Artificial Intelligence Fintech And Financial Inclusion

This book covers big data, machine learning, and artificial intelligence-related technologies and how these technologies can enable the design, development, and delivery of customer-focused financial services to both corporate and retail customers, as well as how to extend the benefits to the financially excluded sections of society. Artificial Intelligence, Fintech, and Financial Inclusion describes the applications of big data and its tools such as artificial intelligence and machine learning in products and services, marketing, risk management, and business operations. It also discusses the nature, sources, forms, and tools of big data and its potential applications in many industries for competitive advantage. The primary audience for the book includes practitioners, researchers, experts, graduate students, engineers, business leaders, and analysts researching contemporary issues in the area.

Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.

This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain—all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths.

This book investigates how smart technologies can play a crucial role in the achievement of the UN Sustainable Development Goals. Focusing on FinTech as well as artificial intelligence, the author demonstrates how one of the most effective strategies for accelerating progress toward global development goals is to make use of emerging technologies to broaden and deepen the scope of action. The first part of the book offers a historical perspective on sustainable development, financial technology and the emergence of the Fourth Industrial Revolution, while the second part looks in-depth at new technologies that can contribute to the realization of the SDGs. The power of AI to reduce poverty and increase food security, the implications of digital innovations for education, the impact of AI on clean transport, the role of FinTech in mitigating climate change, and ways in which AI can aid financial inclusion are all discussed.

This collection critically explores the use of financial technology (FinTech) and artificial intelligence (AI) in the financial sector and discusses effective regulation and the prevention of crime. Focusing on crypto-assets, InsureTech and the digitisation of financial dispute resolution, the book examines the strategic and ethical aspects of incorporating AI into the financial sector. The volume adopts a comparative legal approach to: critically evaluate the strategic and ethical benefits and challenges of AI in the financial sector; critically analyse the role, values and challenges of FinTech in society; make recommendations on protecting vulnerable customers without restricting financial innovation; and to make recommendations on effective regulation and prevention of crime in these areas. The book will be of interest to teachers and students of banking and financial regulation related modules, researchers in computer science, corporate governance, and business and economics. It will also be a valuable resource for policy makers including government departments, law enforcement agencies, financial regulatory agencies, people employed within the financial services sector, and professional services such as law, and technology.

Microfinance is a renowned albeit controversial solution for giving financial access to the unbanked, even if micro-transactions increase costs, limiting outreach potential. The economic and financial sustainability of Microfinance Institutions (MFIs) is a prerequisite for widening a potentially unlimited client base. Automation decreases costs, expanding the outreach potential, and improving transparency and efficiency. Technological solutions range from branchless mobile banking to geo-localization of customers, digital/social networking for group lending, blockchain validation, big data, and artificial intelligence, up to “MicroFinTech” - FinTech applications adapted to microfinance. Of interest to both scholars, students, and professors of financial technology and microfinance, this book examines these trendy solutions comprehensively, going beyond the existing literature and showing potential applications to the traditional sustainability versus outreach trade-off.

In the field of finance, the pervasive influence of algorithms has transformed the very fabric of the industry. Today, over 75% of trades are orchestrated by algorithms, making them the linchpin for trade automation, predictions, and decision-making. This algorithmic reliance, while propelling financial services into unprecedented efficiency, has also ushered in a host of challenges. As the financial sector becomes increasingly algorithm-driven, concerns about risk assessment, market manipulation, and the ethical implications of automated decision-making have taken center stage. Artificial Intelligence and Machine Learning-Powered Smart Finance, meticulously examines the intersection of computational finance and advanced algorithms and the challenges associated with this technology. As algorithms permeate various facets of financial services, the book takes a deep dive into their applications, spanning forecasting, portfolio optimization, market trends analysis, and cryptoanalysis. It sheds light on the role of AI-based algorithms in personnel selection, implementing trusted financial services, developing recommendation systems for financial platforms, and detecting fraud, presenting a compelling case for the integration of innovative solutions in the financial sector. As the book unravels the intricate tapestry of algorithmic applications in finance, it also illuminates the ethical considerations and governance frameworks essential for navigating the delicate balance between technological innovation and responsible financial practices.

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