Souma Basu
Technology and business leader helping organizations turn AI, enterprise platforms, and transformation programs into lasting business value across insurance, banking, and capital markets.
For more than fifteen years I have partnered with business and technology leaders on large-scale transformation: modernizing core platforms, bringing applied AI into regulated operations, and aligning people, process, and technology with strategy. I write here about what actually works in practice.
AI-Powered Data Transformation: Modern Solutions for Insurance and Beyond
A practical, easy-to-read guide for leaders and practitioners who want to understand data and AI without getting buried in difficult jargon. Written entirely from real-world experience on live insurance and financial services programs, it breaks down data governance, integration, and AI readiness in simple, relatable terms, focused on what actually works in regulated environments.
By Souma Basu, with co-authors Pankaj Pant, Anshul Kaushik, and Satya Prakash.
Get the book on Amazon →The latest brief
Talked Into It: Why Financial Institutions Must Govern AI Authority, Not Just AI Intelligence
The attacker doesn't pick the lock. They persuade the doorman. On the AI risk financial services hasn't fully priced.
Read the full insight →Recent thinking
Human Judgment at AI Speed: Rethinking the Claims Operating Model
Every claims AI program eventually meets a file the demo never showed. What happens next depends less on the model and more on where you drew the line of human authority.
AI Is Learning to Build Its Own Foundations
The Day AI Started Designing the Hardware That Will Train the Next AI
The Future of Payments Isn't Coming. It's Already Processing Trillions.
How stablecoins are reshaping payments, treasury, and insurance faster than most institutions realize.
Capability Is Cheap. Readiness Is Not.
The 2026 Stanford AI Index shows a widening gap between what AI can do and what organizations are prepared to govern, absorb, and operate.
The hidden cost of cutting people to "save money" with AI
Workers are also customers. New research shows AI-driven layoffs can shrink the pie for everyone, while the true cost of running AI is badly underestimated.
How OAC Will Reshape Connected & Autonomous Cars and the Future of Insurance
Over-the-Air Computation could let networks deliver risk signals instead of raw telemetry, changing the economics of telematics and how insurers underwrite connected and autonomous vehicles.
Where I focus
Published thought leadership
AI-Powered Data Transformation: Modern Solutions for Insurance and Beyond
Co-authored with Pankaj Pant, Anshul Kaushik, and Satya Prakash. A practical guide to data governance, integration, and AI readiness for regulated industries, written from real-world experience on live insurance and financial services programs. The goal: help readers move from project execution to confident, defensible decision-making that creates lasting value.
Large Language Models in Finance: Our Paper at ISBM 2026
Our paper, "Large Language Models in Finance: A Survey of Foundations, Applications, Adoption Strategies, and Open Challenges," was presented at the 5th World Conference on Information Systems for Business Management (ISBM 2026) in Bangkok. It has been selected for publication in the ISBM Conference Proceedings by Springer Nature. It was a privilege to work with my co-authors, Aparna Krishna Bhat and Pankaj Pant. Why we wrote it Financial institutions are no longer asking whether large language models are useful. They are asking how to actually get one. To answer that, a whole family of financial LLMs (FinLLMs) has emerged, and they are built in very different ways. Some are foundation models trained from scratch on proprietary data. Others take an open-source model and fine-tune it at a fraction of the cost. We didn't try to catalog every published system. We organized the field around the decision an institution actually faces: not which model is best in the abstract, but which construction approach fits its own constraints. What the paper covers Foundations: the adaptation techniques behind the differences between models. These range from full fine-tuning, prompt-based methods and instruction tuning to RLHF, parameter-efficient methods like LoRA, and retrieval-augmented generation (RAG). Applications: the finance work FinLLMs are inheriting, not inventing: trading, risk modeling, text mining and advisory. These set the performance baselines any model has to beat. Systems compared: BloombergGPT, FinMA, InvestLM, FinGPT and FinTral. We compare them by how they were built, because that choice drives cost, openness and fit for real deployments. A decision framework: practical guidance for institutions deciding whether to build, fine-tune, or buy. What the evidence says The benchmark picture is mixed. On sentiment analysis and classification, FinLLMs now hold their own against much larger general-purpose models. On multi-step numerical reasoning, they still fall well short, and none of the adaptation strategies we reviewed has closed that gap. The biggest open obstacles are hallucination, knowledge that stops at the training cutoff, and the lack of a shared evaluation framework. So we argue that retrieval grounding, contamination-checked benchmarks, and auditable fine-tuning should come before simply scaling models up. Why it matters to me My day job is helping insurers and financial services firms modernize their core platforms. There, the questions in this paper are concrete: what to build, what to buy, how to govern it, and how to trust the output. This research was another chance to connect academic work with what we see in the industry every day, and to keep learning as AI keeps changing. I'm grateful to my co-authors and to everyone who supported this work. I'll share a link here once the Springer Nature proceedings are published.
Work with me
Keynote Speaking
Talks on applied AI in financial services, insurance platform modernization, and leading large-scale transformation, grounded in real programs rather than theory.
Strategic Advisory
Advisory for executives on AI strategy, enterprise architecture, platform selection, and transformation delivery across insurance, banking, and capital markets.
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