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Big Data and Artificial Intelligence in Digital Finance

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

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Big Data and Artificial Intelligence in Digital Finance

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

Security Risk Management for the Internet of Things

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot

Data Quality Assessment: Challenges and Opportunities

Data-oriented applications, their users, and even the law require data of high quality. Research has broken down the rather vague notion of data quality into various dimensions, such as accuracy, consistency, and reputation, to name but a few. To achieve the goal of high data quality, many tools and techniques exist to clean and otherwise improve data. Yet, systematic research on actually assessing data quality in all of its dimensions is largely absent, and with it the ability to gauge the success of any data cleaning effort. It is our vision to establish a systematic and comprehensive framework for the (numeric) assessment of data quality for a given dataset and its intended use. Such a framework must cover the various facets that influence data quality, as well as the many types of data quality dimensions. In particular, we identify five facets that serve as a foundation of data quality assessment. For each facet, we outline the challenges and opportunities that arise when trying to actually assign quality scores to data and create a data quality profile for it, along with a wide range of technologies needed for this purpose

Job Implications of Artificial Intelligence

Artificial Intelligence (AI) technology has become increasingly widespread in today's society, yet its potential and development are still in the early stages. Similarly, the establishment of policies and regulatory frameworks to govern AI is still in progress. This thesis work dealt with the use of AI in the workplace, and two main problems were highlighted: The first problem is related to the nature of decisions made by artificial intelligence and its impact on work. Where bad decisions happen by using algorithmic bias or exploiting the systems and invading the privacy of workers and customers and affecting basic rights and safety. This can draw attention to issues of safety, transparency, accountability, job losses, discrimination biases, and the malevolent uses and poor decision-making. The second problem is improving working conditions and ensuring that the systems comply with the proposed regulations from the regulation proposed on April 21, 2021, by the European Commission that aims to introduce a common the regulation legal framework for artificial intelligence and General Data Protection Regulation (GDPR) which applied since May 25, 2018, and is regulated by European Union (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016. Also, by Organic Law 3/2018, of December 5. These regulations are ensuring security and safety for the users of these systems. In fact, the formation of good regulations limits errors and malicious practices. In addition, regulations should be imposed on companies to curb systems of artificial intelligence which has a negative impact on workers before putting these products on the market.La tecnología de Inteligencia Artificial (IA) está muy presente en la sociedad actual, pero aún está en pañales en términos de su potencial y desarrollo. Esto también se aplica al desarrollo de políticas y los marcos regulatorios que las rigen. Esta tesis trató sobre el uso de la IA en el lugar de trabajo y se destacaron dos problemas principales: El primer problema está relacionado con la naturaleza de las decisiones tomadas por la inteligencia artificial y su impacto en el trabajo, y puede llamar la atención la mala toma de decisiones utilizando sesgos algorítmicos o explotando los sistemas e invadiendo la privacidad de los trabajadores y clientes y afectando los derechos básicos y la seguridad. a cuestiones de seguridad, transparencia, rendición de cuentas, pérdidas de empleo, sesgos de discriminación y usos malintencionados y mala toma de decisiones. El segundo problema es mejorar las condiciones de trabajo y asegurar que los sistemas cumplan con las regulaciones propuestas a partir del reglamento propuesto el 21 de abril de 2021 por la Comisión Europea que tiene como objetivo introducir un marco legal común para el reglamento de inteligencia artificial y el Reglamento General de Protección de Datos ( RGPD) que es de aplicación desde el 25 de mayo de 2018, y está regulado por el Reglamento (UE) 2016/679 del Parlamento Europeo y del Consejo, de 27 de abril de 2016, y también por la Ley Orgánica 3/2018, de 5 de diciembre, y que garantiza seguridad y protección para los usuarios de estos sistemas y la formación de buenas normas limitan el error y las prácticas maliciosas. Además, se debería imponer normativa a las empresas para frenar los sistemas de inteligencia artificial que tiene un impacto negativo en los trabajadores antes de poner estos productos en los mercadosDepartamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Máster en Inteligencia de Negocio y Big Data en Entornos Seguro