The Macroeconomic Effects of Fintech (YOSHINO Naoyuki, KAJI Sahoko)
The Latest in Fintech in Japan (SHOJI Masayori)
FinTech Trends in the United States: Implications for Household Finance (Ross Hikida, Jason Perry)
AI/Fintech and Asset Management Businesses (KATO Yasuyuki)
An Overview of Online Alternative Finance (FUKUHARA Masahiro)
|By YOSHINO Naoyuki||(Dean of the Asian Development Bank Institute, Professor Emeritus of economics, Keio University)|
|By KAJI Sahoko||(Professor of economics, Keio University)|
This paper first explains the extremely low level of fin-tech-related investments in Japan compared with the levels in other countries and the possibility of expansion of investments in new, uniquely Japanese businesses, such as the furusato (hometown) investment fund. Next, the paper makes clear distinctions by prefecture in terms of the necessity of financial and economic education and the current status of such education based on the results of a questionnaire survey conducted by the Central Council for Financial Services Information. Finally, by conducting a theoretical analysis of the impact of fin-tech on various economic agents, the paper shows that fin-tech has both positive and negative macroeconomic effects and explains the channels whereby the effects spread.
Keywords: furusato investment fund, financial and economic education, financial literacy map drawn by the Central Council for Financial Services Information, economic analysis of fin-tech
JEL: E02, E21, R22, E44
|By SHOJI Masayori||(Head of Japan Fintech Promotion Support, KPMG Japan)|
Since the birth of the word “Fintech,” the financial business has changed from a closed market limited to certain institutions to a highly competitive market in which venture companies and communications companies have taken place to the forefront by taking advantage of technology. Existing financial institutions can no longer survive without the use of technology. Fintech service began in the area close to the consumer, such as the payments and remittances of small amounts, but is now beginning to enter areas that were considered a source of earnings for financial institutions, such as asset management and financing. This paper examines various cases that show the uses of Fintech domestically while comparing them to those overseas use-cases, looking into the background behind companies including non-financial institutions, that have applied Fintech. Consequently, we have begun to see companies with business strategies that use the data gathered to understand customer trends and recommend services that are aligned with customer preferences. Non-financial institutions that have many members use Fintech to solidify their engagements with their customers, while financial companies have been drastically reforming their customer point of contact and user interface to address customers directly with new technology and business models so as to survive.
Keywords: Fintech, payment, remittance, financing, blockchain, crypto asset, token economy
JEL Classification: M
|By Ross Hikida||(Managing Member at ThirdStream Partners LLC)|
|By Jason Perry||(Head of Data Science at TableCheck, Inc.)|
The modern financial technology (“FinTech”) revolution has two features that distinguish it from previous eras of innovation: (1) Consumers have greater access to financial information and applications using smartphones on high-speed networks; and (2) businesses benefit from dramatically lower costs, improved performance, and enhanced options in data storage, computation, and application development. The once monolithic and proprietary financial services industry is being challenged under the zeitgeist of decentralization, disintermediation, and open protocols. Consequently, households in the United States are witnessing the emergence of new options for investment, credit, insurance, and payments. We illustrate how several influential FinTech trends may help address biases and constraints that hamper households in smoothing intertemporal consumption.
Keywords: FinTech, insurance, payments, credit, blockchain, AI, disintermediation
|By KATO Yasuyuki||(Director of Research Institute, Money Design Co.,Ltd. Research Professor of Tokyo Metropolitan University)|
In asset management business, AI and Fintech are now widely used. This article introduces a wide range of examples where AI and Fintech are applied to the development of asset management methods. The core of their applied technology is text mining that converts text information into numerical data, which has evolved through deep learning. Big data has dramatically expanded the amount of input data to asset management models, and advanced prediction models have been developed by analyzing these data using deep learning. On the other hand, AI has brought about the harmful effect of making the model a black box. A lot of attempts are also being made to contribute to the investment theory by estimating risk factors with AI optimization technology and big data. Fintech, on the other hand, has automated wealth management, which has contributed to the expansion of asset management business for small-sized and inexperienced investors with robot advisors. In addition, the application of big data is progressing even in ESG investment, which has recently attracted a lot of attention.
|By FUKUHARA Masahiro||(Institution for a Global Society Corporation CEO/Founder Keio University Project Professor)|
Online alternative finance is rapidly growing worldwide while driving financial inclusion thanks to the growing body of data made available by the expansion of cyberspace and the advancement of machine learning and other technology. The epicenter of the growth is China, but online alternative finance is also continuing to grow in developed countries such as the United States and the United Kingdom through the creation of new market segments and a shift from some existing financial institutions. The sort of information used for loan screening in the case of online alternative finance is different from that used in the case of finance provided by existing financial institutions. There are even cases where credit history information is not used. Even when credit history information is used, the range of additional data used for loan screening is expanding to include information related to electronic commerce (EC) sites, in the case of corporate borrowers, and detailed information on personality and academic achievement and the history of residential moving, in the case of individual borrowers. This paper provides an overview of the current state of online alternative finance, conducts an empirical and analytical survey on screening methods, and looks at specific cases of online alternative finance.
Keywords: fin-tech, online finance, big data, AI, loan screening, financial inclusion
JELL Classification: G2, M2