Credit Union, a member-focused financial institution, has partnered with Scienaptic AI to bolster its fraud detection capabilities, as announced on July 28, 2025. By integrating Scienaptic’s AI-powered platform, Patriot aims to tackle the rising threat of identity theft and synthetic identity fraud, ensuring a secure and seamless experience for its members.
Patriot Federal Credit Union adopts Scienaptic AI for real-time fraud detection.
Platform integrates fraud and loan underwriting with machine learning algorithms.
FraudShield reduces false positives while maintaining member experience.
Targets identity theft and synthetic fraud, a growing concern for credit unions.
Early detection yields significant savings in fraud losses for Scienaptic clients.
Partnership enhances secure, inclusive, and data-driven financial services.
Patriot Federal Credit Union has selected Scienaptic AI’s platform to enhance its ability to detect fraud in real time, addressing the increasing complexity of identity theft and synthetic identity fraud. “Fraud is fast-moving and increasingly complex, especially with synthetic identities becoming harder to spot,” said Tricia Wareham, Senior Vice President of Consumer Lending at Patriot. “We needed a solution that could act in real time without creating unnecessary friction. With FraudShield by Scienaptic AI, we’re proactively identifying threats, reducing false positives, and maintaining the seamless experience our members expect. It’s a critical step in modernizing our fraud defense.” The platform’s FraudShield leverages advanced machine learning and anomaly detection to identify threats early, minimizing financial losses.
Scienaptic’s platform integrates fraud detection with loan underwriting, aggregating data from multiple sources to create a robust signal for identifying fraudulent activities. This integrated engine employs cutting-edge machine learning algorithms to detect anomalies, enabling Patriot to approve more legitimate members while flagging potential fraud. The system’s ability to reduce false positives ensures minimal disruption to the member experience, aligning with Patriot’s commitment to member-focused financial services. Early adopters of Scienaptic’s platform have reported significant savings in fraud losses, highlighting its effectiveness in combating sophisticated fraud tactics.
FraudShield is designed to act swiftly without compromising the user experience. “FraudShield is designed to stop fraud before it starts, without slowing down genuine members,” said Eric Steinhoff, EVP, Client Impact at Scienaptic AI. “By using real-time AI and behavioral signals, our platform helps credit unions detect anomalies early, reduce operational strain, and confidently approve more members. This partnership with Patriot reflects our shared commitment to secure, inclusive, and data-driven financial services.” This approach ensures that Patriot can maintain operational efficiency while safeguarding its members from evolving fraud threats.
Scienaptic AI’s platform supports Patriot’s mission to provide inclusive financial services. By streamlining fraud detection and underwriting, the platform enables Patriot to serve its 80,000+ members across Pennsylvania, Maryland, and West Virginia more effectively. The partnership aligns with Scienaptic’s broader goal of enhancing credit access for underserved communities, with its platform processing over 3 million credit decisions monthly across 150+ lenders managing $3.9 trillion in assets.
Patriot Federal Credit Union’s adoption of Scienaptic AI’s FraudShield marks a significant step in modernizing its fraud prevention strategy. By leveraging real-time AI and anomaly detection, Patriot strengthens its defenses against identity theft and synthetic fraud, ensuring a secure and efficient experience for its members while supporting its mission of financial empowerment.
Founded in 2014, Scienaptic AI was built with the mission to drive financial inclusion at scale through AI-driven credit decisioning. The platform encapsulates a decade of technological innovation, integrating more data into decision-making, leveraging advanced machine learning algorithms, and supplementing them with rigorous risk and fair lending monitoring processes. This enables financial institutions to reach more borrowers—including underbanked and underserved individuals—and say “yes” more often without increasing risk.