The neo-bank had scaled to 4.2 million active users across three markets in 18 months. Their fraud detection infrastructure hadn't kept pace. The legacy rule-based engine processed transactions in batch cycles every 47 minutes — meaning fraudulent activity could run undetected for nearly an hour before triggering an alert.
False positive rates had climbed to 34%, burying the 12-person operations team under thousands of flagged transactions daily. Legitimate customers were having accounts frozen for hours while analysts manually reviewed alerts. The Head of Risk estimated that customer friction from false positives was driving $3.2M in annual churn.
Meanwhile, actual fraud losses had reached $8.7M quarterly — a number the board found unacceptable for a company preparing for an IPO.
We were losing $8.7M per quarter to fraud while simultaneously freezing legitimate customer accounts. Our fraud system was simultaneously too slow and too aggressive.
— Chief Risk Officer, Series D Neo-Bank
Intelliblitz deployed a three-layer sovereign fraud intelligence grid. Layer one: a real-time streaming pipeline ingesting every transaction, device fingerprint, geolocation signal, and behavioral pattern into a sub-second processing engine — all running on the bank's own AWS infrastructure with PCI-DSS Level 1 compliance.
Layer two: an ensemble AI model combining graph neural networks for relationship-based fraud detection, anomaly scoring for transaction velocity patterns, and behavioral biometrics for account takeover prevention. Trained exclusively on 18 months of the neo-bank's own 2.1 billion transactions.
Layer three: an adaptive feedback loop where analyst decisions continuously retrained the model in near-real-time. Every confirmed fraud case and every false positive dismissal made the system smarter within minutes.
Within the first week, the AI caught a coordinated account takeover ring across 340 accounts that our old system had classified as normal activity. That single catch prevented $2.1M in losses.
— VP of Fraud Operations, Series D Neo-Bank
Within 30 days, fraud detection latency collapsed from 47 minutes to under 3 seconds. The AI grid prevented $12.4M in fraudulent transactions in its first full quarter. False positive rates dropped from 34% to 3.1%, freeing the operations team to focus on genuine threats.
Customer account freeze incidents fell 89%, reducing friction-driven churn by $2.8M annually. The IPO readiness team cited the fraud infrastructure as a key differentiator, contributing to a successful public offering at a $3.4B valuation.
Our underwriters specifically called out the fraud detection infrastructure as a competitive moat during the IPO process. It went from our biggest liability to our strongest operational asset in 90 days.
— CEO, Series D Neo-Bank