Designing Collaterals Assessment Model to Finance Technological Projects and SMEs by Adaptive Neural Fuzzy Inference System (ANFIS)

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management, Allameh Tabataba'i University, Tehran, Iran.

2 MSc., Faculty of Science and Technology Management, Faculty of Science and Technology Management, Amirkabir University of Technology, Tehran, Iran.

3 Ph.D., Department of Financial Management, Faculty of Management, University of Tehran, Tehran, Iran.

10.22059/frj.2022.313263.1007094

Abstract

Objective: Financing is one of the factors of future success for small and medium-sized technology businesses. Because of the immaturity of these businesses, the majority of their assets are intangible, rely on technical knowledge, and lack significant financial and credit records. To control the probable risks, financing systems traditionally rely on patterns based on maximum authentication of physical assets, which do not fulfill the needs of these firms and must be altered. The fundamental purpose of this research is to create a model for assessing and determining the collaterals of technical projects and knowledge-based firms so that finances could be more easily obtained and the risk of non-refunding could be managed. In this research, in the first step, the criteria for evaluating and assessing the intended project and company were defined. By using a fuzzy expert system, the appropriate method of dealing with companies was determined. In the second step, in order to create a learning system, the results of the first step were used as input data in the form of three neural network algorithms implemented in MATLAB software and ANFIS algorithm, with 93% accuracy compared to the input data.
Methods & Results: The final neuro-fuzzy model was tested according to the repayment data available in Iran National Innovation Fund. The statistical population included knowledge-based firms receiving facilities. By using The Cochran formula, 103 companies were selected as a sample. The results obtained by testing and evaluating the model, in 85% of cases, could correctly identify the companies' performance in repaying resources and suggest appropriate collateral (Real estate collateral or Bank Guarantees).
Conclusion: The model presented in this study can be used to evaluate, validate, and determine the collaterals of technological projects and knowledge-based firms. It can facilitate their access to financial resources and also help them with managing the attendant risks. It also employs new optimization methods and has the ability to learn.

Keywords

References

Abdali Mohammadi, F. & Alivand, M. (2016). A comprehensive study of bank customer assessment and presentation of a validation method with data mining and fuzzy logic techniques, first national conference of new approaches in electrical and computer engineering, Khorramabad. (in Persian)
Abdou, H. A. (2009). Genetic programming for credit scoring: The case of Egyptian public sector banks. Expert systems with applications, 36(9), 11402-11417.
Altman E. I. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. Journal of Finance, 25(4), 589-609.
Altman, E. I., & Sabato, G. (2007). Modeling credit risk for SMEs: Evidence from the US market. Abacus, 43(3), 332-357.
Anwari Rostami, A.A., Baqaei, V., Ghorbani, S., & Masoumi, J. (2011). Application of fuzzy-statistical expert system in analysis and management of cash flows under conditions of uncertainty. Organizational Resource Management Research, 1(4), 1-30. (in Persian)
Asmuni, H. (2008). Fuzzy Methodologies for Automated University Timetabling Solution Construction and Evaluation. Ph.D. Thesis. University of Nottingham, UK.
Barzdeh, S. M. & Taghavifard, M. (2013). Design and development of a law-based fuzzy expert system for credit evaluation of commercial companies' customers (case study: Alborz Distribution and Broadcasting Company). Business Administration, 5(2), 17-46.
(in Persian)
Basel Committee on Banking Supervision (2000). Credit Rating & Complementary sources of Credit Quality Information. Working Paper no3.
Beaver, W. (1966). Financial Ratios as Predictors of Failure. Empirical Research in Accounting. Selected Studies, 4, 71–111.
Behrens, E, & Awaranek, P.M H. (1995). Manual for the preparation of industrial Feasibility Studies. UNIDO Publication.
Bekhet, H.A., & Eletter, S.F.K. (2014). Credit risk assessment model for Jordanian commercial banks: Neural scoring approach. Review of Development Finance, 4(1), 20-28.
Beykzadeh, J., Aghazadeh, G. & Aghazadeh, M. (2014). Investigating factors affecting credit risk and prioritizing credit scoring criteria (6C) of bank customers using AHP technique: a case study of National Bank of West Azarbaijan province. Trend (Economic Research Trends), 21(68): 121-150. (in Persian)
Brown, M., Degryse, H., Höwer, D., & Penas, M. F. (2012). How do banks screen innovative firms? Evidence from start-up panel data. Evidence from start-up panel data, 12-032.
Bryant, K. (2001). ALEES: an agricultural loan evaluation expert system. Expert systems with applications, 21(2), 75-85.
Chen, F. L. & Li, F. C. (2010). Combination of feature selection approaches with SVM in credit scoring. Expert systems with applications, 37(7), 4902-4909.
Chen, Y. Y., Lin, Y. H., Kung, C. C., Chung, M. H., & Yen, I. (2019). Design and implementation of cloud analytics-assisted smart power meters considering advanced artificial intelligence as edge analytics in demand-side management for smart homes. Sensors, 19(9), 2047.
Danesh, P. (2017). Designing a customer credit scoring model with a neural network approach (case study of Parsian Bank). The first international conference on new research achievements in management, accounting, economics, and industrial engineering with an emphasis on resilience economy, Sari. (in Persian)
Dang, G., Lai, K.K. & Yen, J. (2010). Credit scorecard based on logistic regression with random coefficients, Procedia Computer Science, 1(1), 2469-2478.
Darlington, K. (2000). The essence of expert systems. Prentice-Hall.
Dehmardeh, N., Shahraki, J., Saifuddin Pour, S. & Esfandiari, M. (2012). Validation of bank customers using the credit scoring approach: a case study of Sepeh Bank branches in Zahedan, Public Management Research. 5(18), 135-152. (in Persian)
Eghbali, A., Razavi Haji Agha, S.H., & Amouzad Mehdirji, H. (2017). Comparative evaluation of the performance of genetic algorithm merit functions in customer credit scoring. Industrial Management, 9(2), 245-264. (in Persian)
Falahpour, S. & Eram, A. (2016). Predicting the financial helplessness of companies using Ant colony optimization algorithms. Financial research. 18(2), 368-347. (in Persian)
Fallah Shams, M., Mahdavi Rad, H. (2011). Designing a validation model and predicting the credit risk of customers of leasing facilities (case study: Iran Khodro Leasing Company). Economic Research Journal, 12(44), 213-234. (in Persian)
Heidarpour, F. & Karzebhi, M. (2007). Designing a model for validating legal clients of the bank using the 5C criteria. Financial Knowledge of Securities Analysis, 2(2), 135-154.
(in Persian)
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
Hosseini, V. & Daneshgar, V. (2015). Presenting a new framework of credit scoring system based on support vector machine for credit risk management in banks and financial institutions, International Research Conference on Engineering, Science and Technology, Istanbul. (in Persian)
Jacobson, T. & Roszbach, K. (2003). Bank lending policy, credit scoring, and value-at-risk. Journal of banking & finance, 27(4), 315-633.
Jamshidi, S. (2007). Customer credit scoring methods, Monetary and Banking Research Institute, Central Bank of Iran. (in Persian)
Jang, J. R. (1991). Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm, AAAI'91: Proceedings of the ninth National conference on Artificial intelligence, (2), 762–767. 
Jiang, H. & Ruan, J. (2010). Investment risks assessment on high-tech projects based on analytic hierarchy process and BP neural network. Journal of networks, 5(4), 393.
Khorrami, A., Taghavifard, M.T. & Khatami Firouzabadi, M.A. (2020). Assessing the credit risk of applicants for bank facilities using case-based reasoning (CBR). Industrial Management Studies, 18(59), 116-79. (in Persian)
Lee, H.K.H. (2008). Model selection for consumer loan application data. Technical report: 650. Carnegie Mellon University. Department of Statistics.
Louzada, F., Ferreira-Silva, P. H., & Diniz, C. A. (2012). On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data. Expert Systems with Applications, 39(9), 8071-8078.
Marques, A. I., Garcia, V. & Sanchez, J. S. (2013). A literature review on the application of evolutionary computing to credit scoring. Journal of the Operational Research Society, 64(9), 1384–1399.
Mohammadi Aghdam, Y. (2014). Validation of bank's legal clients to identify variables influencing the risk of facility default, 24th annual conference on monetary and foreign exchange policies. (in Persian)
Molla Ibrahim Lou, M.H. (2005). Development of a credit scoring model for legal customers of Saman Bank, Master's thesis in economics, Tehran: Sharif University. (in Persian)
Moradi, K., Nilipour Tabatabaei, S.A. (2015). Investigating the role of small and medium enterprises in the development of the country’s economic activities, the first international conference on management, economics, accounting, and educational sciences.
(in Persian)
Raee, R. & Soroush, A. (2012). Validation of small and medium legal clients of banks using logit and probit models. Economic Research Journal, 12(44), 131-145. (in Persian)
Shao, S. P., Shao, L. P. (1998). Mathematics for Management and Finance, Eight Edition, South-Western College Publishing.
Shariat Panahi, S.M. (2008). Presenting a model for validating customers in Sanat and Mine Bank. Financial accounting empirical studies, 6(21), 61-82. (in Persian)
Sharifi, A., Aliari Shureh deli, M., and Teshneh Lab, M. (2010). Introduction of Takagi-Sugeno-Kang pseudo-polynomial fuzzy system with application in system identification and pattern classification. Journal of Control, 4(3), 15-28. (in Persian)
Shing, R. & Tsai, S., Zotani (2011). Soft computing and fuzzy neural networks. (Saeed Satayshi and Shahabeddin Shamshirband, translators). Research Institute of Nuclear Sciences and Technologies. (in Persian)
Sufi Majidpour, M. & Mousavi, S.A. (2014). Designing an assessment model and predicting the credit risk of bank customers, a case study of Bank Day, International Conference on Engineering and Applied Sciences. (in Persian)
Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management Science, 38(7), 926-947.
Thomas, L. C. (2000). A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting, 16(2), 149-172.
United Nations Industrial Development Organization (UNIDO) report, 2015.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. (in Persian)
Zadeh, L. A. (2008). Fuzzy logic. Scholarpedia, 3(3), 1766.
Financial Research Journal
Volume 24, Issue 3
2022
Pages 453-479

Files

Share

How to cite

Statistics