Artificial Intelligence For Risk Mitigation In The Financial Industry

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Artificial Intelligence for Risk Mitigation in the Financial Industry

Ambrish Kumar Mishra,Shweta Anand,Narayan C. Debnath,Purvi Pokhariyal,Archana Patel
Artificial Intelligence for Risk Mitigation in the Financial Industry Book PDF

Author : Ambrish Kumar Mishra,Shweta Anand,Narayan C. Debnath,Purvi Pokhariyal,Archana Patel
Publisher : John Wiley & Sons
Page : 388 pages
File Size : 42,6 Mb
Release : 2024-05-29
Category : Computers
ISBN : 9781394175550

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Artificial Intelligence for Risk Mitigation in the Financial Industry by Ambrish Kumar Mishra,Shweta Anand,Narayan C. Debnath,Purvi Pokhariyal,Archana Patel Pdf

Artificial Intelligence for Risk Mitigation in the Financial Industry 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.

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance Book PDF

Author : El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Publisher : International Monetary Fund
Page : 35 pages
File Size : 53,8 Mb
Release : 2021-10-22
Category : Business & Economics
ISBN : 9781589063952

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Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar Pdf

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Risk Modeling

Terisa Roberts,Stephen J. Tonna
Risk Modeling Book PDF

Author : Terisa Roberts,Stephen J. Tonna
Publisher : John Wiley & Sons
Page : 214 pages
File Size : 47,7 Mb
Release : 2022-09-20
Category : Business & Economics
ISBN : 9781119824947

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Risk Modeling by Terisa Roberts,Stephen J. Tonna Pdf

A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.

Artificial Intelligence and Big Data for Financial Risk Management

Noura Metawa,M. Kabir Hassan,Saad Metawa
Artificial Intelligence and Big Data for Financial Risk Management Book PDF

Author : Noura Metawa,M. Kabir Hassan,Saad Metawa
Publisher : Taylor & Francis
Page : 249 pages
File Size : 51,7 Mb
Release : 2022-08-26
Category : Business & Economics
ISBN : 9781000645279

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Artificial Intelligence and Big Data for Financial Risk Management by Noura Metawa,M. Kabir Hassan,Saad Metawa Pdf

This book presents a collection of high-quality contributions on the state-of-the-art in Artificial Intelligence and Big Data analysis as it relates to financial risk management applications. It brings together, in one place, the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use-cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the internet of things in a marketing environment.

Disrupting Finance

Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Disrupting Finance Book PDF

Author : Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Publisher : Springer
Page : 194 pages
File Size : 46,8 Mb
Release : 2018-12-06
Category : Business & Economics
ISBN : 9783030023300

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Disrupting Finance by Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins Pdf

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.

Generative Artificial Intelligence in Finance

Mr. Ghiath Shabsigh,El Bachir Boukherouaa
Generative Artificial Intelligence in Finance Book PDF

Author : Mr. Ghiath Shabsigh,El Bachir Boukherouaa
Publisher : International Monetary Fund
Page : 24 pages
File Size : 48,5 Mb
Release : 2023-08-22
Category : Business & Economics
ISBN : 9798400251092

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Generative Artificial Intelligence in Finance by Mr. Ghiath Shabsigh,El Bachir Boukherouaa Pdf

In recent years, technological advances and competitive pressures have fueled rapid adoption of artificial intelligence (AI) in the financial sector, and this adoption is set to accelerate with the recent emergence of generative AI (GenAI). GenAI is a significant leap forward in AI technology that enhances its utility for financial institutions that have been quick at adapting it to a broad range of applications. However, there are risks inherent in the AI technology and its application in the financial sector, including embedded bias, privacy concerns, outcome opaqueness, performance robustness, unique cyberthreats, and the potential for creating new sources and transmission channels of systemic risks. GenAI could aggravate some of these risks and bring about new types or risks as well, including for financial sector stability. This paper provides early insights into GenAI’s inherent risks and their potential impact on the financial sector.

Artificial Intelligence and Islamic Finance

Adel M. Sarea,Ahmed H. Elsayed,Saeed A. Bin-Nashwan
Artificial Intelligence and Islamic Finance Book PDF

Author : Adel M. Sarea,Ahmed H. Elsayed,Saeed A. Bin-Nashwan
Publisher : Routledge
Page : 180 pages
File Size : 40,6 Mb
Release : 2021-12-31
Category : Business & Economics
ISBN : 9781000528121

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Artificial Intelligence and Islamic Finance by Adel M. Sarea,Ahmed H. Elsayed,Saeed A. Bin-Nashwan Pdf

This book provides a systematic overview of the current trends in research relating to the use of artificial intelligence in Islamic financial institutions (IFIs), across all organization of Islamic cooperation (OIC) countries. Artificial Intelligence and Islamic Finance discusses current and potential applications of artificial intelligence (AI) for risk management in Islamic finance. It covers various techniques of risk management, encompassing asset and liability management risk, credit, market, operational, liquidity risk, as well as regulatory and Shariah risk compliance within the financial industry. The authors highlight AI’s ability to combat financial crime such as monitoring trader recklessness, anti-fraud and anti-money laundering, and assert that the capacity of machine learning (ML) to examine large amounts of data allows for greater granular and profound analyses across a variety of Islamic financial products and services. The book concludes with practical limitations around data management policies, transparency, and lack of necessary skill sets within financial institutions. By adopting new methodological approaches steeped in an Islamic economic framework (e.g., analysing FinTech in the context of Shariah principles and Islamic values), it devises practical solutions and generates insightful knowledge, helping readers to understand and explore the role of technological enablers in the Islamic finance industry, such as RegTech and artificial intelligence, in providing better and Shariah-compliant services to customers through digital platforms. The book will attract a wide readership spanning Shariah scholars, academicians, and researchers as well as Islamic financial practitioners and policy makers.

Artificial Intelligence in Asset Management

Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Artificial Intelligence in Asset Management Book PDF

Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
Page : 95 pages
File Size : 45,9 Mb
Release : 2020-08-28
Category : Business & Economics
ISBN : 9781952927034

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Artificial Intelligence in Asset Management by Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari Pdf

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Artificial Intelligence and Credit Risk

Rossella Locatelli,Giovanni Pepe,Fabio Salis
Artificial Intelligence and Credit Risk Book PDF

Author : Rossella Locatelli,Giovanni Pepe,Fabio Salis
Publisher : Springer Nature
Page : 115 pages
File Size : 46,9 Mb
Release : 2022-09-13
Category : Business & Economics
ISBN : 9783031102363

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Artificial Intelligence and Credit Risk by Rossella Locatelli,Giovanni Pepe,Fabio Salis Pdf

This book focuses on the alternative techniques and data leveraged for credit risk, describing and analysing the array of methodological approaches for the usage of techniques and/or alternative data for regulatory and managerial rating models. During the last decade the increase in computational capacity, the consolidation of new methodologies to elaborate data and the availability of new information related to individuals and organizations, aided by the widespread usage of internet, set the stage for the development and application of artificial intelligence techniques in enterprises in general and financial institutions in particular. In the banking world, its application is even more relevant, thanks to the use of larger and larger data sets for credit risk modelling. The evaluation of credit risk has largely been based on client data modelling; such techniques (linear regression, logistic regression, decision trees, etc.) and data sets (financial, behavioural, sociologic, geographic, sectoral, etc.) are referred to as “traditional” and have been the de facto standards in the banking industry. The incoming challenge for credit risk managers is now to find ways to leverage the new AI toolbox on new (unconventional) data to enhance the models’ predictive power, without neglecting problems due to results’ interpretability while recognizing ethical dilemmas. Contributors are university researchers, risk managers operating in banks and other financial intermediaries and consultants. The topic is a major one for the financial industry, and this is one of the first works offering relevant case studies alongside practical problems and solutions.

Artificial Intelligence in Financial Markets

Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos
Artificial Intelligence in Financial Markets Book PDF

Author : Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos
Publisher : Springer
Page : 349 pages
File Size : 51,5 Mb
Release : 2016-11-21
Category : Business & Economics
ISBN : 9781137488800

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Artificial Intelligence in Financial Markets by Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos Pdf

As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Artificial Intelligence in Financial Services and Banking Industry

Dr. V.V.L.N. Sastry
Artificial Intelligence in Financial Services and Banking Industry Book PDF

Author : Dr. V.V.L.N. Sastry
Publisher : Idea Publishing
Page : 87 pages
File Size : 44,8 Mb
Release : 2020-03-20
Category : Business & Economics
ISBN : 8210379456XXX

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Artificial Intelligence in Financial Services and Banking Industry by Dr. V.V.L.N. Sastry Pdf

In the last couple of years, the finance and banking sectors have increasingly deployed and implemented Artificial Intelligence (AI) technologies. AI and machine learning are being rapidly adopted for a range of applications for front-end and back end processes to both business and financial management operations. Thus, it is quite significant to consider the financial stability repercussions of such uses. Since AI is relatively new, the data on the usage is largely unavailable, any analysis may be necessarily considered Preliminary1 . Some of the current and potential use cases of AI and machine learning in the finance sector include the following.  Institutions use AI and machine learning methods to optimize scarce capital, back-test models, and analyze the market impact of trading large positions.  Financial institutions and vendors use AI and machine learning techniques to evaluate credit quality for market and price insurance contracts, and to automate client interaction.  Brokers, hedge funds, and other firms are using AI and machine learning to find pointers for higher (and uncorrelated) returns to optimize trading execution.  Private and public sector institutions use these technologies for data quality assessment, surveillance, regulatory compliance, and fraud detection. This book seeks to map the use of AI in current state of affairs in the banking and financial sector. By doing so, it explores:  The present uses of AI in banking and finance and its narrative across the globe.

Artificial Intelligence in Banking

Introbooks
Artificial Intelligence in Banking Book PDF

Author : Introbooks
Publisher : Unknown
Page : 50 pages
File Size : 40,8 Mb
Release : 2020-04-07
Category : Electronic
ISBN : 9798634736815

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Artificial Intelligence in Banking by Introbooks Pdf

In these highly competitive times and with so many technological advancements, it is impossible for any industry to remain isolated and untouched by innovations. In this era of digital economy, the banking sector cannot exist and operate without the various digital tools offered by the ever new innovations happening in the field of Artificial Intelligence (AI) and its sub-set technologies. New technologies have enabled incredible progression in the finance industry. Artificial Intelligence (AI) and Machine Learning (ML) have provided the investors and customers with more innovative tools, new types of financial products and a new potential for growth.According to Cathy Bessant (the Chief Operations and Technology Officer, Bank of America), AI is not just a technology discussion. It is also a discussion about data and how it is used and protected. She says, "In a world focused on using AI in new ways, we're focused on using it wisely and responsibly."

Alternative Data and Artificial Intelligence Techniques

Qingquan Tony Zhang,Beibei Li,Danxia Xie
Alternative Data and Artificial Intelligence Techniques Book PDF

Author : Qingquan Tony Zhang,Beibei Li,Danxia Xie
Publisher : Springer Nature
Page : 340 pages
File Size : 53,8 Mb
Release : 2022-10-31
Category : Business & Economics
ISBN : 9783031116124

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Alternative Data and Artificial Intelligence Techniques by Qingquan Tony Zhang,Beibei Li,Danxia Xie Pdf

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

Machine Learning in Banking Risk Management

Mourine Atsien
Machine Learning in Banking Risk Management Book PDF

Author : Mourine Atsien
Publisher : GRIN Verlag
Page : 17 pages
File Size : 54,8 Mb
Release : 2023-02-20
Category : Business & Economics
ISBN : 9783346814227

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Machine Learning in Banking Risk Management by Mourine Atsien Pdf

Essay from the year 2022 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: A, , course: Business and technology, language: English, abstract: Technological applications are playing a more influential role in management in the contemporary business environment. Machine learning, artificial intelligence, and other algorithmic applications are some of the most common influencers in business applications. They present numerous solutions to business management problems, including banking risk management. In the last decade, risk management has gained greater prominence in financial services. In the past, banks focused on the detection, measuring, and reporting of risks. However, they are now leveraging on machine learning for greater accuracy and efficacy in risk management. As such, this paper explored different ways that machine learning applies in banking risk management. To achieve the objective of this study, the researcher conducted a comprehensive literature review on the topic of machine learning in banking risk management. The researcher found considerable industry and academic research focusing on developments in the financial services industry, especially in relation to risk management. It reviewed the literature, analysing and evaluating various risk management machine-learning techniques. It identified risk management problem areas and explored various ways of addressing them. The review showed that machine learning learning in risk management in financial services sector was still under-researched. While there were many studies on credit risks, other risks such as liquidity risks, market risks, and operational risks saw minimal attention. Nevertheless, machine learning applications were found to have the potential to develop more effective risk management models. Machine learning is leveraged on different data types to predict potential events with greater accuracy and estimate losses associated with different risk types. In addition, the machine learning techniques in risk management were found to provide better and more accurate results than traditional statistical models. Though machine learning suggests improving banking risk management, there are some areas that need further study. For instance, the paper suggested in-depth studies on machine learning models for different types of banking risks.

The Essentials of Machine Learning in Finance and Accounting

Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin
The Essentials of Machine Learning in Finance and Accounting Book PDF

Author : Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin
Publisher : Routledge
Page : 275 pages
File Size : 48,7 Mb
Release : 2021-06-20
Category : Business & Economics
ISBN : 9781000394122

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The Essentials of Machine Learning in Finance and Accounting by Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin Pdf

This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.