Transaction Fraud Detection

Stop fraud in milliseconds. 95%+ detection accuracy. Sub-5% false positives. Real-time protection across all channels.

95%+

Detection Accuracy

Sub-5ms

Decision Latency

28B+

Annual Fraud Stopped

The Problem

✗ Rule-based systems: 95%+ false positive rate
✗ Card fraud at scale: 28B+ annually globally
✗ Fraudsters evolve daily: organized rings, bots, APTs
✗ Customer friction: legitimate users blocked needlessly

Our Solution

✓ Gradient Boosting + DNN: 95%+ accuracy, sub-5% false positives
✓ Real-time streaming: Sub-millisecond fraud decisions
✓ Behavioral biometrics: Detect account takeover instantly
✓ Multi-channel: Cards, wallets, P2P, e-commerce covered

What's Included

ML-Based Fraud Classification

XGBoost, LightGBM, DNN for real-time fraud vs. legitimate scoring

Anomaly Detection

Isolation Forest, LOF, Autoencoders detect novel fraud patterns in real-time

Behavioral Biometrics

Typing speed, mouse movement, scroll velocity, hesitation patterns for ATO detection

Velocity Checks

Card velocity, IP velocity, geolocation monitoring; detect card testing and abuse

Multi-Channel Coverage

Cards, digital wallets, bank transfers, e-commerce, mobile banking, P2P

Explainability & Transparency

SHAP/LIME explanations; customer support tools for dispute resolution

How We Deploy

Cloud

AWS/Azure/GCP - elastic compute for fraud spikes; global endpoints

On-Premises

GPU acceleration; microsecond latency; full data control

Edge

Payment network-embedded; ultra-low latency at point of transaction

Key Use Cases

1Card fraud prevention (stolen cards, card testing, CNP fraud)

2Account takeover detection (behavioral biometrics + session anomaly)

3Digital wallet fraud (Apple Pay, Google Pay, Samsung Pay)

4E-commerce fraud (new account fraud, bot attacks)

5Synthetic identity fraud (mixed real/fake identities)

6Velocity-based attacks (multiple failed attempts, card testing)

7Chargeback prevention (high-risk transactions before dispute)

8Transaction monitoring (continuous real-time review & scoring)

9Mule account detection (money movement networks)

10Organized fraud ring identification (graph analysis & GNN)

Implementation Timeline

Week 1

Assessment

Audit transaction volumes, fraud profile, existing controls, false positive burden

Week 2-3

Pilot

Develop ML models; validate accuracy, latency; test on pilot transaction subset

Week 4+

Production

Deploy to production; integrate with payment processors, issuer networks

Ongoing

Optimization

Monitor performance; retrain models; reduce false positives; expand channels

Fraud Types Detected

Card Fraud

Stolen cards, cloning, testing; velocity + geolocation anomalies

Account Takeover

Compromised credentials; behavioral biometric anomalies; new device

Synthetic Identity

Mixed real/fake identities; velocity abuse; merchant pattern mismatch

New Account Fraud

High-risk profiles; immediate high-value transactions; chargeback history

Bot Attacks

Automated card testing; velocity spikes; identical transaction patterns

Money Mule Networks

Transaction network analysis; graph patterns; rapid fund flows

Technology Stack

Machine Learning Models

Supervised Learning

• XGBoost, LightGBM - 95%+ accuracy, sub-5% false positives
• Deep Neural Networks (DNN) - Complex feature interactions
• Random Forests - Feature importance & interpretability

Unsupervised Learning

• Isolation Forest - Novel fraud pattern detection
• LOF, DBSCAN - Density-based anomaly detection
• Autoencoders/VAE - Latent fraud pattern discovery

Behavioral Analytics & Network Analysis

Behavioral Biometrics

• Typing dynamics & keystroke patterns
• Mouse movement & velocity tracking
• Scroll patterns & hesitation detection
• Device fingerprinting & session analysis

Graph Analysis

• Graph Neural Networks (GNN) - Transaction network patterns
• Community Detection - Organized fraud rings
• Money Movement Tracking - Mule networks & flows

Real-Time Streaming & Inference

Streaming Platforms

• Apache Kafka (millions of transactions/sec)
• Apache Spark (real-time aggregations & features)
• Flink (complex event processing)

Inference Engines

• Sub-millisecond model inference
• GPU acceleration (NVIDIA CUDA)
• Model caching & optimization

Rules Engine & Velocity Checks

Velocity Monitoring

• Card velocity (transactions/time window)
• IP velocity (geolocation changes)
• Failed attempt velocity (brute force)

Rule Engine

• Scenario-based rules (CVV mismatches, etc.)
• Geolocation impossibility checks
• Blacklist/whitelist management

Explainability & Integration

Transparency

• SHAP Values - Fraud reason attribution
• LIME - Local explanations for disputes
• Feature Importance - Decision drivers

System Integration

• Payment gateway APIs (Stripe, Square)
• Issuer network integration (Visa, Mastercard)
• Case management & investigator workflows

Infrastructure & Compute

High-Performance Compute

• GPU clusters for model training & inference
• Distributed processing (billions of transactions/day)
• Sub-millisecond latency SLA

Data & APIs

• PostgreSQL (fraud history & analytics)
• Redis (real-time state & caching)
• FastAPI (fraud decision endpoints)

Business Impact

→Revenue Protection: Block fraud before settlement; prevent chargebacks and refunds; protect billions in annual transactions
→Customer Experience: Sub-5% false positives; approve legitimate transactions instantly; eliminate unnecessary friction
→Operational Efficiency: Automate fraud decisioning; reduce manual review burden; scale without proportional staffing
→Compliance: Meet PCI-DSS, network regulations, and banking authority fraud control expectations
→Brand Trust: Protect customers from identity theft; build reputation as secure, trustworthy payment platform
→Cost Reduction: Lower fraud losses, chargeback fees, and operational support costs; improve profitability

Stop Fraud. Instantly.

95%+ detection accuracy. Sub-5ms decisions. Sub-5% false positives. Protect customers & revenue.

Schedule a Demo →

Quick Questions

How fast must fraud decisions be made?

Payment networks require sub-second responses; most processors enforce hard limits of 100-500ms. Our system delivers decisions in sub-5ms, leaving margin for network latency and business logic.

Can you handle our transaction volume?

Our architecture scales to billions of transactions/day using distributed streaming (Kafka, Spark). Inference is parallelized across GPU clusters for consistent sub-5ms latency even at peak volume.

What false positive rate is acceptable?

Industry standard is 3-5%. Above 5% causes customer friction and support burden. We target sub-5% while maintaining 95%+ detection accuracy.

How do you stay ahead of evolving fraud tactics?

We retrain models monthly on new fraud patterns detected by investigators. Behavioral biometrics and unsupervised anomaly detection catch novel patterns without labeled data.

Can we understand why transactions were blocked?

Yes. SHAP/LIME provide explainable reasons (velocity spike, behavioral anomaly, score threshold) for customer support and dispute resolution.

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What's Included

ML-Based Fraud Classification

XGBoost, LightGBM, DNN for real-time fraud vs. legitimate scoring

Anomaly Detection

Isolation Forest, LOF, Autoencoders detect novel fraud patterns in real-time

Behavioral Biometrics

Typing speed, mouse movement, scroll velocity, hesitation patterns for ATO detection

Velocity Checks

Card velocity, IP velocity, geolocation monitoring; detect card testing and abuse

Multi-Channel Coverage

Cards, digital wallets, bank transfers, e-commerce, mobile banking, P2P

Explainability & Transparency

SHAP/LIME explanations; customer support tools for dispute resolution

Key Use Cases

1Card fraud prevention (stolen cards, card testing, CNP fraud)

2Account takeover detection (behavioral biometrics + session anomaly)

3Digital wallet fraud (Apple Pay, Google Pay, Samsung Pay)

4E-commerce fraud (new account fraud, bot attacks)

5Synthetic identity fraud (mixed real/fake identities)

6Velocity-based attacks (multiple failed attempts, card testing)

7Chargeback prevention (high-risk transactions before dispute)

8Transaction monitoring (continuous real-time review & scoring)

9Mule account detection (money movement networks)

10Organized fraud ring identification (graph analysis & GNN)

Implementation Timeline

Week 1

Assessment

Audit transaction volumes, fraud profile, existing controls, false positive burden

Week 2-3

Pilot

Develop ML models; validate accuracy, latency; test on pilot transaction subset

Week 4+

Production

Deploy to production; integrate with payment processors, issuer networks

Ongoing

Optimization

Monitor performance; retrain models; reduce false positives; expand channels

Fraud Types Detected

Card Fraud

Stolen cards, cloning, testing; velocity + geolocation anomalies

Account Takeover

Compromised credentials; behavioral biometric anomalies; new device

Synthetic Identity

Mixed real/fake identities; velocity abuse; merchant pattern mismatch

New Account Fraud

High-risk profiles; immediate high-value transactions; chargeback history

Bot Attacks

Automated card testing; velocity spikes; identical transaction patterns

Money Mule Networks

Transaction network analysis; graph patterns; rapid fund flows

Technology Stack

Machine Learning Models

Supervised Learning

• XGBoost, LightGBM - 95%+ accuracy, sub-5% false positives
• Deep Neural Networks (DNN) - Complex feature interactions
• Random Forests - Feature importance & interpretability

Unsupervised Learning

• Isolation Forest - Novel fraud pattern detection
• LOF, DBSCAN - Density-based anomaly detection
• Autoencoders/VAE - Latent fraud pattern discovery

Behavioral Analytics & Network Analysis

Behavioral Biometrics

• Typing dynamics & keystroke patterns
• Mouse movement & velocity tracking
• Scroll patterns & hesitation detection
• Device fingerprinting & session analysis

Graph Analysis

• Graph Neural Networks (GNN) - Transaction network patterns
• Community Detection - Organized fraud rings
• Money Movement Tracking - Mule networks & flows

Real-Time Streaming & Inference

Streaming Platforms

• Apache Kafka (millions of transactions/sec)
• Apache Spark (real-time aggregations & features)
• Flink (complex event processing)

Inference Engines

• Sub-millisecond model inference
• GPU acceleration (NVIDIA CUDA)
• Model caching & optimization

Rules Engine & Velocity Checks

Velocity Monitoring

• Card velocity (transactions/time window)
• IP velocity (geolocation changes)
• Failed attempt velocity (brute force)

Rule Engine

• Scenario-based rules (CVV mismatches, etc.)
• Geolocation impossibility checks
• Blacklist/whitelist management

Explainability & Integration

Transparency

• SHAP Values - Fraud reason attribution
• LIME - Local explanations for disputes
• Feature Importance - Decision drivers

System Integration

• Payment gateway APIs (Stripe, Square)
• Issuer network integration (Visa, Mastercard)
• Case management & investigator workflows

Infrastructure & Compute

High-Performance Compute

• GPU clusters for model training & inference
• Distributed processing (billions of transactions/day)
• Sub-millisecond latency SLA

Data & APIs

• PostgreSQL (fraud history & analytics)
• Redis (real-time state & caching)
• FastAPI (fraud decision endpoints)

Business Impact

→Revenue Protection: Block fraud before settlement; prevent chargebacks and refunds; protect billions in annual transactions

→Customer Experience: Sub-5% false positives; approve legitimate transactions instantly; eliminate unnecessary friction

→Operational Efficiency: Automate fraud decisioning; reduce manual review burden; scale without proportional staffing

→Compliance: Meet PCI-DSS, network regulations, and banking authority fraud control expectations

→Brand Trust: Protect customers from identity theft; build reputation as secure, trustworthy payment platform

→Cost Reduction: Lower fraud losses, chargeback fees, and operational support costs; improve profitability

Quick Questions

How fast must fraud decisions be made?

Payment networks require sub-second responses; most processors enforce hard limits of 100-500ms. Our system delivers decisions in sub-5ms, leaving margin for network latency and business logic.

Can you handle our transaction volume?

Our architecture scales to billions of transactions/day using distributed streaming (Kafka, Spark). Inference is parallelized across GPU clusters for consistent sub-5ms latency even at peak volume.

What false positive rate is acceptable?

Industry standard is 3-5%. Above 5% causes customer friction and support burden. We target sub-5% while maintaining 95%+ detection accuracy.

How do you stay ahead of evolving fraud tactics?

We retrain models monthly on new fraud patterns detected by investigators. Behavioral biometrics and unsupervised anomaly detection catch novel patterns without labeled data.

Can we understand why transactions were blocked?

Yes. SHAP/LIME provide explainable reasons (velocity spike, behavioral anomaly, score threshold) for customer support and dispute resolution.