Managing Operational Risk Related to Microfinance Lending Process using Fuzzy Inference System based on the FMEA Method: Moroccan Case Study
Keywords:FMEA method, fuzzy logic inference system, microfinance lending process, operational risk management
Managing operational risk efficiently is a critical factor of microfinance institutions (MFIs) to get a financial and social return. The purpose of this paper is to identify, assess and prioritize the root causes of failure within the microfinance lending process (MLP) especially in Moroccan microfinance institutions. Considering the limitation of traditional failure mode and effect analysis (FMEA) method in assessing and classifying risks, the methodology adopted in this study focuses on developing a fuzzy logic inference system (FLIS) based on (FMEA). This approach can take into account the subjectivity of risk indicators and the insufficiency of statistical data. The results show that the Moroccan MFIs need to focus more on customer relationship management and give more importance to their staff training, to clients screening as well as to their business analysis.JEL Codes - G21; G32; C02
Abdulrahman, U. F. I., Panford, J. K., and Hayfron-acquah, J. B., 2014. Fuzzy Logic Approach to Credit Scoring for Micro Finance in Ghana: A Case Study of KWIQPLUS Money Lending. International Journal of Computers and Applications, 94(8).
Aboulaich, R., Khamlichi, F. I., and Kaicer, M., 2013. Microcredit Scoring with Fuzzy Logic Model. International Journal of Statistics & Economics, 10(1), 109-117.
Barua, A., Mudunuri, L. S., and Kosheleva, O., 2013. Why trapezoidal and triangular membership functions work so well: Towards a theoretical explanation. Departmental Technical Reports (CS), 783. digitalcommons.utep.edu/cgi/viewcontent.cgi?article=1786&context=cs_techrep.
Belhaj, R., and Tkiouat, M., 2015. Including Client Opinion and Employee Engagement in the Strategic Human Resource Management: An Advanced SWOT-FUZZY Decision Making Tool. International Journal of Human Capital and Information Technology Professionals, 6(3), 20-33. doi: http://dx.doi.org/10.4018/IJHCITP.2015070102
Bennouna, G., and Tkiouat, M., 2016. Studies and Research on Microfinance Sector in Morocco: An Overview. Asian Journal of Applied Sciences, 4(3), 585-599.
Dernoncourt, F., 2013. Introduction to fuzzy logic: Massachusetts Institute of Technology.
Dumas, M., La Rosa, M., Mendling, J., and Reijers, H. A., 2013. Fundamentals of business process management. Heidelberg: Springer. doi:http://dx.doi.org/10.1007/978-3-642-33143-5
Duminica, D., Avram, M., and Apostolescu, T. C., 2011. Fuzzy logic used in FMEA analysis. The Romanian Review Precision Mechanics, Optics & Mechatronics, 1, 37-40.
Dunford, C., 2006. Evidence of microfinance's contribution to achieving the millennium development goals. Davis, CA: Freedom from Hunger, USA.
Duvendack, M., and Palmer-Jones, R., 2012. High noon for microfinance impact evaluations: Re-investigating the evidence from Bangladesh. The Journal of Development Studies, 48(12), 1864-1880. doi: http://dx.doi.org/10.1080/00220388.2011.646989
Haq, I. U., Izhar, K., Anwar, S., Khan, M. T., Ahmed, B., and Maqsood, S., 2015. Fuzzy Logic Based Failure Mode and Effect Analysis of Automotive Powertrain Assembly Systems. Technical Journal, University of Engineering and Technology (UET) Taxila, 20(SI)(II), 57-64.
Hermes, N., and Lensink, R., 2007. Impact of microfinance: A critical survey. Economic and Political Weekly, 462-465.
International Finance Corporation, 2014. Ending the Microfinance Crisis in Morocco: Acting Early: Acting Right.
JAIDA, 2009. Sectorial study: Microfinance one year after the announcement of turbulences. Working paper.
Keskin, G. A., and Ozkan, C., 2009. An alternative evaluation of FMEA: Fuzzy ART algorithm. Quality and Reliability Engineering International, 25(6), 647-661. doi: http://dx.doi.org/10.1002/qre.984
La Torre, M., and Vento, G. A., 2006. Microfinance: Palgrave Macmillan. doi:http://dx.doi.org/10.1057/9780230627581
Ledgerwood, J., Earne, J., and Nelson, C., 2013. The new microfinance handbook: A financial market system perspective: World Bank Publications. doi:http://dx.doi.org/10.1596/978-0-8213-8927-0
Littlefield, E., Morduch, J., and Hashemi, S., 2003. Is microfinance an effective strategy to reach the Millennium Development Goals? Focus note, 24, 1-11.
Lozano, C., and Fuentes, F., 2010. A Systemic-Fuzzy Model to Evaluate the Social Impact of Microcredits. Advanced Technologies for Microfinance: Solutions and Challenges, 267.
Nobari, S., Jabrailova, Z., and Nobari, A., 2012. Using Fuzzy Decision Support Systems in Human Resource Management. Paper presented at the International Conference on Innovation and Information Management, Singapore.
Ortolani, M., 2006. Monitoring the Microfinance Processes Microfinance (pp. 93-111). London: Palgrave Macmillan UK. doi:10.1057/9780230627581_6
Pedrycz, W., 1994. Why triangular membership functions? Fuzzy Sets and Systems, 64(1), 21-30. doi: http://dx.doi.org/10.1016/0165-0114(94)90003-5
Pinto, A. C., and Magpili, L., 2015. Operational Risk Management: Momentum Press.
Ravi Sankar, N., and Prabhu, B. S., 2001. Modified approach for prioritization of failures in a system failure mode and effects analysis. International Journal of Quality & Reliability Management, 18(3), 324-336. doi: http://dx.doi.org/10.1108/02656710110383737
Reille, X., 2009. The Rise, Fall, and Recovery of the Microfinance Sector in Morocco: The World Bank.
Reveiz, A., and León, C., 2009. Operational risk management using a fuzzy logic inference system. Minimise risk, Optimise success, 141.
Shah, S., 2002. Measuring and managing operational risks. New York: Towers Perrin-Tillinghast.
Shang, K., and Hossen, Z., 2013. Applying fuzzy logic to risk assessment and decision-making. Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries, 1-59.
Steinwand, D., 2000. A risk management framework for microfinance institutions. Financial Systems Development and Banking Services, 1-40. http://www.ruralfinanceandinvestment.org/sites/
Tarantino, A., and Cernauskas, D., 2009. Risk management in finance: Six sigma and other next generation techniques: John Wiley and Sons.
Zadeh, L. A., 1965. Fuzzy sets. Information and control, 8(3), 338-353.
Zadeh, L. A., 1983. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, 11(1-3), 199-227.
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