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.
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.
Shao Liyu is a senior banking technology expert with over 30 years of experience in banking technology. He possesses extensive expertise in managing large-scale banking IT projects and architectural planning of large projects. He has made significant contributions in the fields of big data assets, data element markets, and artificial intelligence. Mr. Shao has led numerous major IT projects for commercial banks and has received multiple prestigious awards. He is the author of Research and Practice of Big Data Governance in Commercial Banks and has published several papers in authoritative journals, including Construction and Practice of Bank Big Data Risk Control Capability and Analysis of Core Data Capabilities in Commercial Bank Data Governance.
Chen Qin is a banking technology expert with over 23 years of industry experience. He is currently serving as Deputy General Manager of the Information Technology Department at a commercial bank branch. He was honored as one of the banks inaugural Top 10 Technology Stars. He is a researcher at the Chongqing Branch of the National New-Type Crime Research Center and a member of the Financial Technology Working Group in Chongqings Anti-Money Laundering Talent Pool. Specializing in data intelligence, computer vision, recommendation systems, natural language understanding, and knowledge graphs, he has 10 years of AI application development experience in a major commercial bank. Her independently developed banking AI projects include End-to-End AI Applications in Financial Consumer Complaint Management, Intelligent Conference Behavior Management System, AI-Powered Telecom Fraud Account Detection Model, AR-Based Interactive Financial Scenarios, High-Value Customer Mining Based on Social Network Analysis, and Intelligent Financial Scene Text Recognition. These projects have earned her the banks First Prize in Software Development, First Prize in Big Data Innovation, Second Prize in the 2021 Chongqing Banking Association Outstanding Research Project, Chongqing Financial Data Comprehensive Pilot Project, and Third Prize in Chongqing's 2019 Financial Technology Research. He has published multiple academic papers, including Graph Neural Networks in Banking Marketing and Risk Control Applications, The Middle Way to Resolve Banking Technology Practical Contradictions, and Analysis of the Disconnect and Integration Between Bank IT and Business Operations.
He Min is a senior banking architect with a decade of experience in core banking project development. He specializes in banking application architecture planning and has conducted extensive research in blockchain, artificial intelligence, and big data domains. He has led multiple digital innovation projects in financial scenarios and participated in numerous provincial-level key research initiatives. His notable achievements include receiving the Banking and Insurance Regulatory Commissions Third Prize for the research project Research and Practice of Traditional and Internet Core Dual Integration Architecture, the People's Bank of Chinas Third Prize for Robotic Process Automation and AI Applications in Bank Operations Data Management, and an Excellence Award for Research on National Cryptographic Standards Promoting Financial Information Security in the National Financial Standardization Research program. His paper Technical Innovation and Optimization Practices in Core Banking Systems was published in Financial Technology Time magazine.
