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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Computer science --- FinTech --- SupTech --- RegTech --- AI --- machine learning --- P2P lending --- Blockchain

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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.

Artificial intelligence --- Big data. --- Blockchains (Databases). --- Financial applications. --- Block chains (Databases) --- Database security --- Distributed databases --- Data sets, Large --- Large data sets --- Data sets --- Finance --- Data processing --- Artificial Intelligence and Big Data --- Digital Finance --- FinTech --- Blockchain --- RegTech

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Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.

fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.

fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing