FINANCIAL SERVICES

Global Fintech Platform Secures AI-Powered Fraud Detection

How a leading digital payments company maintained PCI DSS compliance while deploying LLM-based fraud detection across 200M+ transactions monthly.

200M+

Transactions/Month

47%

Fraud Detection Increase

3ms

Added Latency

The Challenge

This global fintech company processes payments for millions of merchants across 50+ countries. They wanted to enhance their fraud detection capabilities using large language models to analyze transaction patterns and customer behavior.

The challenge: payment data is subject to strict PCI DSS requirements. Card numbers, CVVs, and cardholder information must never be exposed to external AI services. They needed a way to leverage AI without compromising compliance.

The Solution

The fintech platform deployed SafeKey Lab's complete AI Security Suite:

Real-Time PII Detection

Credit card numbers (with Luhn validation), CVVs, and cardholder data are automatically detected and tokenized before reaching the AI model. The model sees tokens like [CARD_****4242] instead of actual card numbers.

LLM Guard

Protection against prompt injection attacks that could manipulate the fraud detection AI into approving fraudulent transactions or revealing sensitive information.

Edge Deployment

SafeKey Lab runs at the edge in each of the company's data centers, ensuring sub-5ms latency even at peak transaction volumes of 10,000 requests per second.

The Results

Within three months of deploying SafeKey Lab, the fintech platform achieved:

47% increase in fraud detection rate using LLM-enhanced analysis
$23M in prevented fraudulent transactions in the first quarter
Maintained PCI DSS Level 1 certification with zero compliance findings
Only 3ms added latency to transaction processing
Successfully blocked 12 sophisticated prompt injection attempts

"SafeKey Lab allowed us to bring AI into our fraud detection pipeline without any PCI DSS concerns. The performance is remarkable - we added sophisticated LLM analysis with virtually no impact on transaction latency."

- VP of Risk Engineering

Technical Implementation

The integration was straightforward, requiring minimal changes to their existing infrastructure:

from safekeylab import protect, guard

# Before sending to AI model
protected = protect(transaction_data)
result = guard(protected).send_to_llm(fraud_model)

# PII is automatically tokenized, LLM never sees real card numbers

Transform Your Fraud Detection

Ready to enhance your fraud detection with AI while maintaining PCI compliance? Contact our financial services team for a personalized demo.

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