Hariom Tatsat

Hariom Tatsat is an AI researcher, keynote speaker, author and financial AI specialist who helps organisations understand, assess and adopt artificial intelligence responsibly. One of his most recognised achievement is authoring ‘Machine Learning & Data Science Blueprints for Finance’, a number one new release in Amazon’s artificial intelligence category. Through his research and speaking, Hariom makes complex AI systems clearer, safer and more practical for leaders working in finance and other highly regulated industries.

Hariom’s career has been shaped by a strong foundation in engineering, quantitative finance and machine learning. He earned a Bachelor of Technology from the Indian Institute of Technology Kharagpur, before completing a Master’s in Financial Engineering at the University of California, Berkeley. He also qualified as a Financial Risk Manager and completed the Certificate in Quantitative Finance, strengthening his knowledge of risk, financial modelling and investment markets. Hariom began his professional journey modelling interest rate instruments before moving into increasingly senior quantitative roles. At Nomura, he worked on pricing and hedging correlation based credit derivatives, including default baskets, CDO tranches and credit default swap indices. He later joined the Royal Bank of Scotland as Senior Associate, focusing on counterparty credit risk and credit valuation adjustment, before working as a Quantitative Analyst at First Abu Dhabi Bank, where he priced and hedged exotic rates and credit instruments. These formative experiences gave him a detailed understanding of how sophisticated financial systems operate, how risk is measured and how mathematical models influence real business decisions.

In 2017, Hariom joined Barclays Investment Bank, where he progressed to become Director of AI Quant. In this position, he leads the design, fine tuning and implementation of artificial intelligence models and agentic AI systems for financial use cases, while conducting research into AI interpretability. He is also an Advisor and Mentor at Berkeley SkyDeck, supporting start-ups working within the fintech sector. As an author, Hariom has helped finance professionals apply machine learning to trading, pricing, portfolio management, fraud detection, risk prediction and sentiment analysis. His published research includes ‘Beyond the Black Box: Interpretability of LLMs in Finance’, which explores how large language models can become more transparent, auditable and trustworthy in high stakes financial settings. Hariom has also contributed research on robust risk aware reinforcement learning and interpretable deep reinforcement learning for exchange traded fund trading. His expertise has earned him the Indian Achievers’ Award in Machine Learning, alongside invitations to speak at the Federal Reserve Bank of Atlanta, NVIDIA GTC, AI4, Reuters Momentum AI Finance, The AI Summit New York and the NexGen Banking Summit.

Hariom is an engaging choice for organisations seeking a speaker who can connect advanced AI research with the practical realities of business, finance and regulation. His keynotes cover trustworthy AI, AI interpretability, financial AI, large language models, agentic AI, responsible deployment and the future of artificial intelligence in regulated sectors. He explains why organisations must understand how AI reaches decisions, where current monitoring systems can fail and how leaders can introduce stronger validation, governance and accountability. Hariom’s combination of financial leadership, technical research and accessible communication gives audiences a practical framework for adopting AI with greater confidence.