Saurabh Kakkar Releases Guide on Implementing AI/ML in Banking
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Published by Notion Press on August 11, 2026, the book is now available on Amazon.
The book covers the entire AI/ML lifecycle, from credit risk modeling to model validation, deployment, and MLOps. It emphasizes the importance of data quality, data lineage, monitoring, governance, and audit readiness in ensuring that AI systems operate responsibly within financial institutions.
Kakkar's book is designed to bridge the gap between AI/ML theory and practical enterprise implementation. He focuses on creating explainable, governed, and scalable AI systems that can withstand the scrutiny of regulators and management. The author's extensive experience working with regulatory reporting, data-quality challenges, and credit-risk analytics informs his approach, making the book a valuable resource for professionals in the banking and financial services industry.
The book is particularly useful for banking and financial-services executives, credit-risk and model-risk professionals, AI/ML and data-science leaders, technology executives, compliance and audit teams, FinTech and RegTech professionals, researchers, professors, graduate students, and consultants. It provides a detailed exploration of topics such as fairness, bias mitigation, responsible AI, model governance, and human oversight, ensuring that financial institutions can develop AI systems that are transparent, scalable, and ready for regulatory review.
Saurabh Kakkar's background includes an MBA from Fordham University's Gabelli School of Business, a Diploma in Business Management from ICFAI, and a Bachelor of Commerce from the University of Delhi. His professional work has also led to the development of two registered German utility models and nine published scholarly publications. Kakkar's commitment to turning advanced AI/ML and analytics into explainable, governed, and scalable solutions for regulated financial institutions is evident in his latest book.
In addition to his book, Kakkar continues to contribute to the field through his work and research, focusing on the responsible implementation of AI/ML in financial risk management. His efforts are aimed at ensuring that AI systems in banking are not treated as black-box prediction engines but as tools that can be understood, governed, and audited.