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Fintech

SecureBank Financial

$2.5M in fraud prevented annually

A regional bank replaced brittle rules with adaptive ML — and stopped sophisticated fraud without punishing real customers.

Fraud Detection Engine
Fintech
Industry
500,000+
Customers
300% post-pandemic
Txn growth
16 weeks
Go-live

Overview

The story behind the numbers

SecureBank Financial is a regional bank serving more than 500,000 customers with a digital-first product. When transaction volume grew 300% in two years, its legacy rule-based fraud system started failing in both directions at once: blocking good customers while missing the fraud that mattered.

Every new fraud pattern meant a manual rule, and the rule library had become impossible to maintain. SecureBank needed a system that learned continuously instead of one that had to be hand-tuned forever.

The results

Outcomes that moved the business

$2.5M
Fraud prevented / year

Direct savings from blocked fraudulent transactions.

85%
Lower fraud losses

Versus the prior year on the legacy system.

60%
Fewer false positives

Fewer good customers blocked, fewer manual reviews.

50ms
Average decision time

Real-time scoring on every single transaction.

The challenge

What stood in the way

The legacy system was simultaneously too aggressive and too slow — an expensive combination for a bank.

A 40% false-positive rate meant legitimate transactions were routinely blocked.

New fraud patterns emerged faster than analysts could write rules.

Customer complaints about declined payments were rising sharply.

Fraud losses were still growing 25% year-over-year despite the controls.

The manual review team was permanently buried under alert volume.

The solution

How we built the lift

We deployed the Fraud Detection Engine with real-time ML models trained on SecureBank's own transaction history, scoring every transaction in under 100ms.

1

Trained custom models on three years of labeled transaction history.

2

Built real-time scoring with a sub-100ms latency budget per transaction.

3

Created dynamic risk thresholds tied to individual customer behavior profiles.

4

Integrated with the core banking and card-processing systems.

5

Ran shadow-mode testing in production before the full cutover.

In the field

See it in action

SecureBank Financial
Risk analysts get explainable scores, not black boxes.
SecureBank Financial
Live monitoring across every channel and product.
SecureBank Financial
Deep integration with core banking and card rails.
"The AI-powered fraud detection has been a game-changer. We've dramatically reduced losses while improving customer experience. It catches patterns our old rules never could."
Michael Torres
Michael Torres
VP of Risk Management, SecureBank Financial
"Shadow mode gave our board the confidence to switch. We watched it outperform the legacy system for weeks before we flipped a single transaction."
Priya Nair
Priya Nair
Chief Risk Officer
Real-time MLSub-100msCore BankingShadow Deployment

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