Fraud Detection & Prevention
Real-time AI that catches fraud before it happens, from banking transactions and insurance claims to e-commerce abuse and telecom fraud, protecting billions in revenue.
Modern fraud is sophisticated, fast, and constantly evolving. Our AI fraud detection platform uses graph neural networks, behavioral biometrics, and real-time streaming analytics to detect complex fraud patterns that rule-based systems miss entirely. From transaction-level card fraud to organized crime rings, insurance staging, and identity theft, we build multi-layered defense systems that adapt faster than fraudsters.
What We Do
Fraud Detection & Prevention capabilities
- 01
Real-Time Transaction Scoring
Every transaction scored in <50ms using 300+ features, velocity, geolocation, device fingerprint, behavioral patterns, and merchant risk.
- 02
Graph Neural Network Analysis
AI mapping relationships between accounts, devices, and entities to detect mule networks, shell companies, and organized fraud rings.
- 03
Behavioral Biometrics
Mouse movement, typing patterns, and session behavior creating unique user fingerprints that detect account takeover attempts.
- 04
Claims Fraud Intelligence
NLP + computer vision analyzing claims documents, repair estimates, medical records, and photos for inconsistency and staging indicators.
- 05
Identity Verification AI
Document forgery detection, facial liveness verification, and cross-database identity validation preventing synthetic identity fraud.
- 06
Adaptive Learning
Models continuously retrain on confirmed fraud cases, adapting to new patterns within hours, not months like traditional systems.
Our Approach
From your data to a working result
- 01
Fraud Landscape Assessment
Analyze historical fraud patterns, current detection gaps, and false positive rates to establish baseline and improvement targets.
- 02
Feature Engineering & Graph Building
Build 300+ fraud features and entity relationship graphs from transaction, identity, and behavioral data.
- 03
Multi-Model Training
Train ensemble of supervised (labeled fraud), unsupervised (anomaly), and graph models for layered detection.
- 04
Real-Time Deployment
Deploy models in streaming architecture for sub-50ms scoring with automatic fallback and monitoring.
- 05
Feedback Loop Integration
Connect fraud investigation outcomes back to models for continuous learning and pattern adaptation.
- 06
Adaptive Evolution
Automated model retraining, A/B testing new detectors, and proactive fraud pattern research.
Industry Applications
Where it makes a difference
Technologies & Models We Use
Enterprise-grade tools and frameworks powering our solutions.
Python
Language
PyTorch
Deep Learning
Neo4j
Graph DB
Apache Kafka
Streaming
Redis
Real-time Cache
AWS
Cloud
Elasticsearch
Search
Kubernetes
Orchestration
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