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How Community Banks Have Saved Over $1.2 Million with FraudSentry YTD 2025

Read Time 2 mins | Written by: Karly Field

FraudSentry is an AI-powered check-fraud detection solution designed specifically for community banks and credit unions. By continuously scanning transactions and leveraging machine learning to identify anomalous patterns, FraudSentry not only flags fraudulent checks in real time but also streamlines investigations—saving time, reducing staffing costs, and protecting your bottom line. Tailor your thresholds and notification channels to minimize false positives and focus your team on the highest-risk items.

January–June 2025 Performance

From January through June 2025, FraudSentry prevented $1,238,687.21 in fraudulent check losses for its customers—an average of $206,447.87 per month. This sustained impact demonstrates our solution’s consistency and scalability across institutions of all sizes.

Here are some examples of the amount of check fraud prevented from different size institutions.

Institution

Asset Size

Total Check Faud Prevented 1H 2025 (USD)

Bank A

$325M

$158,402.03

Bank B

$628M

$251,650.40

Bank C

$473M

$98,522.74

Bank D

$2.94B

$254,890.63

Bank E

$624M

$69,476.92

Why It Matters

  • Immediate Payback: One community financial institution in Alabama saw over $7 back for every $1 spent in a single month—meaning FraudSentry isn’t just an expense; it’s a revenue-protecting investment.
  • Predictable Costs, Measurable Returns: Transparent monthly fees let you forecast ROI and budget effectively.
  • Scalable for All: Whether you’re a smaller community bank or a larger regional institution, FraudSentry adapts to your transaction volume and risk profile.

Finovifi prioritizes direct access to our leadership and fraud experts, so you’re never stuck waiting on a support ticket. With over $1.2 million prevented so far in 2025 and multiple clients achieving ROI >1 in single months, now is the time to see how FraudSentry can safeguard—and even pay for—your fraud-prevention program.