AI in Banking: Practical Applications and Case Studies

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.

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 bank’s 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 Chongqing’s 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 bank’s 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 Commission’s Third Prize for the research project “Research and Practice of Traditional and Internet Core Dual Integration Architecture,” the People's Bank of China’s 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.