Case Study · Banking & Financial Services
Real-Time Fraud Detection Across 4.8 Million Transactions Per Day
Daily Volume
4.8M txn
Latency
<2ms
Detection Accuracy
97.3%
The Challenge
A regional bank was experiencing rising fraud losses driven by increasingly sophisticated transaction patterns that their existing rules-based detection system was not catching. False positive rates were also high, causing unnecessary friction for legitimate customers. The bank needed a system that could operate at full transaction volume in real time without impacting payment processing speeds.
The Approach
BankGuard was deployed into the bank's transaction pipeline after a six-week embedding period during which Almanor AI engineers analysed 18 months of historical transaction data, including confirmed fraud cases. Models were trained on the bank's own fraud patterns — not generic banking data — producing a detection model that reflected the specific behaviours seen in their customer base. Deployment was phased, with BankGuard initially operating in shadow mode alongside the existing system to validate accuracy before going live.
The Results
BankGuard achieved 97.3% detection accuracy at sub-2ms latency, operating across the full transaction volume without any impact on payment processing. False positive rates fell by 61% compared to the previous system. In the first quarter of full operation, the system detected and prevented fraud losses that the existing system would have missed, with a direct financial impact that offset the full deployment cost within the first three months.