Fraud in fintech isn’t a question of if, it’s a question of when. Transactions happen in milliseconds, but legacy fraud detection systems can take hours to react. By then, the damage is already done.
Our client, a fast-scaling fintech company handling millions of transactions daily, was losing money due to fraudulent activities. Their existing fraud detection system relied on static rule-based checks, meaning it flagged too many legitimate transactions while still allowing fraudsters to slip through.
They needed a real-time, AI-driven system that could detect fraud patterns instantly, reduce false positives, and keep operations running smoothly.
That’s exactly what we built.
Why Rule-Based Fraud Detection Wasn’t Enough?
Fraud tactics were evolving faster than their system- High false positives
- Slow fraud response times
- Operational inefficiencies
They were losing money, customers, and trust. We needed to change the game.
How We Made AI Work for Fraud Prevention?
Instead of adding more manual review layers, we introduced an adaptive AI system that could:
1. AI That Learns, Not Just Reacts
Traditional fraud detection systems work with fixed rules, like spending limits or location mismatches. The problem is, fraudsters quickly adapt to these systems. To combat this, we built a self-learning AI model using Scikit-Learn, XGBoost, and TensorFlow. This AI analyzes billions of transactions to recognize fraud patterns, even those fraudsters haven’t used yet.
Rather than simply reacting after fraud occurs, our AI predicts high-risk transactions before they happen, learning individual user spending behaviors. This approach stopped fraud before it could cause damage while ensuring legitimate transactions weren’t wrongly flagged. As a result, fraud detection accuracy improved by 40%, reducing financial losses and customer complaints.
For example, PayPal uses a similar machine learning model to analyze billions of transactions annually, reducing fraudulent chargebacks by 60%.
2. Smarter Real-Time Monitoring
Fraud happens in milliseconds, so detection needs to be just as fast. A slow fraud detection system means businesses find out about fraudulent transactions after the money is gone. We developed a real-time monitoring system using Apache Spark for high-speed data processing and AWS SageMaker for adaptive fraud detection models that can analyze transactions as they happen.
Our system continuously scans for anomalies in user behavior, device usage, and transaction history to detect fraud before it’s too late. It flags:
- AI detects sudden spikes in spending or high-value purchases that don’t align with past behavior.
- A credit card being used in two countries within minutes? That’s an instant red flag.
- Multiple failed logins, followed by a successful high-value transaction, signals potential account takeover fraud.
Our solution reduced fraud response times from hours to milliseconds, preventing fraudulent transactions before they were approved.
For instance, Stripe, which processes over $100 billion annually, uses similar real-time fraud detection tools, protecting against millions in fraudulent transactions daily.
3. Fixing the False Positive Problem
Stopping fraud is crucial, but blocking real customers is just as harmful. If fraud detection is too aggressive, businesses lose legitimate sales, frustrate customers, and damage their reputation. We implemented an adaptive fraud scoring model using Google AutoML for behavior tracking and PyTorch for advanced fraud scoring, ensuring genuine transactions weren’t wrongly flagged.
Instead of automatically blocking transactions, the AI assigns a risk score to each one based on factors like user history and spending patterns. Moderate-risk transactions trigger additional checks, but aren’t outright denied.
This dynamic approach reduced false positives by 30%, leading to higher approval rates and fewer customer complaints. For example, Square reduced its false positive rate by 25%, resulting in increased sales and happier customers.
4. Easy Integration with Existing Systems
Most financial platforms can’t afford to rip and replace their entire security infrastructure. Our fraud detection solution was designed as an API-first system, making integration effortless without disrupting business operations. Using AWS Lambda for API processing and Kubernetes for scalable deployment, we ensured the system could process millions of transactions per second without latency issues.
This integration worked alongside our client’s existing tools, adding an extra layer of security. It was implemented without downtime, while still meeting PCI-DSS and GDPR compliance.
For instance, MasterCard uses a similar approach to integrate advanced fraud detection seamlessly across its global network
Fraud isn’t slowing down, and neither should your fraud detection system.
With AI-driven real-time monitoring, adaptive fraud scoring, and seamless API integration, our solution doesn’t just react to fraud, it prevents it before it happens.
Our client went from struggling with delayed fraud detection and false positives to a system that catches fraud instantly while keeping genuine transactions flowing smoothly.
50% fewer fraud-related losses, 30% fewer false positives, and a stressless experience for real customers.
At Cogntix, we believe fraud prevention should be fast, accurate, and scalable, without adding complexity to your operations. If your current system is costing you money, customers, and trust, let’s fix that.
Written by: Gayathri Priya Krishnaram (Digital Content Writer at Cogntix)
