AI Fraud Detection System
Real-Time ML Fraud Scoring for Fintech Transactions
$50M+ in monthly transactions, protected in real time — 99.7% fraud detection accuracy at 0.3ms per decision.
Who We Built This For
SecurePay is a fintech startup processing $50M+ in monthly transactions with no real-time fraud detection in place. Every chargeback represented direct financial loss and damaged relationships with payment processors, and the absence of automated screening meant fraud was only caught after the damage was already done.
What We Set Out to Build
SecurePay needed a fraud detection system capable of scoring every transaction in real time, at the scale and speed their $50M+ monthly volume demanded — without introducing latency that would degrade the checkout experience for legitimate customers. The system needed to catch fraud before it completed, not flag it afterward when the money was already gone.
The core of the system is a machine learning model trained on transaction patterns, customer behaviour, and historical fraud cases, generating a real-time risk score for every transaction in under half a millisecond. Geolocation anomaly detection cross-references transaction location against a customer's typical behaviour patterns, flagging transactions that don't fit — like a purchase suddenly originating from a different country than the customer's usual activity.
High-risk transactions can be automatically blocked before completion based on configurable risk thresholds, while borderline cases are routed for additional verification rather than outright rejection — balancing fraud prevention against the cost of false positives that frustrate legitimate customers. The system continuously learns from new transaction outcomes, improving its detection accuracy over time as fraud patterns evolve.
Project Facts
Tech Stack
What Needed to Be Solved
SecurePay was processing $50M+ in monthly transactions with no real-time fraud detection system in place, leading to significant chargebacks and financial losses. The company needed a system that could score and act on fraud risk instantly, at transaction volume scale, without adding noticeable latency to the checkout experience.
No real-time fraud screening existed, meaning fraudulent transactions were only identified after settlement — when financial loss was already locked in
Manual fraud review processes could not keep pace with $50M+ in monthly transaction volume, creating a backlog of unreviewed risk
Chargebacks from undetected fraud directly impacted revenue and strained relationships with payment processing partners
No systematic way existed to detect geolocation anomalies or behavioural pattern deviations that often signal fraudulent activity
Any fraud detection system needed to operate at extremely low latency to avoid degrading the checkout experience for legitimate customers
Static, rule-based fraud checks couldn't adapt to evolving fraud patterns, quickly becoming outdated as fraudsters changed tactics
Inside the Platform
Every module is purpose-built to replace a standalone tool — unified in one workspace.
- Sub-millisecond transaction scoring
- ML-based risk model per transaction
- Configurable risk threshold rules
- Continuous model retraining pipeline
- 99.7% production detection accuracy
- Location-based behaviour profiling
- Cross-border transaction flagging
- Device & IP fingerprint analysis
- Velocity-based anomaly detection
- Travel-pattern learning per customer
- Instant block on high-risk transactions
- Step-up verification for borderline cases
- False-positive minimisation tuning
- Manual review queue for edge cases
- Real-time merchant notification
- Fraud prevented value tracking
- Pattern analysis across transaction history
- Model performance monitoring
- Chargeback rate trend reporting
- Investigator case management tools
Everything in One Workspace
Real-Time ML Scoring
Every transaction is scored for fraud risk in 0.3ms using a machine learning model, with no noticeable impact on checkout speed.
Geolocation Anomaly Detection
Flags transactions that deviate from a customer's typical location and behaviour patterns, catching account takeover and stolen-card fraud.
Automated Blocking
High-risk transactions are automatically blocked before completion based on configurable thresholds, stopping fraud before it happens.
Continuous Model Learning
The fraud model retrains on new transaction outcomes, adapting detection accuracy as fraud patterns evolve over time.
False-Positive Minimisation
Borderline transactions route to step-up verification rather than outright blocking, protecting legitimate customer experience.
Fraud Prevention Analytics
Real-time dashboard tracking fraud value prevented, pattern trends, and model performance across the transaction pipeline.
Case Management Tools
Investigator tooling for reviewing flagged transactions and edge cases that require human judgment beyond the automated score.
High-Throughput Architecture
Built on FastAPI and Redis to handle high transaction volume with consistent low-latency scoring performance.
How We Built It
Fraud Pattern Analysis & Model Design
Analysed historical transaction and fraud data to identify key risk signals, then designed the TensorFlow-based machine learning model architecture for real-time scoring.
Real-Time Scoring Pipeline
Built the FastAPI-based scoring pipeline capable of evaluating every transaction in under half a millisecond, backed by Redis for low-latency data access.
Geolocation & Behavioural Analysis
Developed the geolocation anomaly detection system, profiling customer location and device behaviour to flag deviations indicative of fraud.
Auto-Blocking & Verification Flow
Built the automated blocking engine with configurable risk thresholds, and the step-up verification flow for borderline transactions to minimise false positives.
Production Rollout & Continuous Learning
Rolled out the system across live transaction volume, validated 99.7% detection accuracy, and implemented the continuous retraining pipeline to adapt to evolving fraud patterns.
Full Scope of Work
Built a TensorFlow-based machine learning model for real-time transaction fraud scoring
Developed a FastAPI scoring pipeline achieving 0.3ms average decision time per transaction
Built geolocation and device anomaly detection to flag deviations from customer behaviour patterns
Implemented an automated blocking engine with configurable risk thresholds for high-risk transactions
Built a step-up verification flow for borderline cases to minimise false positives on legitimate customers
Developed a continuous model retraining pipeline adapting detection accuracy to evolving fraud patterns
Built a fraud analytics dashboard tracking prevented fraud value and model performance over time
Architected the system on Redis for low-latency data access at $50M+ monthly transaction scale
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