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Fraud Triage Automation for Digital Payments

Reduced fraud review backlog using AI-assisted prioritization and identity document analysis.

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Fintech9 WEEKSVERIFIED RESULTSDigital payments company

Fraud Triage Automation for Digital Payments

Reduced fraud review backlog using AI-assisted prioritization and identity document analysis.

Founder note: this is written to be practical. We focus on what we learned in delivery, what changed for the team, and what results held up after rollout.

INDUSTRYFintech
CLIENT TYPEDigital payments company
TIMELINE9 weeks
DELIVERY SCOPETransaction scoring, identity docs, watchlist checks
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The Real Problem

1. Fraud review backlog exceeded 48 hours at peak

2. False positives frustrated legitimate customers

3. Identity document checks were manual

4. Analysts lacked risk-ranked work queues

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What We Actually Changed

Implemented ML risk scoring with document verification, sanctions screening, and AI-prioritized queues so analysts focus on highest-risk cases first.

TOOLS AND METHODS USED

PythonXGBoostOCRPostgreSQLSanctions APIAWS
layers

Delivery Journey

PHASE 1

Risk Modeling

Transaction scoring, feature engineering

PHASE 2

Triage System

Analyst queues, document AI

PHASE 3

Production

Live monitoring, compliance sign-off

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What Improved

Analysts work the right cases first. We cut backlog without sacrificing detection — customers notice the difference.

-71%
Backlog
-29%
Fraud Loss
Queue cleared within SLA consistently
Better scoring reduced wrongful blocks
High-risk cases caught faster
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FOUNDER BRIEF

INDUSTRY
Fintech
TIMELINE
9 weeks
CLIENT TYPE
Digital payments company
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DELIVERY STACK

PythonXGBoostOCRPostgreSQLSanctions APIAWS

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