Real-Time Fraud Detection System

The Challenge

A top-20 US regional bank was experiencing rapidly escalating fraud losses. Their legacy rule-based fraud detection system, built over a decade of incremental additions, had grown to over 2,000 rules that were increasingly difficult to maintain and tune. Fraudsters had learned to circumvent the predictable rule patterns, while legitimate customers were frustrated by frequent false declines.

The numbers told a stark story: fraud losses had increased 40% year-over-year, reaching $85 million annually. Meanwhile, false positive rates hovered around 15%, meaning one in seven legitimate transactions was being declined or delayed for manual review. Customer complaints about declined transactions had become a top driver of account closures.

The bank needed a system that could adapt to evolving fraud patterns in real-time while dramatically reducing friction for legitimate customers.

The Solution

Jasnova designed and implemented a comprehensive real-time fraud detection platform that replaced the legacy rule-based system with an adaptive machine learning approach. The solution combined multiple detection strategies in an ensemble architecture capable of catching both known fraud patterns and novel attack vectors.

Technical Architecture

• Streaming Processing Pipeline: Built on Apache Kafka and Apache Flink, the platform processes transactions in real-time with sub-50ms latency from receipt to decision
• Feature Store: Implemented a low-latency feature store using Redis and Apache Cassandra to compute and serve real-time features including velocity metrics, behavioral patterns, and network relationships
• Ensemble Model Architecture: Combined gradient boosting models for known patterns, neural networks for complex behavioral analysis, and graph-based models for detecting organized fraud rings
• Adaptive Learning: Implemented continuous model retraining pipelines that incorporate new fraud patterns within hours of detection

Feature Engineering

The model leverages over 500 engineered features across multiple categories:

• Transaction Features: Amount, merchant category, time of day, transaction type, entry mode
• Velocity Features: Transaction counts and amounts over multiple time windows (1 hour, 24 hours, 7 days) by customer, card, device, and merchant
• Behavioral Features: Deviation from historical spending patterns, new merchant indicators, geographic anomalies
• Network Features: Connections to known fraud accounts, shared devices or addresses with flagged accounts
• Contextual Features: Time since last transaction, distance from last transaction location, device fingerprint matching

Implementation

Phase 1: Data Infrastructure (Weeks 1-6)

Established the streaming data pipeline and feature store infrastructure. Integrated with the bank's core banking system, card network feeds, and authentication systems. Built historical feature datasets from 24 months of transaction data.

Phase 2: Model Development (Weeks 4-12)

Developed and validated the ensemble model architecture using historical data. Worked closely with the bank's fraud operations team to incorporate their domain expertise and ensure model outputs were interpretable. Achieved 95% detection rate on historical fraud with a 3% false positive rate in backtesting.

Phase 3: Shadow Deployment (Weeks 10-16)

Deployed the system in shadow mode alongside the existing rule engine. Both systems scored every transaction, but only the legacy system made decisions. This phase validated real-time performance and allowed side-by-side comparison of detection rates and false positives.

Phase 4: Gradual Rollout (Weeks 16-20)

Progressive transition from legacy to ML-based decisions, starting with low-risk transaction segments and expanding to full coverage. Maintained the ability to fall back to legacy rules for any segment if issues arose.

Results

After 12 months of production operation, the platform delivered exceptional results:

• $50M+ Annual Fraud Prevention: Fraud losses decreased from $85M to $32M annually, a 62% reduction
• 60% Reduction in False Positives: False positive rate dropped from 15% to 6%, dramatically improving customer experience
• Sub-50ms Decision Latency: 99th percentile latency remained under 50ms, ensuring no impact on transaction processing time
• Novel Fraud Detection: The system detected three previously unknown fraud schemes within the first quarter, each worth over $1M in prevented losses
• Operational Efficiency: Manual review volume decreased by 70%, allowing the fraud operations team to focus on complex investigations

Adaptive Defense

One of the most valuable aspects of the platform is its ability to adapt to new threats. When a new card-testing fraud scheme emerged targeting the bank's ATM network, the system:

1. Detected anomalous patterns within 2 hours of the attack beginning
2. Automatically adjusted risk scores for affected transaction types
3. Provided fraud analysts with detailed pattern analysis
4. Incorporated the new pattern into the model within 24 hours

The total exposure from this attack was limited to under $50,000, compared to an estimated $2M+ if detected through traditional methods.

Technology Stack