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The crucial challenge of integrating sustainability into business and investment decisions is compounded by the complexity of analyzing vast and intricate datasets to make informed choices. Traditional approaches often fail to provide timely and accurate insights into environmental, social, and governance (ESG) factors, hindering progress toward a greener future. Additionally, the rapid evolution of AI and machine learning in finance has left many professionals needing help to grasp their full potential in advancing sustainability goals. With a comprehensive understanding and practical guidance, organizations can stay caught up in adopting sustainable practices and leveraging AI for financial and environmental benefits. Social and Ethical Implications of AI in Finance for Sustainability offers a timely and comprehensive solution to these challenges by thoroughly examining how AI can safely enhance sustainability in finance. The book bridges the gap between theory and practice, offering practical insights and real-world applications to empower academics, practitioners, policymakers, and students. Through a series of expertly curated chapters, readers will gain a deep understanding of the role AI plays in reshaping finance for a sustainable future. The book's instructional elements, including case studies and expert analysis, provide a roadmap for incorporating AI into sustainability strategies, enabling organizations to make informed decisions and drive positive change. By offering a unique perspective on the intersection of AI, finance, and sustainability, this book equips professionals with the knowledge and tools needed to navigate the evolving landscape of sustainable finance. Readers will learn how AI can be leveraged to analyze complex datasets, identify trends, and make informed decisions that drive financial performance and environmental impact. By embracing AI and sustainable finance principles outlined in this book, organizations can position themselves as leaders in sustainability, driving innovation and contributing to a greener, more sustainable future.

The effectiveness of internal audit activities is important for the sustainability of change in the public sector. In this sense, the tools and techniques used and the level of competencies of public sector auditors are decisive. This book deals with the effects of current technological developments in the public sector on auditing and risk management activities. Therefore, it is a resource for public internal auditors to create a digital audit strategy based on artificial intelligence (AI) and blockchain-based applications. Institutionalisation of their structures is important for public sector internal auditors. For this, basic requirements, future expectations, and best practices are explained. The digital business model is presented to produce value-added audit findings and outputs that guide public internal auditors and all digital-era stakeholders. This book is a pioneering work based on continuous auditing/continuous monitoring approaches using various AI and blockchain-based tools and techniques.There is nothing more valuable to the success of a public internal auditor than a detailed understanding of the business. The important lesson in developing business knowledge, especially in the new audit universe emerging with digital transformation, is that all auditors must understand that they never finish learning about business processes, risks, and control points in the digital era.They must constantly push themselves to be motivated and learn about the business operations they audit to implement new audit approaches powered by AI. In addition to obtaining up-to-date business information from process owners and stakeholders, public auditors responsible for conducting an AI-based continuous audit programme should also look inside their departments for a different perspective on business information that impacts continuous audit programme phase details and has the potential to add value. It should be noted that the additional source of information begins with your individual audit experience, digital skills, and qualifications.

AI iQ for a Human-Focused Future: Strategy, Talent, and Culture offers a pioneering approach to integrating artificial intelligence (AI) and generative AI (GenAI) in business, emphasizing a business strategy first mindset over a technologycentric one.This book challenges the usual hype surrounding AI, advocating for a more realistic perspective. It delves into the evolution of AI, from traditional data science and machine learning to GenAI, all through the lens of strategic business application. Unlike other texts, this book moves away from case studies, favoring practical, real-world advice from extensive field experience. This book presents strategies for creating an environment that not only accepts but thrives on AI, focusing on strategic leadership, talent development, and inclusivity. It highlights crucial roles, such as the Chief AI Officer, and emphasizes the importance of diversity in AI teams. Uniquely, each chapter concludes with key takeaways, offering actionable steps, and implementation tips. This practical approach transforms theoretical concepts into actionable business strategies, providing leaders with the tools to apply AI initiatives effectively in their organizations.This book is more than an informative resource; it's a practical toolkit for any business leader aiming to navigate the evolving landscape of AI and GenAI, ensuring their organization is prepared for sustainable growth and success in an AI-driven future.

We wrote this book to help financial experts and investors to understand the state of the art of artificial intelligence and machine learning in finance. But first, what is artificial intelligence? The foundations of artificial intelligence lie in the human desire to automate. Often this desire has had foundations in grand civilization-defining visions or economic needs, such as the Antikythera mechanism, circa 200 BCE. Considered to be the oldest known example of an analog computer, it is thought that the mechanism automated the prediction of the positions of the sun, the moon, and the planets to assist in navigation.No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms, applying them to automate routine tasks, procedures and forecasting, thereby improving overall customer experience.The topics covered in this book make it an invaluable resource for academics, researchers, policymakers, and practitioners alike who want to understand how AI has affected the banking and financial industries and how it will continue to change them in the years to come.

Upgrade your sales process by plugging into the new power of artificial intelligence In today's cutthroat sales world, where sales professionals are constantly juggling multiple responsibilities and navigating a sea of relentless competitors, everyone is looking for an edge. What if that EDGE is found in a tool powerful enough to give you more time in your sales day, accelerate your productivity, and still leave room for the human touch that's vital to building relationships? Enter the game-changing world of Artificial Intelligence. Enter The AI Edge. The AI Edge isn't just another book about technology. Anthony Iannarino and Jeb Blount, the world's most prolific sales book authors and trainers, have come together to transform how you navigate the sales process by helping you plug into artificial intelligence. This groundbreaking, hands-on guide marries their unparalleled sales strategies, used by millions of salespeople, with the transformative power of AI. Drawing from cutting-edge research and real-world applications, the authors demystify AI and demonstrate its potential to give you more time to leverage your human advantage—creativity, empathy, and authenticity—to build deeper relationships and winning solutions that give you a leg up over the competition. Inside you'll find: Expert Guidance: Benefit from the combined wisdom of Blount and Iannarino, two giants in the sales realm, as they lay out the roadmap to plugging into an AI-augmented sales strategy Streamlined Processes & Empowered Engagement: Discover AI's role in automating repetitive tasks, freeing you to fully lean into the uniquely human side of sales: cultivating relationships, unleashing creativity, and offering unparalleled authenticity Sales Prompt Engineering: Get hands-on with tailored prompts that allow you to tap into generative AI and get better results in less time Powerful Messaging: Learn how AI, used effectively, can help you develop and go to market with powerful messaging and presentations that connect with stakeholder needs and separate you from the crowded field Intelligent Insights: Grasp how AI can be leveraged to surface insights that give you instant authority, grab stakeholder attention, and lead to richer, more productive sales conversations Research: Leverage the power of AI to build target prospecting lists that open pipeline opportunities while reducing cold calling and rejection Navigating the world of AI might seem daunting, but with Iannarino and Blount at the helm, it's a journey of empowerment, innovation, and profound human connection. Embrace a future where technology and humanity come together and carve out your own AI Edge in sales.

'In the landscape of e-commerce, data security has become a concern as businesses navigate the complexities of sensitive customer information protection and cyber threat mitigation. Strategies involving cloud computing, blockchain technology, artificial intelligence, and machine learning offer solutions to strengthen data security and ensure transactional integrity. Implementing these technologies requires a balance of innovation and efficient security protocols. The development and adoption of security strategies is necessary to positively integrate cutting-edge technologies for effective security in online business.Strategies for E-Commerce Data Security: Cloud, Blockchain, AI, and Machine Learning addresses the need for advanced security measures, while examining the current state of e-commerce data security. It explores strategies such as cloud computing, blockchain, artificial intelligence, and machine learning. This book covers topics such as cybersecurity, cloud technology, and forensics, and is a useful resource for computer engineers, business owners, security professionals, government officials, academicians, scientists, and researchers.'--

This book is divided into two parts, the first of which describes AI as we know it today, in particular the Fintech-related applications. In turn, the second part explores AI models in financial markets: both regarding applications that are already available (e.g. the blockchain supply chain, learning through big data, understanding natural language, or the valuation of complex bonds) and more futuristic solutions (e.g. models based on artificial agents that interact by buying and selling stocks within simulated worlds).The effects of the COVID-19 pandemic are starting to show their financial effects: more companies in a liquidity crisis; more unstable debt positions; and more loans from international institutions for states and large companies. At the same time, we are witnessing a growth of AI technologies in all fields, from the production of goods and services, to the management of socio-economic infrastructures: in medicine, communications, education, and security. The question then becomes: could we imagine integrating AI technologies into the financial markets, in order to improve their performance? And not just limited to using AI to improve performance in high-frequency trading or in the study of trends. Could we imagine AI technologies that make financial markets safer, more stable, and more comprehensible? The book explores these questions, pursuing an approach closely linked to real-world applications.The book is intended for three main categories of readers: (1) management-level employees of companies operating in the financial markets, banks, insurance operators, portfolio managers, brokers, risk assessors, investment managers, and debt managers; (2) policymakers and regulators for financial markets, from government technicians to politicians; and (3) readers curious about technology, both for professional and private purposes, as well as those involved in innovation and research in the private and public spheres.

Längst sind Fragen der »elektronischen Datenverarbeitung« in Unternehmen ein fester Bestandteil datenschutzrechtlicher, arbeits- und gesellschaftsrechtlicher Erörterungen. Auch die Entwicklung künstlich intelligenter Systeme schreitet dynamisch voran. Der Autor befasst sich vor diesem Hintergrund eingehend mit den verschiedenen Rechtsfragen, die sich im Kontext des entscheidungsunterstützenden Einsatzes von Künstlicher Intelligenz im Vorstand einer AG ergeben. Steht es dem Vorstand frei, derartige Informationstechnik als vorgeschaltete Hilfs- bzw. Arbeitsmittel oder ausgelagert über einen externen Dienstleister einzusetzen? Ist er unter Umständen sogar verpflichtet, Künstliche Intelligenz im Rahmen der Entscheidungsprozesse einzusetzen? Und wie gestalten sich die mit dem Systemeinsatz verbundenen Sorgfaltspflichten und Haftungsrisiken der Vorstandsmitglieder? Diese und andere Fragen untersucht der Autor in seiner Arbeit.

This book discusses all aspects of money laundering, starting from traditional approach to financial crimes to artificial intelligence-enabled solutions. It also discusses the regulators approach to curb financial crimes and how syndication among financial institutions can create a robust ecosystem for monitoring and managing financial crimes. It opens with an introduction to financial crimes for a financial institution, the context of financial crimes, and its various participants. Various types of money laundering, terrorist financing, and dealing with watch list entities are also part of the discussion. Through its twelve chapters, the book provides an overview of ways in which financial institutions deal with financial crimes; various IT solutions for monitoring and managing financial crimes; data organization and governance in the financial crimes context; machine learning and artificial intelligence (AI) in financial crimes; customer-level transaction monitoring system; machine learning-driven alert optimization; AML investigation; bias and ethical pitfalls in machine learning; and enterprise-level AI-driven Financial Crime Investigation (FCI) unit. There is also an Appendix which contains a detailed review of various data sciences approaches that are popular among practitioners.The book discusses each topic through real-life experiences. It also leverages the experience of Chief Compliance Officers of some large organizations to showcase real challenges that heads of large organizations face while dealing with this sensitive topic. It thus delivers a hands-on guide for setting up, managing, and transforming into a best-in-class financial crimes management unit. It is thus an invaluable resource for researchers, students, corporates, and industry watchers alike.

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance. Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used. To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This includes algorithmic trading, forecasting, risk analysis portfolio optimization and other less well-known areas in finance. Trading depth for readability, AI for Finance will help readers decide whether to invest more time into the subject.

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.

This book is a collection of real-world cases, illustrating how to handle challenging and volatile financial time series data for a better understanding of their past behavior and robust forecasting of their future movement. It demonstrates how the concepts and techniques of statistical, econometric, machine learning, and deep learning are applied to build robust predictive models, and the ways in which these models can be used for constructing profitable portfolios of investments. All the concepts and methods used here have been implemented using R and Python languages on TensorFlow and Keras frameworks. The book will be particularly useful for advanced postgraduate and doctoral students of finance, economics, econometrics, statistics, data science, computer science, and information technology.

As the spectrum of the internet of things (IoT) expands, artificial intelligence (AI)-assisted agile IoT is the way forward for sustainable finance. The depth of agile IoT has changed the financial market, and it may quickly evolve as a powerful tool in the future. The convergence of AI and IoT techniques will significantly extract valuable financial information and offer better services to customers. Some of the potential benefits of AI-assisted agile IoT for FinTech include prompt customer support, in-door client navigation, on-site queue management, improved customer experience, security and authenticity, wireless payments, increased business efficiency, self-checkout services, and business automation. There is no doubt that leveraging the complete potential of AI-assisted agile IoT will result in the creation of a new and innovative financial system. AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems presents the advances in AI-assisted agile IoT for financial technologies (FinTech). It further explains the new applications, current issues, challenges, and future directions of the field of AI-assisted agile IoT for FinTech applications and ecosystems. Covering topics such as consensus algorithms, IoT-based banking, and secure authentication, this premier reference source is an excellent resource for business executives and managers, IT managers, librarians, students and faculty of higher education, researchers, and academicians.

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 describedwhich 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.

This open access book presents how cutting-edge digital technologies like Big Data, 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, FinTech, 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 describes some more 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.

This book presents frontier research on the use of computational methods to model complex interactions in economics and finance. Artificial Intelligence, Machine Learning and simulations offer effective means of analyzing and learning from large as well as new types of data. These computational tools have permeated various subfields of economics, finance, and also across different schools of economic thought. Through 16 chapters written by pioneers in economics, finance, computer science, psychology, complexity and statistics/econometrics, the book introduces their original research and presents the findings they have yielded.Theoretical and empirical studies featured in this book draw on a variety of approaches such as agent-based modeling, numerical simulations, computable economics, as well as employing tools from artificial intelligence and machine learning algorithms. The use of computational approaches to perform counterfactual thought experiments are also introduced, which help transcend the limits posed by traditional mathematical and statistical tools.The book also includes discussions on methodology, epistemology, history and issues concerning prediction, validation, and inference, all of which have become pertinent with the increasing use of computational approaches in economic analysis.

This book continues the discussion of the effects of artificial intelligence in terms of economics and finance. In particular, the book focuses on the effects of the change in the structure of financial markets, institutions and central banks, along with digitalization analyzed based on fintech ecosystems. In addition to finance sectors, other sectors, such as health, logistics, and industry 4.0, all of which are undergoing an artificial intelligence induced rapid transformation, are addressed in this book.Readers will receive an understanding of an integrated approach towards the use of artificial intelligence across various industries and disciplines with a vision to address the strategic issues and priorities in the dynamic business environment in order to facilitate decision-making processes. Economists, board members of central banks, bankers, financial analysts, regulatory authorities, accounting and finance professionals, chief executive officers, chiefaudit officers and chief financial officers, chief financial officers, as well as business and management academic researchers, will benefit from reading this book.

話題の人工知能の粋を集めた学会誌 学会誌人工知能に関する専門家からのさまざまな研究結果、レポートを載せ、この分野における最新の情報を掲載しています。 ★このような方におすすめ 人工知能に関心のある一般の方、人工知能の研究者 ●主要目次● 巻頭言 特集：「ファイナンスにおける人工知能応特集：「編集委員からの抱負と提言2021」 アーティクル レクチャーシリーズ：「AI哲学マップ」〔第2回〕 研究会報告 私のブックマーク グローバルアイ〔第54回〕 会議報告 書　評 アーティクル

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricingInvestors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets.Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Master's Thesis from the year 2020 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,6, University of applied sciences Frankfurt a. M., language: English, abstract: The purpose and objective of this thesis is to examine the degree of impact WFM is facing due to implementation of AI-based tools within the banking and finance industry. To do this the author will select and classify under the chapter ‘Literature Review'how, in which departments, and to what degree, banks and other financial institutions have implemented AI tools within their organization. Secondly, the author will conduct interviews with executive leaders as well as with AI researchers and experts, and analyze the data received. For this study, the author focuses on AI's impact on WFM within the banking and finance industry. The purpose of the following questions is to refine the present knowledge gap within the banking and finance industry regarding the WFM impact of AI. The author will emphasize via a literature review and interviews exactly how AI-based technology tools have been implemented in the banking and finance industry. To do so, three research questions have been chosen and will be further analyzed throughout this study paper. The first question focuses on WFM and HR teams. It predicts how many people and what kind of qualifications will be deployed. As well as where and when they will be deployed. There are many consequences of banks and other financial institutions implementing more AI technology. In his book, Competing in the Age of AI, Iansiti and Lakhani mention that it is critical for leaders to understand the choice of model along with “navigating the ethics of digital scale”. The author emphasizes that leaders must be able to build a strong organization of safety, security and sustainability. Firms spend billions of dollars on new AI related technologies and innovations. Despite this, banks and other financial institutions face three main issues. The first challenge is an outdated operating model. The second challenge is the lack of a fitting talent strategy. Both challenges are interconnected to each other. Likewise, as a third challenge, Workforce Management (WFM), the core process that boosts performance levels and competency for an organization, has been reformed and disrupted by the introduction of AI.

The book discusses the effects of artificial intelligence in terms of economics and finance. In particular, the book focuses on the effects of the change in the structure of financial markets, institutions and central banks, along with digitalization analyzed based on fintech ecosystems. In addition to finance sectors, other sectors, such as health, logistics, and industry 4.0, all of which are undergoing an artificial intelligence induced rapid transformation, are addressed in this book.Readers will receive an understanding of an integrated approach towards the use of artificial intelligence across various industries and disciplines with a vision to address the strategic issues and priorities in the dynamic business environment in order to facilitate decision-making processes. Economists, board members of central banks, bankers, financial analysts, regulatory authorities, accounting and finance professionals, chief executive officers, chief audit officers andchief financial officers, chief financial officers, as well as business and management academic researchers, will benefit from reading this book.

Today, the financial services sector is facing a period of rapid disruption and innovation, and artificial intelligence (AI) is at the heart of these changes. Artificial intelligence can be used to gather enormous amounts of data, detect abnormalities, and solve complex problems. Financial institutions are already experimenting extensively with AI strategies to enhance and streamline financial institutions, BSA and AML compliance, CRA requirements, fraud detection, and real estate valuations, all while reducing cost levels. This book looks at how artificial intelligence is affecting the financial services industry.

The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.

Die digitale Wirtschaft stellt das Recht vor fundamentale Herausforderungen: Sie operiert in digitalen Plattform- und Netzwerkstrukturen, ersetzt menschliche durch künstliche Intelligenz und überwindet mühelos staatliche Grenzen. Christoph Krönke arbeitet heraus, wie sich diese Herausforderungen auf die Regulierungsansätze und Maßstäbe sowie das administrative Organisations- und Handlungssystem des Öffentlichen Wirtschaftsrechts auswirken, einschließlich seiner unions- und verfassungsrechtlichen Grundlagen und internationalen Bezüge. Anhand zahlreicher Fachrechtsgebiete - von dem Recht digitaler Dienste und dem Datenschutzrecht über klassisches Ordnungs- und Gewerberecht bis hin zur Finanzmarktregulierung und zum Energiewirtschaftsrecht - entfaltet er dabei auf der Basis eines gleichermaßen chancen- und risikobewussten Ansatzes die Bausteine eines'Öffentlichen Digitalwirtschaftsrechts'im Werden.