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AI in banking = practical applications and case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI in banking/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Banks and banking - Technological innovations. -
Online resource:	https://doi.org/10.1007/978-981-96-3837-6
ISBN:	9789819638376

AI in banking = practical 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

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

862622
Banks and banking
--Technological innovations.

LC Class. No.: HG1709

Dewey Class. No.: 332.10285

AI in banking = practical applications and case studies /
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505 0 $a 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.
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.
650 0 $a Banks and banking $x Technological innovations. $3 862622
650 0 $a Artificial intelligence $x Financial applications. $3 3493836
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Computer Vision. $3 3538524
650 2 4 $a Natural Language Processing (NLP). $3 3755514
650 2 4 $a Biometrics. $3 898232
650 2 4 $a Python. $3 3201289
700 1 $a Chen, Qin. $3 3171850
700 1 $a He, Min. $3 1914338
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856 4 0 $u https://doi.org/10.1007/978-981-96-3837-6
950 $a Computer Science (SpringerNature-11645)