January 2026
Intermediate to advanced
336 pages
10h 18m
English
Content preview from Deep Learning in Banking
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Chapter 9Perspectives on the Future of AI in Banking
In the previous chapter, we have finished covering what we believe are the main topics that a user with an interest in deep learning for banks would need to know to get started. Of course, much more can be said. Many of the sections in this book could be books of their own, along with certainly chapters in this book. We are sure that topics in agentic AI (section 6.6) will continue to be in vogue in the foreseeable future. We are also sure that there will be significant disappointment as some applications of the technology fail to materialize into true value.
It has been famously said that around 50% of all data science projects fail at the deployment stage. A recent McKinsey report said that a significant number (∼90%) of generation AI (GenAI) projects get stuck in the pilot project stage (McKinsey & Company, 2025). The future of AI in banking will be mostly tied to one thing: value. This chapter deals with some of the problems in value generation, starting from what it takes for a project to get off the ground in the first place, evaluating return on investment ...
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