track record
fast growth outpaces fraud risk. i've spent my career in that gap - connecting data, operations, and compliance before it becomes a problem.
product manager, fincrime
Led the migration of fraud rules from one rule engine to another across the UK, EU and UAE corridors. Done in 3 weeks, with pass-vs-hold decision outcomes 2% better after the move.
Built fraud observability for the FinCrime stack: real-time alerts and visibility across products and geos in one view. It led directly to two catches: US checkout card chargeback fraud and referral fraud.
Chargeback fraud on US checkout cards: the transaction would go through, then the cardholder raised a chargeback. Tightened the process with 3DS, fixed a couple of bugs, and streamlined the chargeback process, taking the dispute win rate from 10% to 45%.
Referral fraud had been recurring for 4-5 months. Put guardrails in place and stopped it.
Took KYC check features live. They were built to catch referral fraud, and I added PII filters to the basic KYC tier, where the fraud was happening.
fraud and risk manager
Built the entire fraud framework end to end across a platform of 10M+ users: chargeback procedures, investigation SOPs, UPI and RuPay transaction monitoring, cashback and coin fraud detection, Shop order and RTO monitoring, a block management feature, and a fraud attack mitigation and recovery plan.
Built anomaly detection systems, real-time dashboards, and investigation SOPs that eliminated SIM binding fraud, SMS spoofing, referral fraud, and coin farming entirely.
Identified and stopped an active ATO attack - immediate friction changes on day 1 reduced incidents by 70%, fully eliminated within 4 days.
Caught and closed a cashback loophole that was actively being exploited - losses dropped to zero within days of the fix. Caught first party RuPay card-to-cash abuse (velocity checks and refund friction) and RTO abuse in Shop (pattern detectors for order, return, and geography clustering).
When a fraud incident hit, worked with the data team to build a risk scoring model - mapping confirmed fraud user patterns across transaction amount, merchant category, error codes, and failure rates. Model kept evolving as new inputs came in, with a plan to integrate directly into the rule engine as a live feedback loop.
Implemented an AI typology based pattern detector seeded from confirmed fraud cases.
Compliance: built the compliance calendar, managed audit policies on Sprinto, liaised with InfoSec end to end, and drove audit readiness for NPCI, ISO 27001, PCI DSS, SOC 2, and DLSAR - zero lapses across 5 certifications.
senior, risk and fraud analytics
Led a team of analysts to build fraud policies and investigation workflows, reducing fraud losses by 35% within 3 months.
Strengthened KYC and onboarding processes and created anomaly monitoring dashboards; identified and blocked 3 high fraud-risk regions, reducing high-risk sign-ups by 30% and boosting credit underwriting compliance by 25%.
Enhanced name-match accuracy by ~5% through algorithm improvements; led inter and intra-user deduplication and blacklisting projects, reducing duplicate accounts by 10%.
manager, risk and fraud
Built the complete fraud framework: rule engine on ClickHouse and Metabase, investigation procedures for every fraud type, chargeback procedures, and anomaly monitoring dashboards across transaction, onboarding, and rewards layers.
Handled first party fraud where bad actors used the platform to defraud innocent users, and rewards loophole abuse where gaps in cashback and referral logic were exploited at scale.
Built the AML blacklist screening service on Whitebook to screen sanctioned individuals at onboarding - first structured AML layer the product had. Established FIU-IND and RBI regulatory reporting workflows.
Migrated all users to a risk-aware onboarding flow. Developed SOPs aligned with KYC and CDD/EDD, resulting in a 40% reduction in case resolution time. ~90% reduction in fraud losses. Rs 5M in rewards abuse prevented.
Launched three fraud-related products working with Engineering, Product, Legal, and Design - resulting in a 30% faster fraud review process.
analyst, fraud and business intelligence
Detected key fraud patterns through data analysis, building a proof-of-concept model that laid the groundwork for shifting fraud operations to an analytics-driven model.
Developed and implemented an automated fraud detection system using Selenium - 90% reduction in manual review time, 15% increase in identification of suspicious transactions.
Led projects to optimise workflows and automate reporting for 5+ departments saving ~100+ hours per week.
investigations specialist, sanctions compliance
Investigated suspicious accounts and trained a 120-member investigations team, enhancing procedures to improve investigation efficiency and consistency.
Built three SharePoint sites for risk assessments, compliance training modules, and reporting surveys, enhancing knowledge sharing across a 200+ member team and improving investigation rates.