Fraud Detection ML System
The real tradeoff in fraud detection is false positives vs. false negatives
Every fraud detection system makes a tradeoff: catch more fraud and you'll also flag more legitimate transactions as suspicious (frustrating real customers); reduce false positives and some fraud will slip through. There's no model that eliminates both perfectly - the right balance depends on your specific business's cost of each type of error, and we scope the model's tuning around that explicit tradeoff rather than a vague "detect fraud" goal.
What we build
- Model training on your historical transaction data, with fraud/legitimate labels from your own past cases as the foundation
- Feature engineering specific to your transaction patterns - what actually correlates with fraud in your business, not a generic industry template
- Threshold tuning explicitly balanced against your real cost of false positives (customer friction) versus false negatives (fraud losses)
- Integration into your transaction flow with real-time or near-real-time scoring, plus a review queue for borderline cases rather than pure auto-block/auto-allow
Our process
1. Understand your real fraud patterns and cost tradeoffs
What fraud has actually cost you historically, and what customer friction from false positives costs you - this shapes the entire tuning approach.
2. Prepare and label historical data
Your past transactions with known fraud/legitimate outcomes become the training foundation.
3. Train and evaluate honestly
Reporting real precision/recall tradeoffs, not a single "accuracy" number that hides the false-positive rate that actually matters to your business.
4. Deploy with a review queue
Borderline-scored transactions route to human review rather than pure automated block/allow, especially in the early period after launch.
Pricing
Rs 200,000-700,000 depending on data volume, feature complexity, and integration requirements.
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