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on Microfinance |
| By: | Asongu, Simplice; Nwachukwu, Jacinta |
| Abstract: | The overarching question tackled in this paper is: to what degree has financial development contributed to providing opportunities of human development for those on low-incomes and by what information technology mechanisms? We survey about 180 recently published papers to provide recent information technology advances in finance for inclusive development. Retained financial innovations are structured along three themes. They are: (i) the rural-urban divide, (ii) women empowerment and (iii) human capital in terms of skills and training. The financial instruments are articulated with case studies, innovations and investment strategies with particular emphasis, inter alia on: informal finance, microfinance, mobile banking, crowd funding, microinsurance, Islamic finance, remittances, Payment for Environmental Services (PES) and the Diaspora Investment in Agriculture (DIA) initiative. |
| Keywords: | Finance; Inclusive Growth; Economic Development |
| JEL: | G20 I10 I20 I30 O10 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:79638&r=mfd |
| By: | Thai T. Pham; Yuanyuan Shen |
| Abstract: | Kiva is an online non-profit crowdsouring microfinance platform that raises funds for the poor in the third world. The borrowers on Kiva are small business owners and individuals in urgent need of money. To raise funds as fast as possible, they have the option to form groups and post loan requests in the name of their groups. While it is generally believed that group loans pose less risk for investors than individual loans do, we study whether this is the case in a philanthropic online marketplace. In particular, we measure the effect of group loans on funding time while controlling for the loan sizes and other factors. Because loan descriptions (in the form of texts) play an important role in lenders' decision process on Kiva, we make use of this information through deep learning in natural language processing. In this aspect, this is the first paper that uses one of the most advanced deep learning techniques to deal with unstructured data in a way that can take advantage of its superior prediction power to answer causal questions. We find that on average, forming group loans speeds up the funding time by about 3.3 days. |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1706.02795&r=mfd |