Deepu Komati, a technology professional specializing in AI, ML, and analytics has released his new book “AI & Machine Learning in Banking: Models, Systems, and Real-World Financial Applications.” The book explores how AI and ML are being used across banking to improve risk assessment, strengthen fraud detection, personalize customer engagement, support compliance functions, and move institutions toward more data-driven operations.

As banks continue to modernize their technology and decision-making systems, AI is increasingly shifting from a future-facing concept to a practical business capability. The book frames that shift in clear operational terms, showing how financial institutions can move beyond rule-based systems and historical reporting toward intelligent systems that learn from data, identify patterns at scale, and support faster, more adaptive decisions.

The book covers a broad range of banking applications, including credit risk and loan underwriting, fraud detection, customer analytics and personalization, algorithmic trading, natural language processing, anti-money laundering, deep learning, model deployment, and responsible AI. It is organized to help readers understand both the technical foundations and the practical realities of implementing these systems in business environments.

“This book was written to make AI in banking more practical and more understandable for the people responsible for building, evaluating, and leading these systems,” said Deepu Komati. “There is no shortage of discussion around AI, but financial institutions need grounded guidance on how to apply it in ways that are useful, scalable, and responsible.”

The book is intended for a broad professional audience, including data scientists, machine learning engineers, software architects, banking professionals, product leaders, and risk and compliance stakeholders. Rather than presenting AI in banking as a purely theoretical subject, it emphasizes practical applications, implementation approaches, real-world examples, and production considerations.

Among the core topics covered in the book are:

1. Machine learning fundamentals for banking
2. Credit risk modeling and underwriting
3. Fraud detection and real-time monitoring
4. Customer analytics and personalization
5. Chatbots and NLP applications
6. Anti-money laundering analytics
7. Model deployment, monitoring, and MLOps
8. Bias, fairness, privacy, and responsible AI in financial services

The book ultimately presents AI in banking not as a standalone technology trend, but as part of a larger transformation in how financial institutions operate, compete, and serve customers. Its central message is that long-term success with AI depends not only on model performance, but also on thoughtful implementation, regulatory awareness, and alignment with real business outcomes.

AI & Machine Learning in Banking: Models, Systems, and Real-World Financial Applications is available now on Amazon.