IJAZ E

Designed fraud prevention strategies for gift card transactions and bill payments, successfully reducing the fraud-to-sales (FTS) ratio from 10% to 0.5%, despite a 4x increase in transaction volume, by implementing rate limits and real-time fraud alerts. Developed an Address Similarity Model to detect organized fraud in ecommerce brands, reducing new user offer abuse by 40%. Enhanced underwriting

Developed a LightGBM-based model for Suspicious Transaction Detection using transaction, temporal, user, and network features, achieving 65% precision and 20% recall, leading to a 3% reduction in new user delinquency rates. Designed and implemented graph-based rules leveraging the NetworkX library to create dynamic graphs and to extract features. Contributed to enhancing performance and efficiency

Designed and implemented real time dynamic credit utilization strategies, with first transaction and cumulative utilisation thresholds, where dynamic thresholds were updated based on historical transaction data. Worked on end-to-end deployment of address similarity filter, from initial analysis to production deployment. Implemented real-time filtering for API and event flow, incorporating similari

Designed and deployed rate limit and velocity filters for IP addresses, devices, and merchant accounts, to control cluster fraud. Deployed a blacklist based strategy that blocks transactions based on any blacklisted attributes, establishing a feedback loop for dynamically adding and removing attributes from the table. Designed and implemented an IP- based strategy for identifying high-risk IP octe

Conducted exploratory data analysis on data captured by neuro ID service to identify key features for an onboarding model. Developed an automation process for prioritizing the support tickets, eventually improved the efficiency of the support team. Collaborated closely with fraud monitoring team to manually analyze fraud patterns and devise proactive solutions.

Grade: 95.56 %

Completed Higher Secondary Examination with a strong academic performance. Achieved 95.56% in the examination.

Grade: 9.8 / 10

Completed the All India Secondary School Examination (AISSE) with a high grade. Achieved 9.8 out of 10.

Grade: 7.36 /10

Completed a 5-year integrated Master of Science program. Achieved a performance of 7.36 out of 10.