Abstract

Credit scoring has provided a number of financial institutions like banks, Microfinance institutions the means of determining if a given client will default or repay their debt obligation. Credit defaulting has become a stubborn enemy to the financial sector globally. With numerous Saccos in Kenya today it is challenging to predict accurately the trust of its members hence there arise a need of models, which will determine Sacco members credit worthiness. Qualitative output variable (i.e. member credit worth) measured using factors (i.e. Credit Duration, Concurrent Credits, Repayment Amount, Most Valuable Asset and Account Balance with Sacco) are scaled using appropriate linguistic terms and fused using hierarchical sensory fusion to evaluate credit worth of Sacco members in Kenya. Similarly, the output variable member credit worthiness was assigned linguistic terms of Excellent, Good, Fair/ Average, Bad and Poor.

References

• K. Bart, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. 1992.
• V. Kecman, Learning And Soft Computing - Support Vector Machines, Neural Networks, and Fuzzy Logic Models. London, England: Massachusetts Institute of Technology, 1948.
• K. Otieno, R. Mugo, D. Njeje, and A. Kimathi, “Effect of Corporate Governance on Financial Performance of SACCOS in Kenya,” Res. J. Financ. Account., vol. 6, no. 2, pp. 390–407, 2015.
• F. N. Nkuru, “Factors Affecting Growth of Saccos Within the Agricultural Sector In Kenya: A Case Of Meru Farmers Saccos,” vol. 4, no. 1, pp. 34–45, 2015.
• H. Jiang, W. Ching, K. Fai, C. Yiu, and Y. Qiu, “Stationary Mahalanobis Kernel SVM for Credit Risk Evaluation,” Appl. Soft Comput., 2018.
• L. Dirick, G. Claeskens, and B. Baesens, “Time to default in credit scoring using survival analysis : a benchmark study,” J. Oper. Res. Soc., 2016.
• P. . Costa Branco and J. . Dente, “A Fuzzy Relational Identification Algorithm and Its Application to Predict The Behaviour of A Motor Drive System.,” Fuzzy Sets Syst., 2000.
• F. Louzada and G. B. Fernandes, “Classification methods applied to credit scoring : A systematic review and overall comparison,” 2018.
• N. R. Darwish and A. S. Abdelghany, “A Fuzzy Logic Model for Credit Risk Rating of Egyptian Commercial Banks,” Int. J. Comput. Sci. Inf. Secur., vol. 14, no. 2, pp. 11–19, 2016.
• U. Farouk Ibn Abdulrahman, J. Kobina Panford, and J. Ben Hayfron-Acquah, “Fuzzy Logic Approach to Credit Scoring for Micro Finance in Ghana: A Case Study of KWIQPLUS Money Lending,” Int. J. Comput. Appl., vol. 94, no. 8, pp. 11–18, 2014.
• S. Sampath and V. Kalaichelvi, “Assessment of Mortgage Applications Using Fuzzy Logic,” Int. J. Econ. Manag. Eng., vol. 8, no. 11, pp. 3487–3491, 2014.
• J. Ignatius, A. Hatami-Marbini, A. Rahman, L. Dhamotharan, and P. Khoshnevis, “A Fuzzy Decision Support System for Credit Scoring,” Neural Comput. Appl., vol. 29, no. 10, pp. 921–937, May 2018.
• G. Bennouna and M. Tkiouat, “Fuzzy logic approach applied to credit scoring for micro finance in Morocco,” Procedia Comput. Sci., vol. 127, pp. 274–283, 2018.
• G. A. C. Xiao Yao ⇑, Jonathan Crook, “Support vector regression for loss given default modelling,” Expert Syst. Appl., vol. 37, no. 1, pp. 127–133, 2014.
• S. D. Volpone, S. Tonidandel, D. R. Avery, and S. Castel, “Exploring the Use of Credit Scores in Selection Processes: Beware of Adverse Impact,” J. Bus. Psychol., 2015.
• G. Road, “Lending Organization and Credit Supply During the 2008 – 2009 Crisis,” vol. 9999, no. 9999, pp. 1–29, 2017.
• P. Danenas and G. Garsva, “Selection of Support Vector Machines Based Classifiers for Credit Risk Domain,” Expert Syst. Appl., vol. 42, no. 6, pp. 3194–3204, 2015.
• O. Amat, R. Manini, and M. A. Renart, “Credit concession through credit scoring: Analysis and application proposal,” Intang. Cap., vol. 13, no. 1, pp. 51–70, 2017.
• J. M. Mwangi, “( Saccos ) in Financial Intermediation in Nairobi County,” no. October, 2011.
• H. Bierman and W. H. Hausman, “The Credit Granting Decision,” Manag. Sci., vol. 16, no. 8, p. B-519--B-532, Apr. 1970.
• D. Björkegren and D. Grissen, “Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment,” pp. 1–28, 2015.
• R. Calabrese, “Downturn Loss Given Default: Mixture Distribution Estimation,” Eur. J. Oper. Res., vol. 237, no. 1, pp. 271–277, 2014.
• M. G. Kavussanos and D. A. Tsouknidis, “Default Risk Drivers in Shipping Bank Loans,” Transp. Res. Part E Logist. Transp. Rev., vol. 94, pp. 71–94, Oct. 2016.
• F. Dernoncourt, Introduction to fuzzy logic. MIT, 2013.