The new book offers practical guidance on credit risk, model governance, explainable AI, compliance and MLOps.
Louisville, Kentucky, United States, 2nd Oct 2026 - Saurabh Kakkar announces the release of Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks, a new AI & Machine Learning genre book designed for professionals working with credit risk, banking, data, technology, compliance and financial regulation. Published by Notion Press on August 11, 2026, the book is now available on Amazon.
Bringing AI/ML Into Real-World Banking
A comprehensive look at how banks might plan, develop, implement, and oversee AI/ML systems in regulated financial contexts can be found in Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks. Rather than treating AI as a simple technology experiment, the book focuses on how these systems can work within real banking processes.
The AI & Machine Learning book covers the complete AI/ML lifecycle. It explores credit risk modeling, data engineering, feature engineering, model development, explainable AI, model validation, deployment and MLOps. It also explains why data quality, data lineage, monitoring, governance and audit readiness matter when AI is used for important financial decisions.
Connecting Innovation With Regulation
Banks must be able to defend and explain significant choices since they are subject to stringent regulations. The book links the development of AI/ML with important regulatory and risk-management requirements, including Basel III, CRR, IFRS 9 and SR 11-7.
With a strong focus on regulated banking, the book also discusses fairness, bias mitigation, responsible AI, model governance & human oversight. It shows how financial institutions can work toward AI systems that are transparent, scalable, controlled and ready for review by management, validators, auditors and regulators.
The book also looks at emerging technologies and ideas, including Generative AI, federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing and quantum computing. These topics give readers a view of how AI/ML may continue to shape financial risk management.
A Practitioner’s View of AI/ML
Saurabh Kakkar brings more than 15 years of experience across global banking, professional services, financial risk, regulatory transformation, enterprise data and technology change. His professional work includes credit risk, Basel/CRR, IFRS 9, responsible AI, enterprise data engineering, model governance, RegTech and regulatory analytics.
His experience is central to this AI & Machine Learning book. Kakkar wrote it to help close the gap between AI/ML theory and enterprise implementation. His approach focuses on practical systems that can operate responsibly inside regulated financial institutions.
Kakkar holds an MBA from Fordham University’s Gabelli School of Business in New York, a Diploma in Business Management from ICFAI and a Bachelor of Commerce from the University of Delhi. His independent professional work also comprises two registered German utility models, nine published scholarly publications, peer-review and innovation-judging assignments, conference speaking and expert research.
Why the Book Was Written
Saurabh Kakkar’s view is that AI in banking should not be treated as a black-box prediction engine. An effective system must be accurate, explainable, governable, auditable and able to fit into real-world risk, finance, data and regulatory processes.
This AI & Machine Learning book was shaped by Kakkar’s years of working with regulatory reporting, data-quality challenges, model governance, credit-risk analytics and large technology transformations. These experiences showed him that strong AI systems require more than good predictions. They also need controls, traceability, validation, monitoring & human oversight.
Who Can Benefit From the Book?
Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks is written 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.
The AI & Machine Learning book is especially useful for experienced professionals and graduate-level readers who want to understand how AI/ML can be implemented in credit risk while maintaining regulatory discipline and responsible practices.
Availability
Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks is now available on Amazon in paperback and hardcover editions.
Readers can also connect with Saurabh Kakkar on Instagram & Facebook.
About Saurabh Kakkar
Saurabh Kakkar is a senior AI, data and financial-risk transformation executive with more than 15 years of experience in global banking and professional services. Through his work and research, he focuses on turning advanced AI/ML and analytics into explainable, governed and scalable solutions for regulated financial institutions.
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Saurabh Kakkar
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