Vikash Agrawal

• Uing complex algoriths to identify fraud and abuse vector.
• Evaluating product-sense analytics to optimize user safety and trust at scale.
• Leveraging advanced Python and SQL ecosystems to solve high-impact, ambiguous business problems.

• Collaborating cross-functionally with strategy teams to translate data-driven insights into actionable policies, balancing risk mitigation and minimizing friction for good users with a total monthly transaction volume of USD 70 Billions.
• Developed robust forecasting models using SQL and Python to actively predict portfolio health and analyze transaction patterns, leading to a 9.5% YoY decrease in false declines for legitimate users.
• Engineered automated daily tracking systems for risk metrics like transaction declines & onboarding restrictions, enabling rapid identification of fraud trends and forecasting impacts, resulting in $5M monthly savings.
• Created an XG Boost ML model to predict transactional level losses in High-risk portfolio enabling more precise loss tracking.
• Developed algorithms to forecast matured losses based on daily arrival resulting in 70% lower code execution times and 100% execution rates.
• Integrating AI to automate complex excel calculation into python scripts & summarizing fraud trends saving 10+ hrs monthly.

Awards: 3x VP Recognition

• Conceptualized and created an internal prospect database and developed risk filters to optimize ROI of marketing campaigns, resulting in faster end to end acquisition of customers and savings worth more than 5 million USD.
• Created a decision tree segmentation model for international markets to predict probability of card decline... achieving a 1.5x increase in overall capture rate of declined cards.
• Innovatively developed Time to target, a forecasting model leveraging SQL and Python to predict the seasonal growth across different industries and targeting them accordingly.
• Engineered a merchant recommendation algorithm, driving revenue growth by guiding customers to spending opportunities.
• Pioneered the development of a Python based NPV(Net Present Value) model for prospect customers, refining the marketing campaign targeting.

• Architected and implemented a sophisticated Salesforce Incentive model, resulting in a remarkable 90% satisfaction rate among sales representatives and a substantial 20% increase in quarter-over-quarter sales.
• Employed a strategic blend of Excel, SQL, and Python to revolutionize sales data, claims data, and physician databases through meticulous statistical analysis.
• Innovatively crafted a dynamic sales tracker featuring live target tracking and goal meeting probability for various products, seamlessly integrating SQL database functionality with an Excel dashboard.