Fraud DetectionSolutions
Catch suspicious orders, payments and accounts early with scoring and rules, while keeping false alarms low enough that honest customers are not blocked.
Fraud Detection Solutions: what the work involves
Fraud reaches small businesses through stolen cards, fake cash-on-delivery orders, refund abuse, duplicate accounts and fabricated invoices. Staff spot some by instinct and miss many more. Fixed rules, such as blocking orders above a set amount, either let clever fraud through or reject good customers, and each wrongly declined sale is a customer who rarely returns.
We start by examining your labelled history: chargebacks, returned-undelivered parcels, confirmed fraud cases. Features capture velocity, mismatches between address, phone and IP location, device and email patterns, basket composition and past behaviour of the same customer. A gradient-boosted model scores each event in milliseconds, and a rules layer handles known patterns and hard limits. Scores map to three outcomes: approve, send for review, or decline, with thresholds set according to your cost of fraud versus cost of lost sales. Reviewers see the contributing signals and decide, and their decisions are fed back. Where labelled fraud is scarce, we add anomaly detection and say so.
Core features
Real-time risk scoring
Orders, payments or sign-ups receive a risk score within a fraction of a second, ready for checkout decisions.
Rules plus model
Clear rules handle known patterns and hard limits, while the model catches subtler combinations of signals.
Three-way decisions
Approve, review or decline, with thresholds you set from the real cost of fraud and of turning away good customers.
Manual review queue
Borderline cases reach an analyst with the key signals listed, and every decision becomes training data.
Linked-account detection
Shared phones, addresses, devices and payment details reveal clusters of accounts operated by one actor.
Monitoring and alerts
Dashboards track approval rates, review volumes and fraud losses, and warn when patterns shift suddenly.
What we get right before launch
False positives hurt revenue
Blocking honest customers is costly and invisible. We choose thresholds by weighing both errors, send uncertain cases to review, and report declined-good-order estimates where measurable.
Adaptive adversaries
Fraudsters change tactics once blocked, so a static model decays. We monitor performance, retrain regularly and keep fast-to-edit rules for new patterns.
Fairness and explanation
Decisions affecting customers should not rest on protected traits or opaque proxies. We exclude such inputs, keep reason codes, and provide a route for customers to appeal.
Tools and technology
- Python
- XGBoost
- scikit-learn
- FastAPI
- Redis
- PostgreSQL
- Kafka
- Metabase
Common questions, answered
Can you guarantee fraud will be stopped?
No one can. The aim is to reduce losses at an acceptable cost in false alarms. We test on your history, set thresholds with you, and monitor results, since fraud patterns keep changing.
We have few confirmed fraud cases. Can you still help?
Yes, though with limits. We combine rules from your experience with anomaly detection, and start collecting labelled outcomes through the review queue so a supervised model becomes possible later.
Will it slow checkout?
Scoring is designed to complete in a small fraction of a second. For heavy checks we run them asynchronously and hold only borderline orders for review before dispatch.
What data do you need to share?
Order or transaction records with outcomes, such as refunds, chargebacks and refused deliveries. We minimise personal data, pseudonymise fields where possible and agree access and retention in advance.
More AI Agents & Automation services
All AI Agents & Automation servicesAnomaly Detection Solutions
Automatic detection of numbers and events that look wrong, so a human learns about the problem hours after it starts, not at month-end.
Credit Scoring Models
Risk scores for lending decisions that you can explain to a regulator, a loan officer and a declined applicant, built on your own repayment data.
Customer Churn Prediction
Spot the customers drifting away while there is still time to act, with reasons attached so your team knows what to say.
Ready to start your Fraud Detection Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.