The Application of Hadoop in Natural Risk Prevention and Control of Rural Microcredit

Huaqing Mao, Li Zhu^*

Wenzhou University Oujiang College, Wenzhou, China.

DOI: 10.4236/ajibm.2015.53011                

Abstract

Rural microcredit means that the loan institutions extend the small amount of loans to the farmers. The purpose of rural microcredit is to meet the increasing needs of agriculture, animal husbandry, aquaculture, and the other business activities associated with the rural economic development. However, the rural microcredit is currently facing severe problems, such as operation risk, business risk and natural risk. Of those risks, the natural risk of rural microcredit has the most different forms and complex relationships, and the effective coping strategies lack of controllability. In the event that we can’t control and make up the losses from natural risks, it will cause the rural incomes and productions stepping down; and there is no way to get any compensation from the other capital, and this will cause the farmers can’t pay the principal and interest. As a result, natural risk prevention and control become a very important issue in rural microcredit. This paper analyzed the original cause of formation and characteristic of natural risk, and discussed how to predict the natural risk in rural microcredit. Finally, we gave the result and performance evaluation, and provided various methods to defend against the natural risk.

Keywords

Cloud Computing, Hadoop, Rural Microcredit, Natural Risk

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Barry, J.J. (2012) Microfinance, the Market and Political Development in the Interact Age. Third World Quarterly, 33.
[2]	Arora, S. and Meenu (2012) The Banking Sector Intervention in the Microfinance World: A Study of Bankers’ Perception and Outreach to Rural Microfinance in India with Special Reference to the State of Punjab. Development in Practice, 22, 991-1005. http://dx.doi.org/10.1080/09614524.2012.696092
[3]	Wagner, W. (2010) Loan Market Competition and Bank Risk-Taking. Journal of Financial Services Research, 37, 71-81. http://dx.doi.org/10.1007/s10693-009-0073-8
[4]	Altunbas, Y., Gambacorta, L. and Marques-Ibanez, D. (2010) Bank Risk and Monetary Policy. Journal of Financial Stability, 6, 121-129. http://dx.doi.org/10.1016/j.jfs.2009.07.001
[5]	Kauffman, R.J. and Riggins, F.J. (2012) Information and Communication Technology and the Sustainability of Microfinance. Electronic Commerce Research and Applications, 11, 450-468. http://dx.doi.org/10.1016/j.elerap.2012.03.001
[6]	Wang, R.Y. (2012) Reliablity Improvement of Fluorescent Lamp Using Grey Forecasting Model. Microelectronics Reliability, 42, 127-134.
[7]	Li, A.Y. and Zhang, M. (2011) Research about Loan for Small and Middle-Sized Enterprises Based on Perspective of Information Economics. China Information Times, 9, 43-48.
[8]	Ye, Q. (2011) Policy Selection and Development Path of Current Rural Microcredit Based on the View of Rural Financial Extension. Foreign Investment in China, 20.
[9]	Smith, B.L. (2011) Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting Transportation Research. Emerging Technologies, 10, 303-312. http://dx.doi.org/10.1016/S0968-090X(02)00009-8
[10]	Chen, L.W. (2008) Applied Research for Decision Tree Algorithm in Rural Microcredit. Computer Engineering and Applications, 31.
[11]	Sun, T. and Zhang, G.J. (2010) Risk Evaluation for Commercial Bank Based on Genetic Algorithm. Journal of Qingdao University (Natural Science Edition), 2.
[12]	Zhao, J.X. and Du, Z.P. (2009) Research on Credit Risk Assessment Model Based on Hybrid Neural Network and Decision Tree Algorithm. Journal of Beijing Institute of Technology (Social Sciences Edition), 1.
[13]	Jacobson, T. and Roszbach, K. (2012) Bank Lending Policy, Credit Scoring and Value-at-Risk. Journal of Banking & Finance, 27, 615-633. http://dx.doi.org/10.1016/S0378-4266(01)00254-0