Artificial Intelligence For Financial Risk Management And Analysis
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Machine Learning For Financial Risk Management With Python
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Author : Abdullah Karasan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-07
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: 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 Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
Artificial Intelligence For Financial Risk Management And Analysis
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Author : Derbali, Abdelkader Mohamed Sghaier
language : en
Publisher: IGI Global
Release Date : 2025-04-08
Artificial Intelligence For Financial Risk Management And Analysis written by Derbali, Abdelkader Mohamed Sghaier and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-08 with Business & Economics categories.
The revolution of artificial intelligence (AI) impacts various business sectors, including accounting and finance. Machine intelligence is on the rise in human interaction, as novel technologies automate tasks and enhance human capabilities at an increasingly rapid rate. While AI has the potential to assist in the identification and management of risks, such as in financial risk measurement, analysis, and management, the disruptive nature of these emerging technologies introduces new and complex scenarios. Utilizing these technologies to facilitate decision-making processes could result in biased, inequitable, and unreliable decisions, giving rise to concerns regarding data, privacy, and security. Further research is necessary to understand the implications of AI in financial practices. Artificial Intelligence for Financial Risk Management and Analysis delves into the most recent advancements in AI technologies that facilitate risk analysis and decision-making. It examines the potential risks these technologies pose to individuals, businesses, and establishments. Covering topics such as firm management, automation, and long short-term memory (LSTM) networks, this book is an excellent resource for financial advisors, banking professionals, computer scientists, professionals, researchers, academicians, and more.
Artificial Intelligence And Big Data For Financial Risk Management
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Author : Noura Metawa
language : en
Publisher: Taylor & Francis
Release Date : 2022-08-31
Artificial Intelligence And Big Data For Financial Risk Management written by Noura Metawa and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-31 with Business & Economics categories.
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
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Author : Theo Lynn
language : en
Publisher: Palgrave Pivot
Release Date : 2018-12-19
Disrupting Finance written by Theo Lynn and has been published by Palgrave Pivot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-19 with Business & Economics categories.
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.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Bio Inspired Credit Risk Analysis
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Author : Lean Yu
language : en
Publisher: Springer
Release Date : 2008-06-03
Bio Inspired Credit Risk Analysis written by Lean Yu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-03 with Business & Economics categories.
Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.
Artificial Intelligence For Financial Markets
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Author : Thomas Barrau
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Artificial Intelligence For Financial Markets written by Thomas Barrau and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Mathematics categories.
This book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional multivariate regressions. With its easy to interpret results, this method provides an ideal preliminary step towards the traditional neural network approach. The first two chapters compare the technique with other regression alternatives and introduces an estimation method which regularizes a polynomial regression using cross-validation. The rest of the book applies these ideas to financial markets. Certain equity return components are predicted using polymodels in very different ways, and a genetic algorithm is described which combines these different predictions into a single portfolio, aiming to optimize the portfolio returns net of transaction costs. Addressed to investors at all levels of experience this book will also be of interest to both seasoned and non-seasoned statisticians.
Artificial Intelligence For Financial Risk Management
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Author :
language : en
Publisher:
Release Date : 2025
Artificial Intelligence For Financial Risk Management written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
The Essentials Of Machine Learning In Finance And Accounting
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Routledge
Release Date : 2021-06-20
The Essentials Of Machine Learning In Finance And Accounting written by Mohammad Zoynul Abedin and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-20 with Business & Economics categories.
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.
Artificial Intelligence In Economics And Finance Theories
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Author : Tankiso Moloi
language : en
Publisher: Springer Nature
Release Date : 2020-05-07
Artificial Intelligence In Economics And Finance Theories written by Tankiso Moloi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-07 with Computers categories.
As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.