Abhinav Kumar

Abhinav Kumar is a credit strategy and data science leader who applies machine learning and causal inference to high-stakes decisions in consumer finance. As Vice President of credit acquisition strategy at a leading global bank, he leads strategy across a $12 billion credit card portfolio - consuming ML models, designing champion/challenger experiments, and translating model output into approval decisions and measurable P&L impact. His work lives where most enterprise AI stalls: in the gap between a data science model and the business decision it's meant to drive.

Abhinav is also an educator and speaker. He has delivered data science and machine learning courses at the University of Massachusetts Amherst and JK Lakshmipat University (India), lectured in IIFT New Delhi's (India) Management Development Program, and speaks on wealth management for industry audiences. He holds an MS in Data Analytics and Computational Social Sciences from UMass Amherst (Dean's Scholar) and an MBA from the Indian Institute of Foreign Trade, New Delhi (India).

He is the author of the peer-reviewed paper Propensity Score Matching to Control for Consumer Bank Churn: A Case of Causal Inference, which demonstrates how causal-inference methods can complement traditional predictive models in designing customer-retention strategy. (DOI: 10.58425/ajt.v5i6.543 · read the paper)