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AI is increasingly being used by credit unions to improve fraud detection and prevention capabilities. Kathy Stares, EVP for Provenir, believes that AI can help optimize processes and improve service delivery in credit unions.
If artificial intelligence (AI) is properly deployed, it improves credit unions’ fraud decisioning capabilities, Provenir’s EVP for North America Kathy Stares believes. Fraud detection and prevention is a top priority for credit unions as they consider how AI can improve service delivery, likely because fraud hits them hard. According to a recent survey, 79% of credit unions and community banks reported more than $500,000 in direct fraud losses, more than any other segment. According to Juniper Research, businesses across the globe will spend more than $10 billion every year on AI-enabled financial fraud detection and prevention strategy platforms in 2027. That is a more than 50% increase from 2022.
Stares said predictive AI enables financial organizations to optimize business processes. That frees up resources and fosters a more focused approach to fraud. AI can process millions of attributes beyond human capability to deliver predictive capability effective in fraud modeling across the customer life cycle. Credit unions have unique AI considerations. By design, credit unions can attract different fraud types. Their branch and membership design lends itself to first-party and identity fraud. That also attracts social engineering scams. As they integrate digital fraud prevention solutions, credit unions must maintain high trust with their localized base. Systems must decrease false positives and allow legitimate clients to transact seamlessly.
AI also plays a role as credit unions consolidate and gain scale. Stares said it is essential for AI to be connected to all relevant databases, considering false positives and looking at everything in totality. Data is key. Develop proficiency in injecting data and then using AI to quickly detect fraud. While Generative AI is generating more buzz, institutions are wise to first consider predictive AI. Stares said it can help test the effectiveness of different fraud detection models. Which ones create more false positives, for example? AI-based models also learn from their mistakes and improve over time.
