🚀 Hi, I’m Divya Chittiprolu — and this is my hands-on plan to drive innovative fraud prevention strategies and build scalable post-bind fraud detection solutions at Definity.

I specialize in building and scaling fraud intelligence platforms that empower:

• Fraud analysts & decision scientists → to detect suspicious behaviours, identify anomalies, and refine fraud rules.
• Fraud strategic leads & business stakeholders → to act on fraud intelligence and make faster, data-driven business decisions.

The goal: reduce fraud deflection, strengthen enterprise trust, and deliver a seamless customer experience.

As a Fraud Risk APM at Uber I built 0-1 Saas Data Workbenches for Fraud intelligence, — enabling global fraud risk operations teams to analyze customer behaviour, detect anomalies, and design rules that reduced fraud risk while improving customer trust.

How I built Fraud Investigations Data Workbench from scratch and scaled it 10x

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Modular UI Design Wireframe → Built MVP for Promo Fraud

• Wins: 21% operational efficiency and 15% reduction in false positives

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Phase1 - Core Fraud Detection: Account Take Over , Eats Fraud & Trip level Fraud

• Wins: 30% operational efficiency, Account review SLA boosted from 48hrs to 24hrs, reduced fraud deflection by 20%

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Phase 2 – User Base Expansion: Driver & Courier Partner Investigations • Wins: Case handing time reduced by 40%, Precision maintained 97%+

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Phase 3 – Cross Vertical expansion: Safety, Account Identity, ML Ops, Onboarding Workflows

• Wins: 10k+ users, Core platform for Global Fraud Risk investigations

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Check my portfolio and LinkedIn to see more of my accomplishments and impact.

✉️ Why I’m Writing This

I believe the fraud detection space is one of the most innovative areas in product management — it demands creativity, advanced detection technologies, and, most importantly, the ability to think like a fraudster to mitigate emerging risks.

As a Product Owner, I’ve worn multiple hats for my MVP launches — yes, even the “Sherlock Holmes” hat 🕵️‍♀️.

This document demonstrates how I already think and act like Definity’s Enterprise Fraud risk catcher: how I build rules, design models, and deliver scalable solutions to detect and prevent fraud across insurance products and services.

👉The Real Challenge

• Post-bind fraud deflection: Detect suspicious claims, policy manipulations, or suspicious customer behaviours without creating friction for genuine customers
• Model and rule implementation: Build, test, and iterate AI-driven detection models and rules engines to flag anomalies and patterns in real-time
• Mitigating emerging risks: Stay ahead of evolving fraud methods (synthetic identities, staged claims, organized fraud rings) while ensuring compliance and trust