How AI-Driven Fraud Prevention Is Revamping Credit Union Strategy in Small Business Lending
- Will Tumulty
- 2 minutes ago
- 3 min read
Guest Editorial by Will Tumulty, CEO, Rapid Finance
As more credit unions expand into small business (SMB) lending to capture a share of the $1.4 trillion market, they face the rapidly growing threat of fraud. The industry’s move toward digital and remote underwriting has opened the door to new, increasingly sophisticated fraud schemes, like synthetic-identity fraud and loan-stacking attacks. If left unchecked, fraud losses may erode profitability and thwart growth altogether.
For credit union leaders, embracing AI-driven fraud prevention has become table stakes for institutions that want to scale their SMB portfolio without sacrificing risk posture, member trust or long-term viability.
Recent data shows that 65% of financial institutions reported increased fraud in 2024. Of those surveyed, nearly half of SMB loan applications exhibited signs of first-party fraud. It’s not just these institutions, though, as analysts estimate that AI-driven scams could result in as much as $40 billion in lending losses by 2027.
Furthermore, around 80% of fraud now originates through digital channels, creating opportunity for automated, high-volume attacks that can exploit gaps in manual underwriting workflows.
Traditional fraud defenses often fail to catch up, from manual reviews that are slow and error-prone, to siloed legacy systems lacking the data integration required to flag synthetic identities or organized fraud rings. Many consumer-focused tools simply weren’t built for the complexity of SMB lending, where multi-entity ownership, layered credit applications and mixed data sources complicate risk evaluation.
Credit unions that rely solely on these outdated defenses risk having their growth ambitions undermined by escalating, undetected fraud. However, AI-based fraud prevention can empower credit unions and bolster detection at scale to enable risk-informed growth, shifting from reactive to proactive.
Instead of relying solely on fixed rules, AI systems can analyze vast amounts of data in real time, identifying patterns and anomalies that may indicate fraudulent activity. These systems continuously learn from new data, improving their ability to distinguish between legitimate and suspicious transactions. By integrating multiple data sources, AI can detect subtle signals that might be invisible to human analysts, from unusual spending patterns to inconsistencies in application information.
The advantages of AI for credit unions are both operational and strategic. On the operational side, AI can streamline processes that previously required extensive manual effort. For example, automated systems can rapidly evaluate loan applications, flag potentially fraudulent cases for review and reduce false positives that burden compliance teams. This efficiency allows staff to focus on higher-value activities, such as member engagement and relationship-building.
From a strategic perspective, AI-driven fraud prevention equips credit unions to offer their members faster, more secure services, enhancing trust and loyalty. In an environment where member experience is increasingly tied to digital offerings, the ability to prevent fraud proactively while maintaining seamless service positions a credit union as a reliable and technologically adept institution.
For leadership teams evaluating their next-gen strategy, the question is no longer about if, but rather, when and how to embed AI into fraud defense and underwriting infrastructure, and there are several practical considerations for implementing AI-based fraud prevention:
Data Quality and Integration AI systems are only as effective as the data they analyze, so credit unions must ensure access to comprehensive, accurate and up-to-date information from both internal and external sources.
Balancing Automation and Human Oversight While AI can significantly reduce manual workload, human judgment remains critical for evaluating complex or ambiguous cases. Institutions should establish processes that integrate automated alerts with expert review.
Continuous Learning and Adaptation
Fraud tactics evolve quickly, and AI models must be updated and trained regularly to remain effective. This requires a commitment to ongoing monitoring, model refinement and investment in staff capabilities.
By thoughtfully adopting AI-driven solutions, credit unions can strengthen their defenses against fraud, improve operational efficiency and enhance the member experience. Rather than viewing AI as a replacement for human expertise, it should be approached as a force multiplier that amplifies the effectiveness of existing teams. In doing so, credit unions can navigate a challenging financial environment with confidence, protect their members and create a tangible strategic advantage in an increasingly competitive market.
Will Tumulty is CEO of Rapid Finance, a leading provider of small business financing and enterprise lending solutions.