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AI in bankingpractical applications and case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI in bankingby Liyu Shao, Qin Chen, Min He.
Reminder of title:	practical applications and case studies /
Author:	Shao, Liyu.
other author:	Chen, Qin.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xxii, 354 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Banks and bankingTechnological innovations.
Online resource:	https://doi.org/10.1007/978-981-96-3837-6
ISBN:	9789819638376$q(electronic bk.)

AI in bankingpractical applications and case studies /
Shao, Liyu.

AI in bankingpractical applications and case studies /[electronic resource] :by Liyu Shao, Qin Chen, Min He. - Singapore :Springer Nature Singapore :2025. - xxii, 354 p. :ill. (some col.), digital ;24 cm.

Part I: Smart Marketing -- Chapter 1. Mobile Banking Potential Monthly Active Customer Mining: Automated Machine Learning Techniques -- Chapter 2. Retail Potential High-value Customer Identification: Graph Neural Network Technology -- Chapter 3. Accurate Recommendation for Banking: Recommender System -- Chapter 4. Assessing the Value of Bank Online Marketing Posts: Reinforcement Learning Techniques -- Chapter 5: Modeling Binary Causal Effects of Related Repayments: Causal Inference Techniques -- Part II: Intelligent Risk Control -- Chapter 6. Telecom Fraud Money Laundering Account Recognition Case: Multiple Machine Learning Techniques -- Chapter 7. Developing a Dialectal Speech Phone Collection Bimodal Robot from Scratch: Intelligent Voice Q&A Technology -- Chapter 8. Chattel Collateral Warehouse Visual Monitoring Project: Image Understanding Technology -- Chapter 9. Personal Loan Delinquency Prediction Project: Bayesian Network Techniques -- Part III: Intelligent Operation -- Chapter 10. Enterprise WeChat Private Traffic Customer Cold Start Program: Automated Control Technology -- Chapter 11 Intelligent Inspection Robot for Commercial Bank Data Centers: Computer Vision Technology.

Big data and artificial intelligence (AI) cannot remain limited to academic theoretical research. It is crucial to utilize them in practical business scenarios, enabling cutting-edge technology to generate tangible value. This book delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. It provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, Bayesian networks, edge computing, and more. This book stands as a rare and practical guide to AI projects in the banking industry. By avoiding complex mathematical formulas and theoretical analyses, it uses plain language to illustrate how to apply AI technology in commercial banking business scenarios. With its strong readability and practical approach, this book enables readers to swiftly develop their own AI projects.

ISBN: 9789819638376$q(electronic bk.)

Standard No.: 10.1007/978-981-96-3837-6doiSubjects--Topical Terms:

365530
Banks and banking
--Technological innovations.

LC Class. No.: HG1709

Dewey Class. No.: 332.10285

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520 $a Big data and artificial intelligence (AI) cannot remain limited to academic theoretical research. It is crucial to utilize them in practical business scenarios, enabling cutting-edge technology to generate tangible value. This book delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. It provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, Bayesian networks, edge computing, and more. This book stands as a rare and practical guide to AI projects in the banking industry. By avoiding complex mathematical formulas and theoretical analyses, it uses plain language to illustrate how to apply AI technology in commercial banking business scenarios. With its strong readability and practical approach, this book enables readers to swiftly develop their own AI projects.
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