• Card fraud prevention: Detect stolen card usage, card testing, and unauthorized transactions in real-time
• Account takeover (ATO): Identify compromised accounts using behavioral biometrics and session anomalies; block unauthorized access
• Digital wallet fraud: Prevent unauthorized payments through Apple Pay, Google Pay, Samsung Pay with device and behavioral verification
• E-commerce fraud: Detect new account fraud, card-not-present (CNP) fraud, and bot-driven automated attacks
• Synthetic identity fraud: Flag accounts created with mixed real/fake identities through behavior profiling and velocity analysis
• Velocity-based attacks: Detect card testing, multiple failed payment attempts, and high-frequency transaction abuse
• Chargeback prevention: Identify high-risk transactions likely to result in chargebacks before they occur
• Transaction monitoring: Continuous review of all transactions with real-time risk scoring and decision support

Payment processors and financial institutions face escalating fraud threats:

• Fraud Scale and Velocity: Global card fraud exceeds $28B annually; organized fraud rings operate at scale with automated bot attacks, card testing, and synthetic identity creation
• Attack Sophistication: Fraudsters use compromised credentials, RATs (Remote Access Tools), masked VPNs, and social engineering; static rules fail to detect evolving tactics
• Real-Time Decision Pressure: Payment networks require sub-second fraud decisions; delays cause legitimate customer friction and lost sales
• False Positive Burden: Traditional rule-based systems produce 95%+ false positive rates, overwhelming customer service teams and frustrating legitimate customers
• Multi-Channel Complexity: Fraud spans cards, wallets, mobile, e-commerce, P2P; each channel requires tailored detection logic
• Regulatory and Compliance: PCI-DSS, fraud liability rules (chargebacks), and emerging regulations require transparent, auditable fraud controls

• Revenue protection: Block fraudulent transactions before settlement; prevent chargebacks, refunds, and customer disputes
• Customer experience: Reduce false positives; approve legitimate transactions faster; eliminate unnecessary friction (challenges, 3D Secure) for good customers
• Operational efficiency: Automate fraud decisioning; reduce manual review burden; scale fraud prevention without proportional staffing growth
• Regulatory compliance: Demonstrate robust fraud controls meeting PCI-DSS, network regulations, and banking authority expectations
• Brand reputation: Protect customers from identity theft and fraud; build trust through secure, frictionless transactions
• Cost reduction: Lower fraud losses, chargeback fees, and operational support costs; improve bottom-line profitability

Transaction fraud detection is grounded in advanced statistical techniques, machine learning, and behavioral science. Key foundations and recent advancements include:

• Rule-Based and Threshold Detection: Foundational layer for velocity checks, geolocation, and scenario-based alerts
• Supervised Machine Learning: Gradient boosting (XGBoost, LightGBM) and DNNs achieve 95%+ detection accuracy while reducing false positives to <5%
• Unsupervised Anomaly Detection: Isolation Forests, Autoencoders, VAE capture novel fraud patterns without labeled training data
• Deep Neural Networks (DNN) & Recurrent Networks (RNN/LSTM): Capture complex temporal patterns; learn feature interactions in high-dimensional data
• Behavioral Biometrics: Passive authentication via micro-patterns (typing dynamics, mouse movement, scroll velocity) detects account takeovers even with valid credentials
• Graph Neural Networks (GNNs): Model transaction networks; detect organized fraud rings, mule accounts, and money movement patterns
• Explainable AI (XAI): SHAP, LIME provide transparent fraud reason explanations; critical for customer support, disputes, and regulatory inquiries
• Real-Time Streaming ML: Sub-millisecond inference via edge computing, network-embedded ML, and serverless functions for instant transaction decisions
• Adversarial Robustness: Defense against fraudster evasion techniques; continuous model monitoring and retraining to counter adaptive attacks
• Multi-Modal Fusion: Combine transaction data, behavioral biometrics, device intelligence, network context, and external threat intelligence for holistic risk scoring

These advancements enable real-time, accurate fraud detection with minimal false positives, protecting customers and institutions while maintaining seamless, frictionless transaction experiences.

We don't believe in one-size-fits-all and our solutions are tailored to your business problem. Our approach:

• Discovery: We assess your transaction volumes, fraud profile, payment channels, customer base, and existing fraud controls
• Architecture Design: We design real-time fraud detection pipelines supporting sub-second latency, high throughput (billions of transactions/day), and multiple payment channels
• Technology Selection: We select ML models (gradient boosting, DNN, autoencoders), behavioral analytics engines, and real-time streaming frameworks optimized for your transaction mix
• Model Development: We build supervised models for fraud classification, unsupervised models for anomaly detection, and behavioral biometric profiles
• Rules & Velocity Checks: We implement card velocity, IP velocity, geolocation, and scenario-based rules tailored to your fraud landscape
• Integration & Deployment: We integrate with payment gateways, issuer networks, merchant processors, and customer systems with real-time APIs
• Testing & Validation: We conduct rigorous backtesting, sensitivity analysis, and pilot deployments to validate accuracy and minimize false positives
• Monitoring & Governance: We provide real-time alert dashboards, false positive reduction feedback loops, and continuous model optimization

Flexible Architecture and Deployment

• Cloud Deployment (AWS, Azure, GCP):
• Elastic compute for handling transaction spikes and seasonal fraud patterns
• Managed services for Kafka/streaming, ML model inference, and real-time analytics
• Global CDN and low-latency endpoints for international transaction processing
• On-Premises Deployment:
• Full control over transaction data and customer information; no data egress
• Custom GPU/TPU clusters for accelerated model inference; microsecond-level latency
• Air-gapped environments for highly regulated financial institutions
• Hybrid Deployment:
• Transaction processing on-premises; ML model training and analytics in the cloud
• Edge computing for ultra-low-latency fraud decisions at payment network level
• Meets compliance requirements while leveraging cloud ML innovation

Phase 1: Assessment and Strategy:

• Audit your transaction volumes, fraud characteristics, current fraud controls, and existing false positive burden
• Define fraud detection objectives, acceptable false positive rates, and key performance targets (fraud catch rate, decision latency)
• Design a real-time fraud detection architecture incorporating ML, behavioral analytics, velocity checks, and multi-channel coverage

Phase 2: Pilot Deployment:

• Develop and deploy ML-based fraud detection models on a pilot transaction subset (e.g., card transactions or e-commerce)
• Validate fraud detection accuracy, false positive rates, and latency performance against production requirements
• Develop fraud decisioning dashboards, case management tools, and investigator workflows

Phase 3: Production Integration:

• Deploy fraud detection engines to production for real-time scoring of all transaction streams across channels
• Integrate with payment processors, issuer networks, merchant systems, and fraud response workflows
• Train fraud analysts, customer service teams, and risk management on interpreting fraud scores and managing false positive feedback

Phase 4: Continuous Monitoring and Optimization:

• Monitor model performance, fraud detection rates, false positive rates, and latency; track against baseline and KPIs
• Collect feedback from fraud investigators and customers; identify emerging fraud patterns and model blind spots
• Retrain models, update rules, and optimize thresholds to maintain accuracy and reduce false positives as fraud tactics evolve
• Expand fraud detection to new payment channels, geographies, and customer segments as business grows