Financial AI in Practice (A playbook for credit, fraud, and investment systems)
List Price:
$79.99
| Expected release date is Jul 28th 2026 |
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Product Details
Author:
Taehun Kim
Format:
Paperback
Pages:
375
Publisher:
Manning (July 28, 2026)
Imprint:
Manning
Release Date:
July 28, 2026
Language:
English
ISBN-13:
9781633435391
ISBN-10:
1633435393
Weight:
15.84oz
Dimensions:
7.375" x 9.25"
File:
Eloquence-SimonSchuster_04022026_P9912986_onix30_Complete-20260402.xml
List Price:
$79.99
Pub Discount:
37
Series:
In Practice
As low as:
$75.99
Publisher Identifier:
P-SS
Discount Code:
H
Folder:
Eloquence
Overview
Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
Modern finance involves crunching more data that any single human can efficiently and effectively process. Sophisticated machine learning models and applications have dominated the high-end fintech industry for decades. Now that extraordinarily powerful AI technologies are readily available to everyone, you can take advantage of deep learning, graph analytics, and large language models (LLMs) to create your own custom finance applications.
Financial AI needs to navigate rapidly changing conditions, process incredibly complex data, make split-second decisions and, of course, do it all within the restrictions of regulatory compliance. Author Taehun Kim has spent over a decade building AI systems that perform on the front lines of the finance industry.
In Financial AI in Practice you’ll learn how to:
• Build end-to-end AI pipelines for credit scoring and fraud detection
• Design hybrid strategies combining ML models and LLM-driven insights
• Expose complex fraud using graph analytics and network detection
• Architect secure, compliant generative AI with RAG techniques
• Navigate AI business strategy, ROI, and stakeholder alignment
About the book
Financial AI in Practice shows you how to deliver financial AI solutions that are more than just a few deployed algorithms. You’ll learn to build a complete, compliant application using the kind of messy, imperfect data you'll encounter in industry. The book introduces around four complete, production-minded systems that handle the core tasks of credit, fraud, investment, and operational efficiency. You’ll build an end-to-end pipeline that assesses credit risk, use supervised, unsupervised, and graph-based models to detect fraud, and combine a quantitative model with LLM-powered news analyses for a hybrid investment strategy. As you build, you’ll master Taehun’s simple-but-powerful 4-Layer Framework, a mental model you can apply to any AI project in finance.
About the reader
For data scientists, product owners, business leaders, product strategists, and financial analysts.
About the author
Taehun Kim is a Staff Data Scientist at a major NYSE-listed e-commerce company, where he spearheads fintech initiatives that process millions of transactions daily.
Modern finance involves crunching more data that any single human can efficiently and effectively process. Sophisticated machine learning models and applications have dominated the high-end fintech industry for decades. Now that extraordinarily powerful AI technologies are readily available to everyone, you can take advantage of deep learning, graph analytics, and large language models (LLMs) to create your own custom finance applications.
Financial AI needs to navigate rapidly changing conditions, process incredibly complex data, make split-second decisions and, of course, do it all within the restrictions of regulatory compliance. Author Taehun Kim has spent over a decade building AI systems that perform on the front lines of the finance industry.
In Financial AI in Practice you’ll learn how to:
• Build end-to-end AI pipelines for credit scoring and fraud detection
• Design hybrid strategies combining ML models and LLM-driven insights
• Expose complex fraud using graph analytics and network detection
• Architect secure, compliant generative AI with RAG techniques
• Navigate AI business strategy, ROI, and stakeholder alignment
About the book
Financial AI in Practice shows you how to deliver financial AI solutions that are more than just a few deployed algorithms. You’ll learn to build a complete, compliant application using the kind of messy, imperfect data you'll encounter in industry. The book introduces around four complete, production-minded systems that handle the core tasks of credit, fraud, investment, and operational efficiency. You’ll build an end-to-end pipeline that assesses credit risk, use supervised, unsupervised, and graph-based models to detect fraud, and combine a quantitative model with LLM-powered news analyses for a hybrid investment strategy. As you build, you’ll master Taehun’s simple-but-powerful 4-Layer Framework, a mental model you can apply to any AI project in finance.
About the reader
For data scientists, product owners, business leaders, product strategists, and financial analysts.
About the author
Taehun Kim is a Staff Data Scientist at a major NYSE-listed e-commerce company, where he spearheads fintech initiatives that process millions of transactions daily.
