This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability.
The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc.
Audience
This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.
Table of Contents
Preface xvii
1 Artificial Intelligence in Risk Management 1
Pankaj Yadav, Priya Gupta, Rajeev Sijariya and Yogesh Sharma
2 Application of Artificial Intelligence in Risk Assessment and Mitigation in Banks 27
Ankita Srivastava, Bhartrihari Pandiya and Navtika Singh Nautiyal
3 Artificial Intelligence and Financial Risk Mitigation 53
Raja Rehan, Auwal Adam Sa'ad and Razali Haron
4 Artificial Intelligence Adoption in the Indian Banking and Financial Industry: Current Status and Future Opportunities 81
Deepthi B. and Vikram Bansal
5 Impact of AI Adoption in Current Trends of the Financial Industry 103
S. C. Vetrivel, T. Mohanasundaram, T. P. Saravanan and R. Maheswari
6 Artificial Intelligence Applications in the Indian Financial Ecosystem 133
Vijaya Kittu Manda and Khaliq Lubza Nihar
7 The Extraction of Features That Characterize Financial Fraud Behavior by Machine Learning Algorithms 159
George X. Yuan, Yuanlei Luo, Lan Di, Yunpeng Zhou, Wen Chen, Yiming Liu and Yudi Gu
8 A New Surge of Interest in the Cybersecurity of VIP Clients is the First Step Toward the Return of the Previously Used Positioning Practice in Domestic Private Banking 187
Gusev Alexey
9 Determinants of Financial Distress in Select Indian Asset Reconstruction Companies Using Artificial Neural Networks 209
Shashank Sharma and Ajay Kumar Kansal
10 The Framework of Feature Extraction for Financial Fraud Behavior and Applications 229
George X. Yuan, Shanshan Yang, Lan Di, Yunpeng Zhou, Wen Chen and Yuanlei Luo
11 Real-Time Analysis of Banking Data with AI Technologies 261
S. C. Vetrivel, T. Mohanasundaram, T. P. Saravanan and R. Maheswari
12 Risks in Amalgamation of Artificial Intelligence with Other Recent Technologies 289
K. Sathya and A. Hency Juliet
13 Exploring the Role of ChatGPT in the Law Enforcement and Banking Sectors 327
Shubham Pandey, Archana Patel and Purvi Pokhariyal
References 345
Index 349
