Artificial Intelligence for Risk Mitigation in the Financial Industry
Description
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.
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Persons
Ambrish Kumar Mishra, PhD, is a scholar in the School of Management at Gautam Buddha University in Greater Noida, Uttar Pradesh, India. He obtained his Master's in banking services from Amity University Noida, India in 2014, and has spent six years in the banking industry teaching and as a mutual fund and GST trainer with the BFSI sector skill council in India. He has published research papers and received various awards in his field of research.
Shweta Anand, PhD, is the dean of the School of Management at Gautam Buddha University in Greater Noida, Uttar Pradesh, India. She earned a PhD in Wealth Management and has 30+ years of experience of which 14 years were in industry. She has won several awards and accolades and has published numerous papers in international and national journals and conferences.
Narayan C. Debnath, PhD, is the Founding Dean of the School of Computing and Information Technology at Eastern International University (EIU), Vietnam where he also serves as the Head of the Department of Software Engineering. He has been the Director of the International Society for Computers and their Applications (ISCA), USA since 2014 as well as serving as a Full Professor of Computer Science at Winona State University, Minnesota, USA for 28 years. He is the author or co-author of more than 500 publications in numerous refereed journals and conference proceedings as well as the editor of several books.
Purvi Pokhariyal, PhD, is the Campus Director at the National Forensic Sciences University of the Delhi Campus, India. She has more than 25 years of academic and industry experience in the field of law and justice administration. She has published about 50 papers in national and international journals and conferences.
Archana Patel, PhD, is an assistant professor at the National Forensic Sciences University, Delhi, India. She completed her PhD in Computer Applications and a PG degree from the National Institute on Technology in Kurukshetra, India in 2020 and 2016, respectively. Dr. Patel has received various awards for her presentation of research work, and published more than 40 papers in peer-reviewed journals and conferences, as well as edited 10 books. Her research interests are in ontological engineering, semantic web, big data, expert systems, and knowledge warehouses.
Content
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
1
Artificial Intelligence in Risk Management
Pankaj Yadav, Priya Gupta, Rajeev Sijariya and Yogesh Sharma*
Atal Bihari Vajpayee School of Management and Entrepreneurship, Jawaharlal Nehru University, New Delhi, India
Abstract
The financial industry is well known for a high level of complexity in addition to a rapid rate of change; hence, it is important that effective risk management practices should be put into place. Traditional methods of risk management have many limitations, such as their inability to manage huge amounts of data, their inability to react quickly to swings in the market, and their inability to give real-time monitoring of market trends. Artificial intelligence (AI) can enhance the efficiency and effectiveness of risk management in the financial sector using deep learning, machine learning algorithms, and natural language processing. These methods can be used to ascertain the existence of potential threats, unearth fraudulent activities, and provide predictive analytics that are helpful in making decisions. The application of artificial intelligence to risk management has the potential to significantly improve decision-making and to reduce risks and raise overall financial stability. These benefits could be achieved through the use of artificial intelligence. The chapter presents an in-depth review of the potential ways in which AI could improve risk management methods in the financial industry. The chapter includes types of risks in the financial industry with the light on the various advantages that artificial intelligence could bring to mitigate this risk. These advantages include the capacity to analyze huge volumes of data and the flexibility to respond to altering market conditions. The chapter will also discuss real-time monitoring of market trends as well as alerts for potential risks, different tools of artificial intelligence make it possible for businesses to proactively manage the risks to which they are exposed. This chapter will provide an insight into the opportunities and limitations and ethical challenges of this technology by providing the tools and methodologies that are used in AI-based risk management.
Keywords: Artificial intelligence, machine learning, risk management, sentiment analysis, predictive analysis
1.1 Introduction
The financial industry operates within a dynamic and intricate environment that is characterized by complicated transactions, volatile markets, and regulatory limits. This environment is necessary for the sector to function effectively. In recent years, there have been significant shifts in the global economy as a result of technology upheavals, economic uncertainties, and evolving geopolitical landscapes. These factors have shaped these changes. It is essential to be aware that the financial sector makes a considerable contribution to the economy of the entire world, accounting for approximately 7%-8% of the total gross domestic product (GDP) of the entire world1. The contribution of India's financial sector to the country's GDP has been gradually expanding in recent years, reaching approximately 7.5% of the total2.
The overall market capitalization of the global financial markets is measured in the trillions of dollars. These markets are enormous. For illustration, the New York Stock Exchange alone had a market capitalization of more than $19 trillion3. On the other hand, throughout the course of the past few years, the stock market in India has experienced a substantial amount of expansion. The overall market capitalization of the Bombay Stock Exchange (BSE) was close to $3 trillion [1].
The global banking industry is controlled by significant businesses based in a variety of geographic locations. To give just one illustration, the 10 largest banks in the world collectively have assets that are worth trillions of dollars. According to S&P Global Market Intelligence, the banking industry in India is made up of a combination of public sector, private sector, and international banks. The State Bank of India (SBI), which is India's most prominent financial institution, has assets worth more than 600 billion dollars in total (Information obtained from the State Bank of India).
When the economy of India is compared to the economy of the world as a whole, it becomes abundantly evident that both must contend with the presence of a unique set of challenges. Following the global economic crisis of 2008, governments and financial institutions in every region of the world came to the realization that they needed to do a better job of risk management.
As a direct consequence of this, artificial intelligence (AI) is being applied in an increasing number of risk assessment methodologies, leading to the creation of risk models that are more accurate. The major global financial centers of New York, London, and Hong Kong have been at the forefront of the use of artificial intelligence for risk management. These major global financial centers have been utilizing AI's capabilities to minimize systemic risks, handle credit and market risks, and combat financial crime. The way risk management is carried out in these spheres has been revolutionized by AI, which has enabled financial institutions to better keep up with the rapid shifts that are occurring in the market and in the rules.
It has become essential for financial institutions all over the world to include AI into their risk management processes. This gives these institutions the ability to negotiate the intricacies and difficulties connected with modern banking. Real-time monitoring, predictive analytics, and the ability to automate decision-making are just a few of the benefits offered by risk management systems that are powered by artificial intelligence. These technologies give financial institutions the ability to recognize possible hazards, spot irregularities, and react quickly to newly emerging dangers, thereby boosting their capacity to protect investments and keep operations steady.
1.1.1 Context and the Driving Force Behind It
For the purpose of guaranteeing financial stability, protecting investments, and defending the interests of stakeholders, effective risk management is an absolute necessity. Traditional techniques of risk management, on the other hand, have a difficult time keeping up with the volumes of data that need to be managed, responding quickly to fluctuations in the market, and providing real-time monitoring of market trends. In this chapter, these constraints are discussed, and an investigation of the potential of artificial intelligence to improve risk management in the financial sector is conducted.
1.1.2 Aim of This Chapter
To provide an in-depth understanding of how AI can improve risk management practices in the financial industry is the primary purpose of this chapter. The goals of this chapter are to:
- Explain what artificial intelligence is and how it relates to risk management.
- Engage in a discussion on the shortcomings of conventional approaches to risk management.
- Investigate the potential applications of a variety of AI-based methodologies, including deep learning, machine learning algorithms, and natural language processing, in the context of risk management.
- Discuss the difficulties and factors to consider when putting artificial intelligence into risk management systems.
- Explain the benefits of artificial intelligence, such as its capacity to process large amounts of data and adapt quickly to shifting market conditions.
- Give some background information on the approaches and tools that are utilized in AI-based risk management.
- Discuss the restrictions, obstacles, and ethical concerns that are involved with the use of AI in risk management.
- As a last step, provide a high-level summary of the potential effects that AI could have on decision-making, risk reduction, and overall financial stability.
1.1.3 Outline of This Chapter
This chapter is structured as follows: Section 1.2 provides an overview of risk management in the financial sector. Section 1.3 discusses the role of artificial intelligence in risk management. Section 1.4 addresses the issues that arise when implementing AI-based risk management systems. Section 1.5 highlights the advantages of utilizing artificial intelligence in risk management. Section 1.6 delves into the methodologies and tools available for AI-based risk management. Section 1.7 examines the limitations and key considerations associated with AI-based risk management. Finally, Section 1.8 concludes the chapter, summarizing the main points discussed throughout.
1.2 The Role of AI in Risk Management
1.2.1 The Significance of Risk Management
Risk management is very important in the financial industry, which is subject to numerous different sorts of risks, such as market risk, credit risk, liquidity risk, operational risk, and regulatory compliance risk [2]. Institutions in this industry are susceptible to all of these types of risks. It is possible for financial institutions to identify, evaluate, and mitigate risks through effective risk management, which in turn ensures the stability and resilience of the institutions' operational processes. It entails the formulation and execution of plans, policies, and procedures with the purpose of proactively managing risks while simultaneously optimizing returns on investments [3].
In a nutshell, risk management is important...
