Fraud and anomaly detection — E-commerce
Graph and sequence models catching fraud and process anomalies in real time. Delivered for a e-commerce client in Jamnagar.
- Location
- Jamnagar
- Year
- 2022
- Duration
- 8 weeks
- Team
- 6 engineers
- 89%
- fraud caught
- −68%
- false positives
- 26k
- tx per second
The challenge
Conditions on site — heat, dust, vibration and patchy connectivity — had defeated two earlier attempts at this in e-commerce.
What we built
We ran a two-week discovery sprint, then built and fielded the system end to end on XGBoost, Graph ML and Kafka, with operator training and an SLA.
Delivery timeline
Phase 1
Discovery sprint and site survey
Phase 2
Prototype built and tested in the Motera lab
Phase 3
Pilot on site with before/after measurement
Phase 4
Scale-out, training and handover
Highlights
- Integrated with existing PLC, ERP or GIS systems — no rip-and-replace
- Before-and-after measurements agreed with the client up front
- Operator training delivered in Gujarati, Hindi and English
- Remote monitoring and over-the-air updates included
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