Islamic Personality Model as Psychometric Tool To Assess Creditworthiness of Micro Financing
Abstract
This study aims to develop an Islamic personality model as a psychometric tool to assess creditworthiness as an alternative predictive character analysis for micro businesses. The method designed to formulate the proposed model coded in R Studio uses two approaches. First, we modify Moslem Religiosity Personality Inventory and then frame a structural model based on Partial Least Square. Subsequently, we use the random forest technique to see the model's accuracy. The result shows a valid and reliable model and performs with 89.47 % accuracy with an Area Under Curve -Receiver Operating Characteristic of 90.06 %. This model implies a solution to strengthen the assessment of the character of creditworthiness of a potential micro-business and helps Islamic Financial Institutions to assess prospective micro-business to determine credit risk and pricing.
JEL Classification: B41, D81, D87, G21, P43
How to Cite:
Hardiansyah., Amalia, E., & Hamid, A. (2023). Islamic Personality Model as Pychometric Tool To Access Creditworthiness of Micro Financing. Etikonomi, 22(1), 233–246. https://doi.org/10.15408/etk.v22i2.30370.
Keywords
References
Agarwal, S., & Hauswald, R. (2006). Distance and information asymmetries in lending decisions. Washington DC, American University.
Aggarwal, R. K., & Yousef, T. (2000). Islamic Banks and Investment Financing. Journal of Money, Credit and Banking, 32(1), 93. https://doi.org/10.2307/2601094
Arráiz, I., Bruhn, M., & Stucchi, R. (2017). Psychometrics as a tool to improve credit information. World Bank Economic Review, 30(November), S67–S76. https://doi.org/10.1093/wber/lhw016
Anderson, R. (2011). Psychometrics : A new tool for Small Business Lending. Credit Scoring and Control XII Conference, Edinburgh, Scotland, 1–12.
Antunes, J. A. P. (2021). To supervise or to self-supervise: a machine learning based comparison on credit supervision. Financial Innovation, 7(1). https://doi.org/10.1186/s40854-021-00242-4.
Barshan, E., Ghodsi, A., Azimifar, Z., & Jahromi, M.Z. (2011). Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds. Pattern Recognit., 44, 1357-1371.
Becchetti, L. & Conzo, Pierluigi. (2011). Creditworthiness as a signal of trustworthiness : field experiment in microfinance and consequences on causality in impact studies . Journal of Public Economics, Vol. 95, No. 3-4, 2011
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Caggiano, A., Angelone, R., Napolitano, F., Nele, L., & Teti, R. (2018). Dimensionality Reduction of Sensorial Features by Principal Component Analysis for ANN Machine Learning in Tool Condition Monitoring of CFRP Drilling. Procedia CIRP, 78, 307–312. https://doi.org/10.1016/j.procir.2018.09.072
Chahboun, S., & Maaroufi, M. (2021). Principal component analysis and machine learning approaches for photovoltaic power prediction: A comparative study. Applied Sciences (Switzerland), 11(17). https://doi.org/10.3390/app11177943
Chin, W.W. (2010) How to Write Up and Report PLS Analyses. In: Esposito Vinzi, V., Chin, W.W., Henseler, J. and Wang, H., Eds., Handbook of Partial Least Squares: Concepts, Methods and Applications, Springer, Heidelberg, Dordrecht, London, New York, 655-690. https://doi.org/10.1007/978-3-540-32827-8_29
Cohen, Taya R., Panter, A.T., Turan, Nazli. (2012). Guilt proneness and moral character. Current Directions in Psychological Science, 21, 355-359. doi: 10.1177/0963721412454874.
Costa, P., & McCrae, R. (1985). The NEO Personality Inventory Manual. Odessa, FL: Psychological Assessment Resources. https://doi.org/10.1037/t07564-000
Diana, N., Mahudin, M., Noor, N. M., Dzulkifli, M. A., & Shari, N. (2016). Religiosity among Muslims : A Scale Development and Validation Study Religiusitas pada Muslim : Pengembangan Skala dan Validasi Studi. 20(2), 109–120. https://doi.org/10.7454/mssh.v20i2.3492
Dubina, N., & Kang, D. (2019). Credit Scoring for Micro-Loans. Cornell University. https://doi.org/10.48550/arXiv.1905.03946
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18, 382-388. https://doi.org/10.2307/3150980
Francis, L. J., & Sahin, A. (2009). Psychometric properties of two Islamic measures among young adults in Kuwait: the Sahin Francis Scale of Attitude toward Islam and the Sahin Index of Islamic Moral Values. Journal of Moslem Mental Health. https://doi.org/10.1088/1751-8113/44/8/085201
Garson, G.D. (2016) Partial Least Squares: Regression and Structural Equation Models. Statistical Associates Publishers, Asheboro.
Gool, J. Van, Verbeke, W., Sercu, P., & Baesens, B. (2010). Credit scoring for microfinance: is it worth it? International Journal of Finance and Economics, 315(October 2009), 307–315. https://doi.org/10.1002/ijfe
Ghazali, F. B., Ramlee, S. N. S., Alwi, N., & Hizan, H. (2020). Content validity and test–retest reliability with principal component analysis of the translated Malay four-item version of Paffenbarger physical activity questionnaire. Journal of Health Research, 35(6), 493–505. https://doi.org/10.1108/JHR-11-2019-0269.
Gudergan, Siegfried P. , Ringle, Christian M. , Wende, Sven., Will, Alexander. (2018). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research 61 (2008) 1238–1249
Hair, J.F., Tatham, R.L., Anderson, R.E., Black, W. (1998) Multivariate data analysis. (Fifth Edition). Prentice-Hall: London (UK).
Hamka. (2016). Kesepaduan Iman dan Amal Saleh. Gema Insani
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015) A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43, 115-135.
https://doi.org/10.1007/s11747-014-0403-8
Huang, T., Li, J., & Zhang, W. (2020). Application of principal component analysis and logistic regression model in lupus nephritis patients with clinical hypothyroidism. BMC Medical Research Methodology, 20(1). https://doi.org/10.1186/s12874-020-00989-x.
James, G., Witten, D., Hastie, T., Tibshirani., R. (2013). An introduction to statistical learning: with applications in R. New York, US: Springer Verlag. https://doi.org/10.1007/978-1-4614-7138-7
Karamizadeh, S., Abdullah, S. M., Manaf, A. A., Zamani, M., & Hooman, A. (2013). An Overview of Principal Component Analysis. Journal of Signal and Information Processing, 04(03), 173–175. https://doi.org/10.4236/jsip.2013.43b031
Krauss, S. (2015). Development of the Muslim Religiosity-Personality Inventory for Measuring the Religiosity of Malaysian Muslim Youth. August.
Klinger, B. B., Khwaja, A. I., & Carpio, C. (2013). Enterprising Psychometrics and Poverty Reduction. SpringerBriefs in Psychology. ISBN-10: 1461472261
Krauss, S. E., Hamzah, A., Suandi, T., Mohd Noah, S., Mastor, K. A., Juhari, R., Kassan, H., Mahmoud, A., & Manap, J. (2005). The Muslim Religiosity-Personality Measurement Inventory (MRPI)’s Religiosity Measurement Model : Towards Filling the Gaps in Religiosity Research on Muslims. Pertanika JournalSocial Science and Humanities, 13(2), 131–145.
Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. In Progress in Artificial Intelligence (Vol. 5, Issue 4, pp. 221–232). Springer Verlag. https://doi.org/10.1007/s13748-016-0094-0.
Lam, Tzeng Yih, & Maguire, Douglas A. (2012). Structural Equation Modeling: Theory and Applications in Forest Management. International Journal of Forestry Research 2012(1687-9368). DOI:10.1155/2012/263953
Liaw, A., Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3). 18-22.
Luque, A., Carrasco, A., Martín, A., & de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231. https://doi.org/10.1016/j.patcog.2019.02.023
LPPI. (2018). Profil Bisnis UMKM. Kerjasama LPPI dengan Bank Indonesia. Jakarta
Mohd, M., Kadir, A., Iskandar, M., Atiqah, N., Demong, R., Normalina, E., Khalid, M., & Abbas, M. (2016). Islamic Personality Model : A Conceptual Framework. Procedia Economics and Finance, 37(16), 137–144. https://doi.org/10.1016/S2212-5671(16)30104-6
Mujib, H. A. (2006). Kepribadian dalam psikologi Islam - H. Abdul Mujib - Google Books. Raja Grafindo Persada.
Noor, Juliansyah (2016). Analisis Penelitian Data Ekonomi dan Manajemen, Jakarta: Kompas Gramedia.
Obaidullah, M., Salma, H., & Latiff, H. A. (2008). Islamic Finance For Micro And Medium Entreprises. Edited by Islamic Development Bank Centre for Islamic Banking, Finance and Management Universiti Brunei Darussalam. February.
Othman, A. K., Hamzah, M. I., & Hashim, N. (2014). Conceptualizing the Islamic Personality Model. Procedia - Social and Behavioral Sciences, 130, 114–119. https://doi.org/10.1016/j.sbspro.2014.04.014
Ozgur, O., Karagol, E. T., & Ozbugday, F. C. (2021). Machine learning approach to drivers of bank lending: evidence from an emerging economy. Financial Innovation, 7(1). https://doi.org/10.1186/s40854-021-00237-1.
Penczynski, S. P. (2019). Using machine learning for communication classification. Experimental Economics, 22(4), 1002–1029. https://doi.org/10.1007/s10683-018-09600-z.
Rabecca, H., Atmaja, N., & Safitri, S. (2018). Psychometric credit scoring in Indonesia microfinance industry: A case study in PT Amartha Mikro Fintek. Proceeding Book of International Conference on Management in Emerging Markets (ICMEM 2018), June, 620–631.
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Revelle, W. (2021). Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.1.9, https://CRAN.R-project.org/package=psych.
Rose, B. (2016). Predicting creditworthiness in retail banking with limited scoring data. March. https://doi.org/10.1016/j.knosys.2016.03.023
Safitri, D., Novianti, T., & Sartono, B. (2019). Analysis of Financing Risk Using Credit Scoring on Microfinance: a Case Study in X Islamic Bank. Russian Journal of Agricultural and Socio-Economic Sciences, 88(4), 102–111. https://doi.org/10.18551/rjoas.2019-04.14
Schreiner, M. (2003). Scoring: the next breakthrough in microcredit. Cgap, 7, 1–64.
Sekaran, U. (2003). Research Methods for Business: A Skill Building Approach (Fourth Edition). John Wiley & Sons: New York (US).
Sohn, S.Y.; Moon, T.H.; Kim, S. (2005). Improved technology scoring model for credit guarantee fund. Expert Syst. Appl. 2005, 28, 327–331. [CrossRef]
Sohn, S.Y.; Lim, K.T.; Lee, B.K. (2016), A technology credit scoring model for the biotechnology industry? In Academic Entrepreneurship: Translating Discoveries to the Marketplace; Edward Elgar Publishing: Cheltenham, UK; Northampton, MA, USA, 2016; pp. 93–114.
Sugiyono. (2012). Metode Penelitian. Kuantitatif, Kualitatif, dan R&D. Bandung : Alfabeta
Tabachnick, B.G., Fidell, L.S. (2007). Using multivariate statistics (Fifth Edition). Pearson Education: London (UK).
Tambunan, T. (2019). Recent evidence of the development of micro, small and medium enterprises in Indonesia. Journal of Global Entrepreneurship Research, 9(1). https://doi.org/10.1186/s40497-018-0140-4
Vidal, M. F., & Barbon, F. (2019). Credit Scoring in Financial Inclusion. Washington : CGAP
Zhu, Y.; Xie, C.; Sun, B.;Wang, G.J.; Yan, X.G. (2016), Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models. Sustainability 2016, 8, 433.
DOI: 10.15408/etk.v22i1.30370
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