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Artificial Intelligence for Risk Mitigation in the Financial Industry

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  • Дата: 2-06-2024, 16:48
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Название: Artificial Intelligence for Risk Mitigation in the Financial Industry
Автор: Ambrish Kumar Mishra, Shweta Anand, Narayan C. Debnath
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 384
Язык: английский
Формат: epub (true)
Размер: 10.8 MB

This book extensively explores the implementation of Artificial Intelligence (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 Artificial Intelligence 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. There is no closely related study, or a smaller number of studies, being published to help mitigate the risk in the financial industry with AI. Hence, there is a gap that inspires researchers to develop a strong foundation for prospective research that will benefit industries across the globe.

This is an introductory book that provides insights on 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 government. By extensively exploring the implementation of AI in the risk mitigation process enhances the effectiveness and reliability of the process. 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.

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 (NLP). 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.

General Applications of ChatGPT: ChatGPT, as a language generation model, has made inroads in various human-oriented tasks and has completely automated many of them. This includes (a) Text Generation: ChatGPT can be used to generate human-like text on a given topic, making it useful for content creation, such as writing articles, stories, and poetry. (b) Language Translation: ChatGPT can be fine-tuned for language translation tasks; it can translate text from one language to another. (c) Text Summarization: ChatGPT can be used to summarize long documents or articles, making it useful for quickly getting the main points of a piece of text. (d) Question Answering: By fine-tuning the ChatGPT model, it can be used to answer questions based on a given context and provide relevant information. (e) Conversational AI: ChatGPT can be used to create chatbots, which can have natural conversations with users. It can be used to generate responses to user inputs, making the chatbot more engaging and human-like. (f) Sentiment Analysis: ChatGPT can be fine-tuned for sentiment analysis tasks; it can classify the text into positive, negative, and neutral. (g) Text Completion: ChatGPT can be used to complete a given text prompt. (h) Text Classification: ChatGPT can be fine-tuned for text classification tasks; it can classify the text into different categories.

Audience:
This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of Artificial Intelligence 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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