Aziz Lookman Ai Mind
Cornell Financial Engineering Manhattan 2026 Ai & Future of Finance Conference
Aziz Lookman is an AI and quantitative finance executive, economist, and entrepreneur with more than two decades of experience applying machine learning, statistical modeling, and artificial intelligence to finance, risk management, insurance, and private equity.
He is the Founder and Chief AI Officer of Rational AI, a firm he founded in 2018 that works with private equity and financial institutions across the investment lifecycle, including deal sourcing, due diligence, valuation, and portfolio-company value creation. Rational AI specializes in transparent, auditable “glass-box” AI solutions that combine quantitative economics, machine learning, and human expertise. The firm has developed AI systems for areas including private-equity secondaries valuation, private credit, healthcare operations, digital entertainment, advertising optimization, and renewable-energy investment analysis.
From 2021 to 2024, Lookman served as Head of AI for Private Equity at Two Sigma, where he built the firm’s machine-learning and generative-AI capabilities for its approximately $2 billion private-equity business. His work supported investment sourcing, due diligence, and portfolio-company value creation.
Previously, Lookman was Chief Risk Officer of Amazon Lending, where he led the risk function for a more than $1 billion business-lending program using AI-driven underwriting and alternative data. Earlier, he served as Principal Economist in Analytics at Cornerstone Research, applying quantitative analysis to complex financial litigation involving asset-backed securities, bankruptcies, credit ratings, and credit derivatives.
Lookman also served as Global Head of Applied AI for Management Liability at AIG, where he established and ran applied-AI capabilities supporting the underwriting of more than $2 billion in insurance policies. At Moody’s, he developed stress-testing and risk-management tools for securitized credit products as well as credit-risk models covering commercial and residential mortgages, credit cards, commercial and industrial lending, and other consumer credit.
His career began in academia. From 2004 to 2009, he was an Assistant Professor of Finance at Carnegie Mellon University and the University of Pittsburgh, where he conducted research and taught statistics and finance. He earned a Ph.D. in Finance from Carnegie Mellon University in 2005 and a B.Tech. in Mechanical Engineering from the Indian Institute of Technology Bombay.
Lookman’s technical expertise spans time-series analysis, hazard models, gradient boosting, regularization, natural-language processing, Bayesian methods, causal inference, explainable AI, and human-in-the-loop systems. He has extensive experience programming in Python and SQL and has worked with cloud platforms including Google Cloud and AWS.
Across his career, Lookman has focused on bringing rigorous quantitative methods and increasingly sophisticated AI systems into high-stakes financial and business decisions, with an emphasis on models that are both analytically powerful and understandable to the people responsible for using them.
