Artificial Intelligence in Banking: Revolutionizing Finance and Data Security
Artificial intelligence revolutionizes banking with fraud detection, personalized services, and secure data management, enhancing financial efficiency and trust. See latest price on Amazon.
Quick facts
Topic: Finance
Tags: Finance, Artificial Intelligence, AI Trends
Length: 286 pages
Best for: Readers who want practical, plain-English AI insights with real-world examples.
Because AI in finance affects fraud losses, compliance exposure, customer trust, and competitive positioning. Getting the basics right matters long before anyone wheels in the hype machine.
What you’ll learn
Where AI is already being used in finance today — and where the claims are running ahead of reality.
The workflows, systems, and trade-offs behind practical finance use cases, explained in plain English.
Key themes including fraud detection, risk scoring, compliance, personalisation.
The limits, risks, and awkward questions worth asking before you sign off on the sales pitch.
Who this book is for
Banking professionals, fintech founders, and financial services executives exploring AI's role in fraud detection, risk management, and customer personalisation.
What this book covers
A 286-page guide to AI in banking — from fraud detection and personalised services to secure data management and regulatory compliance.
What makes this book distinct
Banking was early to AI adoption and is now among the deepest users of machine learning in any industry — yet most of what gets written about it stays at the surface. This book cuts through to examine how fraud detection models are being retrained faster than fraudsters can adapt, how credit scoring is moving beyond traditional indicators, and where the privacy and regulatory fault lines actually sit.
Not your book? Written for banking leaders, product managers in financial services, and fintech professionals — not quants wanting mathematical model internals. The focus is on what AI actually changes about how financial institutions operate and compete.
Jonathan HarrisArtificial Intelligence Author & Host of Turing's Torch AI Weekly
Longer-form context from the retired overview page, now folded into the canonical book route.
Problem framing: where this topic gets messy
Finance rewards speed, pattern recognition, and risk control, but it also punishes hidden bias, weak controls, and brittle models. That tension sits at the centre of serious AI adoption. A 286-page guide to AI in banking — from fraud detection and personalised services to secure data management and regulatory compliance. Pages: 286. Because AI in finance affects fraud losses, compliance exposure, customer trust, and competitive positioning. Getting the basics right matters long before anyone wheels in the hype machine.
Practical outcomes
In practical terms, the aim is simple: you should gain a more sensible lens on fraud detection, personalisation, risk scoring, and data security in financial services. That means clearer judgement, fewer lazy assumptions, and a much better sense of where to press further or walk away.
Identify where ai is already being used in finance today — and where the claims are running ahead of reality.
Work through the workflows, systems, and trade-offs behind practical finance use cases, explained in plain english.
Work through key themes including fraud detection, risk scoring, compliance, personalisation.
Work through the limits, risks, and awkward questions worth asking before you sign off on the sales pitch.
Chapter-level signals
Not a chapter list carved in stone, but the sort of material readers can reasonably expect to work through.
Where AI is already being used
Where AI is already being used in finance today — and where the claims are running ahead of reality.
The workflows, systems, and trade-offs behind
The workflows, systems, and trade-offs behind practical finance use cases, explained in plain English.
Key themes including fraud detection, risk
Key themes including fraud detection, risk scoring, compliance, personalisation.
The limits, risks, and awkward questions
The limits, risks, and awkward questions worth asking before you sign off on the sales pitch.
What makes this title distinct
Banking was early to AI adoption and is now among the deepest users of machine learning in any industry — yet most of what gets written about it stays at the surface. This book cuts through to examine how fraud detection models are being retrained faster than fraudsters can adapt, how credit scoring is moving beyond traditional indicators, and where the privacy and regulatory fault lines actually sit. Not your book? Written for banking leaders, product managers in financial services, and fintech professionals — not quants wanting mathematical model internals. The focus is on what AI actually changes about how financial institutions operate and compete.
A 286-page guide to AI in banking from fraud detection and personalised services to secure data management and regulatory compliance.
Who is this book for?
Banking professionals, fintech founders, and financial services executives exploring AI's role in fraud detection, risk management, and customer personalisation.
How long is it?
It’s 286 pages (varies by edition).
What format is it available in?
Available as an eBook via Amazon (use the buy link on this page).
Keep exploring the Jonathan Harris AI library
Use the links below to carry on browsing the wider catalogue, the podcast, the newsletter, or a related topic.
