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.

Artificial intelligence (AI) systems are machine-based systems with varying degrees of autonomy that generate predictions, suggestions, or judgements for a set of humanspecified goals utilizing enormous numbers of alternative data sources and data analytics referred to as "big data"3 (OECD, 2019). Artificial intelligence (AI) systems are sometimes referred to as intelligent machines. Systems that are powered by artificial intelligence (AI) are increasingly finding applications in a wide range of fields, including the medical field, the financial sector, and the armed forces. Common synonyms for artificial intelligence (AI) include "machine learning" and "machine learning systems." An explanation of artificial intelligence may be found in the following: "Artificial intelligence systems" are defined by the Oxford English Dictionary as "machine-based systems that can exhibit varying degrees of autonomy." You may learn more about this concept by reading the page titled "Artificial Intelligence Systems." Once they have access to the data, the models have the potential to "self-improve" by inferentially learning from further data sets without the need for human instruction. This may occur if they learn to draw conclusions from other data sets via inference. The acceleration and strengthening of a tendency toward digitalization that was already apparent before the pandemic is a direct outcome of the spread of the COVID-19 virus. This trend was already clear before the epidemic. Utilization of artificial intelligence is included in this trend. The abundance of data that is already available, in addition to the advancements in computer capacity that have made computers both more affordable and more powerful, have made it possible for artificial intelligence to play an increasingly important role in the financial sector. This role can be seen in asset management, algorithmic trading, credit underwriting, and blockchain-based financial services, among other applications. AI4 is integrated into goods and services across a wide range of sectors, including healthcare, autos, consumer items, and the phrase "internet of things" is abbreviated as "IoT."

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.

This open access book presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTechs, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also introduces some of the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance. Introduces the latest advances in Big Data and AI in Digital Finance that enable scalable, effective, and real-time analytics; Explains the merits of Blockchain technology in digital finance, including applications beyond the blockbuster cryptocurrencies; Presents practical applications of cutting edge digital technologies in the digital finance sector; Illustrates the regulatory environment of the financial sector and presents technical solutions that boost compliance to applicable regulations; This book is open access, which means that you have free and unlimited access.

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.

Unlock the secrets to navigating the complex world of financial risk with "Mastering Financial Risk Management." This comprehensive guide takes you on a deep dive into the intricate mechanisms that underpin the global financial landscape. Whether you're a seasoned finance professional or an ambitious newcomer, this eBook provides you with the tools and insights needed to manage risk effectively and strategically. Begin your journey with an insightful introduction to the evolution and significance of risk management in today's volatile economy. Grasp the foundational concepts and essential definitions that every finance professional should know. Delve into detailed discussions on credit and market risks, as these chapters break down everything from basic assessment techniques to advanced management strategies such as the role of credit derivatives and portfolio management. Learn to harness the power of tools like Value at Risk (VaR) and mastering hedging techniques to safeguard your investments. Explore the burgeoning field of Environmental, Social, and Governance (ESG) risks and understand their growing impact on financial decision making. Gain essential knowledge in operational risk, uncovering its sources and management frameworks to fortify your organization against unexpected disruptions. Navigate the increasingly crucial sphere of cybersecurity, learning how to identify threats and build a cyber-resilient financial infrastructure. Familiarize yourself with regulatory and compliance risks, ensuring your strategies align with the ever-evolving legal landscape. The book also tackles contemporary technological advancements such as AI, big data, and their implications for risk analysis, alongside the psychological elements affecting financial risk decisions. Prepare for the future with insights into emerging trends, challenges, and innovations shaping the world of financial risk management. With practical insights and actionable strategies, "Mastering Financial Risk Management" empowers you to fortify your business against uncertainty and seize new opportunities with confidence. Your journey to mastering financial risk starts here.

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.

Following the success of the first edition that brought attention to the digital revolution in Islamic financial services, comes this revised and updated second edition of Blockchain, Fintech and Islamic Finance. The authors reiterate the potential of digital disruption to shrink the role and relevance of today’s banks, while simultaneously creating better, faster, cheaper services that will be an essential part of everyday life. Digital transformation will also offer the ability to create new ways to better comply to Islamic values in order to rebuild trust and confidence in the current financial system. In this new edition, they explore current concepts of decentralized finance (DeFi), distributed intelligence, stablecoins, and the integration of AI, blockchain, data analytics and IoT devices for a holistic solution to ensure technology adoption in a prudent and sustainable manner. The book discusses crucial innovation, structural and institutional developments for financial technologies including two fast-growing trends that merge and complement each other: tokenization, where all illiquid assets in the world, from private equity to real estate and luxury goods, become liquid and can be traded more efficiently, and second, the rise of a new tokenized economy where inevitably new rules and ways to enforce them will develop to fully unleash their capabilities. These complementary and oft-correlated trends will complete the decentralization of finance and will influence the way future financial services will be implemented. This book provides insights into the shift in processes, as well as the challenges that need to be overcome for practical applications for AI and blockchain and how to approach such innovations. It also covers new technological risks that are the consequence of utilizing frontier technologies such as AI, blockchain and IoT. Industry leaders, Islamic finance professionals, along with students and academics in the fields of Islamic finance and economics will benefit immensely from this book.

Integrating Artificial Intelligence (AI) presents immense opportunities and daunting challenges in the rapidly evolving finance landscape as AI-driven algorithms and models revolutionize decision-making and enhance efficiency, concerns about bias, ethics, and security loom. Financial institutions must navigate these complexities responsibly while leveraging AI's potential to innovate and thrive. Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security guides this dynamic environment. Written for professionals, researchers, policymakers, and students, this book comprehensively explores AI's impact on finance. It delves into the intricacies of bias in algorithms, ethical frameworks, cybersecurity, and regulatory compliance, offering actionable insights to address these critical issues.