AI-Powered Graph-Based Fraud Detection System for Financial Services
87%
reduction in false positive rates
Sub700ms
real-time scoring
$4.2M
annual fraud loss reduction
ABOUT THE CLIENT
The client is a fintech group operating within high-volume digital transaction processing, serving both institutional and retail customers. Their platform facilitates fast payments and financial transfers, requiring high accuracy and efficiency to maintain customer trust and ensure regulatory compliance.
Challenge
The company struggled with significant financial losses due to evolving fraud tactics that surpassed traditional rule-based detection systems. The existing setup generated over 15% false positives, causing customer dissatisfaction and unnecessary investigations. Real-time processing requirements, complex fraud networks involving interconnected accounts, and a lack of visibility into transactional relationship patterns prevented effective fraud prevention. Additionally, increasing regulatory pressure and the need for scalable infrastructure intensified the urgency to implement an advanced system capable of early outlier detection while supporting rapid transaction growth.
Deeper Look
OmiSoft’s Solution
Graph-Based Transaction Mapping
We built a real-time relationship graph engine that dynamically mapped connections between transactions, accounts, devices, and merchant entities across sliding time windows. By analyzing both direct and indirect relationships as well as temporal dependencies, the system identified previously undetectable fraud schemes and uncovered complex network patterns typical of money laundering activities.
Advanced Statistical Anomaly Detection
Omisoft implemented a statistical scoring framework using ensemble methods that analyzed transaction amounts, frequencies, and behavioral patterns against individual historical baselines. By assigning adaptive anomaly scores to transactions, the system achieved high precision in highlighting fraudulent attempts while significantly reducing false positive alerts.
Real-Time AI Fraud Scoring Pipeline
To meet strict sub-second response requirements, we engineered a high-performance processing pipeline enabling scoring within 700 milliseconds. The solution incorporated graph metrics, feature engineering, and adaptive thresholding algorithms that learn from feedback, enabling continuous improvement in detection accuracy and supporting proactive fraud prevention strategies.
Custom AI-Powered Graph-Based Fraud Detection System
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Tech Stack used
Cross-platform Technologies Used
Python
Docker
Apache Flink
Apache Kafka
Business Results
The implementation of Omisoft’s graph-based AI fraud detection system fundamentally enhanced the client's fraud prevention capability, reducing operational costs and improving customer satisfaction while ensuring compliance with regulatory requirements. The real-time system provided unparalleled visibility into transactional relationships and enabled predictive fraud mitigation, positioning the company as a technology-driven leader in financial risk management.
87% reduction in false positive rates (from 15% to just 2%)
87% reduction in false positive rates (from 15% to just 2%)
94% detection accuracy for known fraud types
94% detection accuracy for known fraud types
Sub-700ms real-time scoring, enabling seamless transaction processing
Sub-700ms real-time scoring, enabling seamless transaction processing
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