Hariom Tatsat

Hariom Tatsat

Democratizing Financial AI

Professional Experience
2017 - Present
Barclays

Vice President at Barclays Investment Bank, NY

Leading data-driven decision-making using statistics, machine learning and AI.

2014 - 2016
FAB

Desk Quant at First Abu Dhabi Bank (FAB)

Pricing and hedging of Financial Instruments for exotic rates and credit desk. Used financial mathematics for data driven decision-making.

2012 - 2014
RBS

Senior Quant at RBS

Led Counterparty Credit Risk and CVA Quant teams. Worked on simulation on Big-Data for Financial decision making.

2010 - 2012
Nomura

Associate at Nomura

Pricing and hedging of Financial Instruments for credit derivatives desk.

Corporate Trainings
AI Finance Masterclass
AI and Generative AI for Finance Masterclass (Online Interactive Workshop)
AI Use Cases
Harnessing data for AI Use Cases in Finance by Hariom Tatsat
ISB Programme
Advanced Programme in Leadership and Digital Innovation
Media Mentions
AI Finance Masterclass
A Quant Striving to Bridge the Worlds of Finance and ML
Economic Insider Article
Unveiling the Next-Gen of Generative AI and ChatGPT in Finance
AI Use Cases
A Remarkable Journey: From a Small Town in India to Wall Street
AIM Research Article
Adoption of AI/Gen AI in Finance with Hariom Tatsat
US Insider Article
Bridging the Gap Between ML and Finance
ISB Programme
Journey Of Financial Innovation Through AI And ML
San Francisco Post Article
The Future is Here: How AI Will Transform Investment and Asset Allocation
NY Wire Interview
Up and Close Interview with Hariom Tatsat
NY Weekly Article
Reinforcement Learning: The Untapped Frontier in Algorithmic Trading
Books & Courses
Co-author of the book published in December 2020 by O'Reilly.
Machine Learning in Finance
Algo Trading Multiverse
Awards & Publications
Award for outstanding professional achievement and contribution to nation building, presented by the Indian Achievers’ Forum.
Robust risk-aware RL using RDEU and Wasserstein distance to optimize financial decisions under uncertainty and downside risk.
Interpretability framework for Deep Q-Learning applied to ETF trading, enabling visual, user-friendly insights into RL decision-making processes.
Analyzing investor risk tolerance using machine learning to correct biases, adapt to market changes, and optimize long-term asset allocation.
Technical Reviewer and Editorial Board Member, contributing expertise to the evaluation and development of technical publications.