Key contributor in design and development of an analytical credit card dispute management system that now saves over $30 million every year through targeted challenges of credit card disputes. 

Quantified impact of bad experience on the app store for a select set of fraudulent applications using Causal Inference,

Implemented a custom clustering analysis framework for identifying and estimating genuine customers who had poor experiences with applications that were terminated for fraud and App Store guideline violations.

Designed and implemented dispute management performance monitoring to identify long-term trends and opportunities using Python, SQL and Tableau. This was also extended to identify aberrant patterns whose resolution led over savings of $2M/year. 

Designed and implemented analytical process to calculate KPIs that drove influential conversations with banks and card networks to streamline the credit card dispute management process resulting in higher success rate of up to 60% from 35%. 

Implemented customized dispute management framework in collaboration with cross-functional teams engineering and product teams for new markets in China and India thereby saving $2.5M USD.

Led cross-functional efforts initiatives to identify, induct and standardize fraud data from third parties that resulted in a lift of 10-15% in identification of specific kinds of credit card fraud on the app store.

Implemented a Fraud early warning system using time series analysis to identify potentially active large scale Fraud rings across different markets.

Developed and operationalized a central fraud repository of labels from different sources which was then used for fraud model training and feature development