AI vs AI: The New Fraud Battleground

When the fraud team at the University of Hawai’i FCU finally connected the dots, they realized they’d been watching a masterclass in modern fraud unfold right before their eyes. For months, they had been approving what seemed like legitimate applications with solid scores in the 730s and 750s.

Sarah Mitchell lived at 1247 Oak Street, Apartment 3B. Michael Thompson lived at 1247 Oak Street, Apartment 4A. Jennifer Davis lived at 1247 Oak Street, Apartment 2C. All had different names, though some shared similar last names. All had pristine credit scores. All were self-employed with 1099s to prove their income.

All had started with small loans, then returned months later, requesting much larger amounts after establishing perfect payment histories.

And all turned out to be fraudulent.

When investigators dug deeper, they discovered that Sarah Mitchell, Michael Thompson, and Jennifer Davis either didn’t exist or had never applied for these loans. The businesses listed on their 1099s were nowhere to be found. The credit reports, despite looking legitimate with scores of 736, 757, and 750, were sophisticated fabrications.

What made this particularly unsettling was how the fraudsters had gamed the system’s logic. They understood that credit unions reward loyalty and good payment behavior. So they built relationships first, earned trust with smaller loans, then struck with larger amounts once they’d established credibility.

Welcome to fraud in 2025. It doesn’t look like the fraud we’re all accustomed to.

Credit Unions Under Siege

The numbers tell a sobering story.

79% of credit unions experienced more than $500,000 in direct fraud losses in 2023 — higher than any other type of financial institution (Alloy 2024 State of Fraud Report)

Credit union fraud rates increased by more than 70% in 2022 (Pindrop Voice Intelligence Report)

33% of credit unions say scam cases skyrocketed by 50–100% just in the past year (PYMNTS Intelligence 2025)

Why are credit unions getting hit so hard? The answer lies in what makes them special: their commitment to serving members and their willingness to lend to people that big banks often reject. Unfortunately, these same qualities make them attractive targets for AI-powered fraudsters.

The AI Problem

Fraudsters are now using generative AI to automate the creation of synthetic identities. They can generate fake social media histories, create convincing employment records, and manufacture digital footprints that span years. AI can learn from its mistakes and churn out more of what works, creating identities so convincing they fool both automated systems and human reviewers.

Most credit union risk teams still think about fraud through the lens of traditional identity theft: someone steals a member’s identity and applies for credit. But AI has changed the game completely:

Traditional Identity Theft: Still happens, it’s what your staff are trained to catch.

First-Party Synthetic Fraud: Real people using their actual information but using made-up Social Security numbers.

Third-Party Synthetic Fraud: Completely fabricated identities blending real SSNs (often from children or the elderly) with fake names and addresses. According to the Justice Department, this is one of the fastest-growing forms of identity theft in the United States.

Bust-Outs: Perfect credit histories built over years, then maxed out and abandoned.

Credit Washing: Manipulation of credit reports to remove negative marks, often AI-assisted.

Assumed Identity Abuse: Using identities of people who moved abroad and would otherwise be credit inactive.

Bot Attacks: Automated systems that probe for weaknesses in underwriting models.

Fighting AI with AI

Here’s the reality: You can’t fight AI-powered fraud with traditional tools. Credit scores, basic identity verification, and device tracking work well against the most obvious fraud attempts. But those tools struggle with AI-generated schemes because this fraud is specifically designed to bypass traditional fraud flags.

AI approaches fraud detection differently: Instead of looking for what’s wrong, it learns what’s right. It analyzes thousands of legitimate applications to understand standard patterns, then flags anything that deviates, even in subtle ways.

Real-Time Pattern Recognition: AI processes hundreds of data points simultaneously, spotting connections that would take human investigators hours to uncover.

Cross-Industry Intelligence: AI learns from patterns across hundreds of millions of applications across thousands of lenders, detecting emerging fraud tactics before they hit your credit union.

Behavioral Analytics: Rather than just checking documents, AI analyzes application behavior, device patterns, and typing rhythms to detect automated activity.

Smart Data Usage: AI pulls expensive third-party data only when suspicious patterns are detected, reducing verification costs by up to 60% while maintaining 100% fraud coverage. This intelligent approach auto-approves clean applications instantly, flags suspicious patterns for human review with additional data sources when needed, and auto-declines apparent fraud immediately. The algorithms are trained across networks of financial institutions, learning from 500M+ applications to detect emerging fraud patterns.

The Credit Union Advantage

43% of credit unions now report that fraud detection ranks among their top three technology investment priorities, and 88% plan to invest in identity risk solutions this year — the highest percentage of any financial institution type.

Credit unions actually have an advantage over big banks in fighting AI fraud: they know their members. AI can amplify this advantage by learning not just generic fraud patterns but patterns specific to your member base and community.

To combat the advancing skills of fraudsters, CommunityWide Federal Credit Union deployed Scienaptic AI fraud detection that analyzes patterns across multiple data sources in real time.

“With fraud evolving rapidly, especially in identity and synthetic attacks, we needed a solution that could act in real time without compromising member experience,” said Andy Burggraf, CEO of CommunityWide Federal Credit Union.

The results?

They’re catching fraud attempts that would have sailed through their old systems, without adding friction for legitimate members.

The Bottom Line

Sarah Mitchell’s case was eventually solved. The credit union caught the fraud and upgraded its detection systems with AI-powered tools. But her story represents thousands of similar cases happening every month across credit unions nationwide.

The fraudsters have AI. The question for every credit union CEO is simple: Do you? The time to act is now, before the next Sarah Mitchell walks through your virtual doors.

Ready to see how AI can transform your fraud detection? Most credit unions discover fraud patterns they never knew existed when they start analyzing their data with AI-powered tools.

Book a demo with Scienaptic’s experts to know more!