This study presents an all-inclusive analysis of the literature on the augmentation of financial inclusion through fintech.Ninety-six papers were selected from the 2951 articles in the Web of Science,Scopus,and EBSCO ...This study presents an all-inclusive analysis of the literature on the augmentation of financial inclusion through fintech.Ninety-six papers were selected from the 2951 articles in the Web of Science,Scopus,and EBSCO databases.This study uses bibliometric and content analysis techniques to illuminate the underexplored aspects of fintech’s impact on financial inclusion.Unlike previous studies,this study consolidates a significant amount of the literature on financial inclusion by systematically contextualizing theories and viewpoints from the fintech sector.The key findings include the identification of three main research clusters:(1)the advent of novel services,(2)the transformation of the market landscape,and(3)the roles of stakeholders in the fintech ecosystem.The analysis reveals gaps in the existing research,such as the need for more studies on the tangible impact of fintech on financial inclusion and regulation.This study concludes by highlighting potential directions for future research and emphasizing the importance of policymakers paying greater attention to fintech’s implications for financial inclusion.展开更多
With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific...With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a </span><span style="font-family:Verdana;">running example, which allows different kind of researchers to find their</span><span style="font-family:Verdana;"> needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.展开更多
文摘This study presents an all-inclusive analysis of the literature on the augmentation of financial inclusion through fintech.Ninety-six papers were selected from the 2951 articles in the Web of Science,Scopus,and EBSCO databases.This study uses bibliometric and content analysis techniques to illuminate the underexplored aspects of fintech’s impact on financial inclusion.Unlike previous studies,this study consolidates a significant amount of the literature on financial inclusion by systematically contextualizing theories and viewpoints from the fintech sector.The key findings include the identification of three main research clusters:(1)the advent of novel services,(2)the transformation of the market landscape,and(3)the roles of stakeholders in the fintech ecosystem.The analysis reveals gaps in the existing research,such as the need for more studies on the tangible impact of fintech on financial inclusion and regulation.This study concludes by highlighting potential directions for future research and emphasizing the importance of policymakers paying greater attention to fintech’s implications for financial inclusion.
文摘With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a </span><span style="font-family:Verdana;">running example, which allows different kind of researchers to find their</span><span style="font-family:Verdana;"> needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.
