Measuring Credit Risk in a Quantitative way for Countryside Microfinance Institutions: Case study of China
Abstract
Credit scoring models (CSM) are very common in various financial institutions but in the microfinance industry it is a comparatively recent activity. The efficiency contribution of microfinance institutions towards default risk is to improve their competitiveness in an increasingly constrained environment and also reducing their cost. Now microfinance institutions measure and manage the credit risk in a quantitative way in order to gain competitiveness. In order to establish a CSM with sound predictive power, MFIs (microfinance institutions) choose models, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. A CSM model having a good classifying effect based on the data from some MFI’s in Jiangsu province has been produced as an illustration and it shows some variables play important roles in classification and need to pay much concern to reduce the predict the loan repayment credibility of the client and to manage credit risk.
References
Aguilera-Caracuel, J., Hurtado-Torres, N. E., & Aragón-Correa, J. A. (2012). Does international experience help firms to be green? A knowledge-based view of how international experience and organisational learning influence proactive environmental strategies. International Business Review, 21(5), 847–861. https://doi.org/10.1016/j.ibusrev.2011.09.009.
Baklouti, I., & Bouri, A. (2014). The loan officer’s subjective judgment and its role in microfinance institutions. International Journal of Risk Assessment and Management, 17(3), 233. https://doi.org/10.1504/IJRAM.2014.062778
Blanco, A., Pino-MejГas, R., Lara, J., & Rayo, S. (2013). Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Systems with Applications, 40(1), 356–364. https://doi.org/10.1016/j.eswa.2012.07.051
Bordo, M. D., Duca, J. V., & Koch, C. (2016). Economic policy uncertainty and the credit channel: Aggregate and bank level U.S. evidence over several decades. Journal of Financial Stability, 26, 90–106. https://doi.org/10.1016/j.jfs.2016.07.002
Chen, C. (2016). Solving the Puzzle of Corporate Governance of State-Owned Enterprises: The Path of the Temasek Model in Singapore and Lessons for China. Northwestern Journal of International Law and Business, 36(2), 303–370. Retrieved from http://datubazes.lanet.lv:3536/ehost/pdfviewer/pdfviewer?sid=bc6e262b-4eee-468a-a199-ee79fb2f4e19@sessionmgr102&vid=5&hid=125
Cole, S., Kanz, M., & Klapper, L. (2015). Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan Officers. Journal of Finance, 70(2), 537–575. https://doi.org/10.1111/jofi.12233
Conti, G., Frühwirth-Schnatter, S., Heckman, J. J., & Piatek, R. (2014). Bayesian exploratory factor analysis. Journal of Econometrics, 183(1), 31–57. https://doi.org/10.1016/j.jeconom.2014.06.008
Correia, M., Richardson, S., & Tuna, I. (2012). Value investing in credit markets. Review of Accounting Studies, 17(3), 572–609. https://doi.org/10.1007/s11142-012-9191-x
Cuéllar-Fernández, B., Fuertes-Callén, Y., Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2016). Determinants of margin in microfinance institutions. Applied Economics, 48(4), 300–311. https://doi.org/10.1080/00036846.2015.1078447
D’Espallier, B., Goedecke, J., Hudon, M., & Mersland, R. (2017). From NGOs to Banks: Does Institutional Transformation Alter the Business Model of Microfinance Institutions? World Development, 89, 19–33. https://doi.org/10.1016/j.worlddev.2016.06.021
Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47(1), 54–70. https://doi.org/10.1080/00036846.2014.962222
Etongo, D., Djenontin, I. N. S., Kanninen, M., Fobissie, K., Korhonen-Kurki, K., & Djoudi, H. (2015). Land tenure, asset heterogeneity and deforestation in Southern Burkina Faso. Forest Policy and Economics, 61, 51–58. https://doi.org/10.1016/j.forpol.2015.08.006
Gentile, C., Spiller, N., & Noci, G. (2007). How to Sustain the Customer Experience:. An Overview of Experience Components that Co-create Value With the Customer. European Management Journal, 25(5), 395–410. https://doi.org/10.1016/j.emj.2007.08.005
Hall, A. T., Frink, D. D., & Buckley, M. R. (2017). An accountability account: A review and synthesis of the theoretical and empirical research on felt accountability. Journal of Organizational Behavior, 38(2), 204–224. https://doi.org/10.1002/job.2052
Hansen, D. J., Monllor, J., & Shrader, R. C. (2016). Identifying the elements of entrepreneurial opportunity constructs: Recognizing what scholars are really examining. International Journal of Entrepreneurship and Innovation, 17(4), 240–255. https://doi.org/10.1177/1465750316671471
Hashim, N. H., Sultan, U., Abidin, Z., & Terengganu, K. (2014). Determinants Factor of Housing Loan / House Financing Pricing : Comparative Evaluation Between Conventional and Islamic Bank. International Journal Of Social Sciences, 19, 19–27.
Ismail, R., Rahman, R. A., & Ahmad, N. (2013). Risk Management Disclosure In Malaysian Islamic Financial Institutions: Pre-And Post-Financial Crisis. The Journal of Applied Business Research, 29(2), 419–432. Retrieved from http://www.cluteinstitute.com/
Ivashina, V., & Sun, Z. (2011). Institutional demand pressure and the cost of corporate loans. Journal of Financial Economics, 99(3), 500–522. https://doi.org/10.1016/j.jfineco.2010.10.009
James, G. M., & Hastie, T. J. (2001). Functional Linear Discriminant Analysis for Irregularly Sampled Curves. Journal of the Royal Statistical Society, 63(3), 533–550. https://doi.org/10.1111/1467-9868.00297
Khashei, M., Zeinal Hamadani, A., & Bijari, M. (2012). A fuzzy intelligent approach to the classification problem in gene expression data analysis. Knowledge-Based Systems, 27, 465–474. https://doi.org/10.1016/j.knosys.2011.10.012
Kliestik, T., Misankova, M., & Kocisova, K. (2015). Calculation of Distance to Default. Procedia Economics and Finance, 23, 238–243. https://doi.org/10.1016/S2212-5671(15)00481-5
Lee Rodgers, J., & Alan Nice Wander, W. (1988). Thirteen ways to look at the correlation coefficient. American Statistician, 42(1), 59–66. https://doi.org/10.1080/00031305.1988.10475524
Li, K., Niskanen, J., Kolehmainen, M., & Niskanen, M. (2016). Financial innovation: Credit default hybrid model for SME lending. Expert Systems with Applications, 61, 343–355. https://doi.org/10.1016/j.eswa.2016.05.029
Lopatta, K., Tchikov, M., Jaeschke, R., & Lodhia, S. (2017). Sustainable Development and Microfinance: The Effect of Outreach and Profitability on Microfinance Institutions’ Development Mission. Sustainable Development, 25(5), 386–399. https://doi.org/10.1002/sd.1663
Ngo, V. M., & Nguyen, H. H. (2016). The Relationship between Service Quality, Customer Satisfaction and Customer Loyalty: An Investigation in Vietnamese Retail Banking Sector. Journal of Competitivenes, 8(2), 103–116. https://doi.org/10.7441/joc.2016.02.08
Serrano-Cinca, C., Gutiérrez-Nieto, B., & Reyes, N. M. (2016). A social and environmental approach to microfinance credit scoring. Journal of Cleaner Production, 112, 3504–3513. https://doi.org/10.1016/j.jclepro.2015.09.103
Shu-Teng, L., Zariyawati, M. A., Suraya-Hanim, M., & Annuar, M. N. (2015). Determinants of Microfinance Repayment Performance: Evidence from Small Medium Enterprises in Malaysia. International Journal of Economics and Finance, 7(11), 110. https://doi.org/10.5539/ijef.v7n11p110
Singh, S., Murthi, B. P. S., & Steffes, E. (2013). Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management. European Journal of Operational Research, 224(2), 425–434. https://doi.org/10.1016/j.ejor.2012.08.007
Smith, D. J. (2011). Reliability, Maintainability and Risk. Reliability, Maintainability and Risk, 29–37. https://doi.org/10.1016/B978-0-08-096902-2.00003-9
Taddy, M. (2013). Multinomial inverse regression for text analysis. Journal of the American Statistical Association, 108(503), 755–770. https://doi.org/10.1080/01621459.2012.734168
Tchakoute Tchuigoua, H. (2016). Buffer capital in microfinance institutions. Journal of Business Research, 69(9), 3523–3537. https://doi.org/10.1016/j.jbusres.2016.01.034
Todorov, V. (2007). Robust selection of variables in linear discriminant analysis. Statistical Methods and Applications, 15(3), 395–407. https://doi.org/10.1007/s10260-006-0032-6
Treiblmaier, H., & Filzmoser, P. (2010). Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research. Information and Management, 47(4), 197–207. https://doi.org/10.1016/j.im.2010.02.002
Van Gestel, T., & Baesens, B. (2009). Credit Risk Management: Basic Concepts Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital. Credit Risk Management: Basic Concepts Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital (Vol. 9780199545117). https://doi.org/10.1093/acprof:oso/9780199545117.001.0001
Widiarto, I., & Emrouznejad, A. (2015). Social and financial efficiency of Islamic microfinance institutions: A Data Envelopment Analysis application. Socio-Economic Planning Sciences, 50, 1–17. https://doi.org/10.1016/j.seps.2014.12.001
Aguilera-Caracuel, J., Hurtado-Torres, N. E., & Aragón-Correa, J. A. (2012). Does international experience help firms to be green? A knowledge-based view of how international experience and organisational learning influence proactive environmental strategies. International Business Review, 21(5), 847–861. https://doi.org/10.1016/j.ibusrev.2011.09.009
Baklouti, I., & Bouri, A. (2014). The loan officer’s subjective judgment and its role in microfinance institutions. International Journal of Risk Assessment and Management, 17(3), 233. https://doi.org/10.1504/IJRAM.2014.062778
Blanco, A., Pino-MejГas, R., Lara, J., & Rayo, S. (2013). Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Systems with Applications, 40(1), 356–364. https://doi.org/10.1016/j.eswa.2012.07.051
Bordo, M. D., Duca, J. V., & Koch, C. (2016). Economic policy uncertainty and the credit channel: Aggregate and bank level U.S. evidence over several decades. Journal of Financial Stability, 26, 90–106. https://doi.org/10.1016/j.jfs.2016.07.002
Chen, C. (2016). Solving the Puzzle of Corporate Governance of State-Owned Enterprises: The Path of the Temasek Model in Singapore and Lessons for China. Northwestern Journal of International Law and Business, 36(2), 303–370. Retrieved from http://datubazes.lanet.lv:3536/ehost/pdfviewer/pdfviewer?sid=bc6e262b-4eee-468a-a199-ee79fb2f4e19@sessionmgr102&vid=5&hid=125
Cole, S., Kanz, M., & Klapper, L. (2015). Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan Officers. Journal of Finance, 70(2), 537–575. https://doi.org/10.1111/jofi.12233
Conti, G., Frühwirth-Schnatter, S., Heckman, J. J., & Piatek, R. (2014). Bayesian exploratory factor analysis. Journal of Econometrics, 183(1), 31–57. https://doi.org/10.1016/j.jeconom.2014.06.008
Correia, M., Richardson, S., & Tuna, I. (2012). Value investing in credit markets. Review of Accounting Studies, 17(3), 572–609. https://doi.org/10.1007/s11142-012-9191-x
Cuéllar-Fernández, B., Fuertes-Callén, Y., Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2016). Determinants of margin in microfinance institutions. Applied Economics, 48(4), 300–311. https://doi.org/10.1080/00036846.2015.1078447
D’Espallier, B., Goedecke, J., Hudon, M., & Mersland, R. (2017). From NGOs to Banks: Does Institutional Transformation Alter the Business Model of Microfinance Institutions? World Development, 89, 19–33. https://doi.org/10.1016/j.worlddev.2016.06.021
Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47(1), 54–70. https://doi.org/10.1080/00036846.2014.962222
Etongo, D., Djenontin, I. N. S., Kanninen, M., Fobissie, K., Korhonen-Kurki, K., & Djoudi, H. (2015). Land tenure, asset heterogeneity and deforestation in Southern Burkina Faso. Forest Policy and Economics, 61, 51–58. https://doi.org/10.1016/j.forpol.2015.08.006
Gentile, C., Spiller, N., & Noci, G. (2007). How to Sustain the Customer Experience:. An Overview of Experience Components that Co-create Value With the Customer. European Management Journal, 25(5), 395–410. https://doi.org/10.1016/j.emj.2007.08.005
Hall, A. T., Frink, D. D., & Buckley, M. R. (2017). An accountability account: A review and synthesis of the theoretical and empirical research on felt accountability. Journal of Organizational Behavior, 38(2), 204–224. https://doi.org/10.1002/job.2052
Hansen, D. J., Monllor, J., & Shrader, R. C. (2016). Identifying the elements of entrepreneurial opportunity constructs: Recognizing what scholars are really examining. International Journal of Entrepreneurship and Innovation, 17(4), 240–255. https://doi.org/10.1177/1465750316671471
Hashim, N. H., Sultan, U., Abidin, Z., & Terengganu, K. (2014). Determinants Factor of Housing Loan / House Financing Pricing : Comparative Evaluation Between Conventional and Islamic Bank. International Journal Of Social Sciences, 19, 19–27.
Ismail, R., Rahman, R. A., & Ahmad, N. (2013). Risk Management Disclosure In Malaysian Islamic Financial Institutions: Pre-And Post-Financial Crisis. The Journal of Applied Business Research, 29(2), 419–432. Retrieved from http://www.cluteinstitute.com/
Ivashina, V., & Sun, Z. (2011). Institutional demand pressure and the cost of corporate loans. Journal of Financial Economics, 99(3), 500–522. https://doi.org/10.1016/j.jfineco.2010.10.009
James, G. M., & Hastie, T. J. (2001). Functional Linear Discriminant Analysis for Irregularly Sampled Curves. Journal of the Royal Statistical Society, 63(3), 533–550. https://doi.org/10.1111/1467-9868.00297
Khashei, M., Zeinal Hamadani, A., & Bijari, M. (2012). A fuzzy intelligent approach to the classification problem in gene expression data analysis. Knowledge-Based Systems, 27, 465–474. https://doi.org/10.1016/j.knosys.2011.10.012
Kliestik, T., Misankova, M., & Kocisova, K. (2015). Calculation of Distance to Default. Procedia Economics and Finance, 23, 238–243. https://doi.org/10.1016/S2212-5671(15)00481-5
Lee Rodgers, J., & Alan Nice Wander, W. (1988). Thirteen ways to look at the correlation coefficient. American Statistician, 42(1), 59–66. https://doi.org/10.1080/00031305.1988.10475524
Li, K., Niskanen, J., Kolehmainen, M., & Niskanen, M. (2016). Financial innovation: Credit default hybrid model for SME lending. Expert Systems with Applications, 61, 343–355. https://doi.org/10.1016/j.eswa.2016.05.029
Lopatta, K., Tchikov, M., Jaeschke, R., & Lodhia, S. (2017). Sustainable Development and Microfinance: The Effect of Outreach and Profitability on Microfinance Institutions’ Development Mission. Sustainable Development, 25(5), 386–399. https://doi.org/10.1002/sd.1663
Ngo, V. M., & Nguyen, H. H. (2016). The Relationship between Service Quality, Customer Satisfaction and Customer Loyalty: An Investigation in Vietnamese Retail Banking Sector. Journal of Competitivenes, 8(2), 103–116. https://doi.org/10.7441/joc.2016.02.08
Serrano-Cinca, C., Gutiérrez-Nieto, B., & Reyes, N. M. (2016). A social and environmental approach to microfinance credit scoring. Journal of Cleaner Production, 112, 3504–3513. https://doi.org/10.1016/j.jclepro.2015.09.103
Shu-Teng, L., Zariyawati, M. A., Suraya-Hanim, M., & Annuar, M. N. (2015). Determinants of Microfinance Repayment Performance: Evidence from Small Medium Enterprises in Malaysia. International Journal of Economics and Finance, 7(11), 110. https://doi.org/10.5539/ijef.v7n11p110
Singh, S., Murthi, B. P. S., & Steffes, E. (2013). Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management. European Journal of Operational Research, 224(2), 425–434. https://doi.org/10.1016/j.ejor.2012.08.007
Smith, D. J. (2011). Reliability, Maintainability and Risk. Reliability, Maintainability and Risk, 29–37. https://doi.org/10.1016/B978-0-08-096902-2.00003-9
Taddy, M. (2013). Multinomial inverse regression for text analysis. Journal of the American Statistical Association, 108(503), 755–770. https://doi.org/10.1080/01621459.2012.734168
Tchakoute Tchuigoua, H. (2016). Buffer capital in microfinance institutions. Journal of Business Research, 69(9), 3523–3537. https://doi.org/10.1016/j.jbusres.2016.01.034
Todorov, V. (2007). Robust selection of variables in linear discriminant analysis. Statistical Methods and Applications, 15(3), 395–407. https://doi.org/10.1007/s10260-006-0032-6
Treiblmaier, H., & Filzmoser, P. (2010). Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research. Information and Management, 47(4), 197–207. https://doi.org/10.1016/j.im.2010.02.002
Van Gestel, T., & Baesens, B. (2009). Credit Risk Management: Basic Concepts Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital. Credit Risk Management: Basic Concepts Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital (Vol. 9780199545117). https://doi.org/10.1093/acprof:oso/9780199545117.001.0001
Widiarto, I., & Emrouznejad, A. (2015). Social and financial efficiency of Islamic microfinance institutions: A Data Envelopment Analysis application. Socio-Economic Planning Sciences, 50, 1–17. https://doi.org/10.1016/j.seps.2014.12.001
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Nadeem Iqbal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
