Software · Cybersecurity
Fraud detection
Real-time risk scoring for payments, sign-ups and claims, with clear reasons and a review queue.
Fraud rules written once and never revisited either block good customers or let losses through. We build fraud detection systems that score each payment, sign-up or claim in milliseconds using rules and machine learning together, show the reasons behind every score, and send borderline cases to a review queue. Reviewer decisions feed back so the models keep improving.
What we build
- Real-time scoring APIs that return allow, review, step-up or decline within your latency budget
- Rule engines that analysts edit and test against past transactions before switching on
- Machine learning models trained on your labelled history with gradient-boosted trees
- Device, velocity and link features such as shared cards, emails, phones or payout accounts
- Review queues with case history, linked accounts and one-click decisions
- Chargeback and confirmed-fraud feedback loops that relabel training data
DemoInteractive · sample data
Try it live.
A working sample with made-up data. It runs in your browser; nothing is sent anywhere.
| Transaction | Customer | Amount | Risk score | Decision |
|---|---|---|---|---|
| TX-88412 | Ravi Kulkarni | ₹4,999 | 12 | Allow |
| TX-88415 | Elena Petrova | ₹1,82,000 | 87 | Review |
| TX-88419 | Hannah Lee | ₹23,500 | 64 | Step-up |
| TX-88420 | Omar Siddiqui | ₹1,299 | 8 | Allow |
What it does well
Explainable scores
Each decision lists its top factors, such as a new device or an unusual amount.
Rules plus models
Firm business rules and learned models run together, each tested before release.
Link analysis
Graphs reveal accounts that share cards, devices, addresses or payout details.
Shadow mode
New rules and models score live traffic silently first, so their effect is measured.
Reviewer tools
Analysts see the full case, add notes and decide, with every action recorded.
Fair and auditable
Features are reviewed for bias, and every model version is stored with its training data.
Questions
Rules or machine learning: which should we use?
Both. Rules encode what you know for certain, such as blocked regions or daily limits, and are easy to explain. Models catch patterns too subtle for rules. We run them together, and every rule and model goes through shadow testing on live traffic before it can block anyone.
How much history do we need to start?
Rules can start on day one. A useful supervised model generally needs several thousand confirmed fraud and genuine cases with outcomes attached. With fewer, we start with rules and anomaly scores and collect labels from reviewers until a trained model is worth building.
How do you avoid blocking genuine customers?
We measure false positives as carefully as caught fraud. Borderline scores go to review rather than straight to decline, step-up checks such as 3-D Secure or an OTP are used where they fit, and every decline reason is logged so complaints can be traced and rules corrected.