Investigating Factors Affecting the Cost of Money in Iranian Banks Based on Artificial Intelligence and Using Data Mining

Document Type : Research Paper

Authors

1 Department of Management, Hamedan Branch, Islamic Azad University, Hamedan, Iran.

2 Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.

3 Department of Knowledge and Information Science, Islamic Azad University, Hamadan Branch, Hamadan, Iran.

Abstract

One of the potential goals of companies, including banks, is to earn profit and cover current expenses. Iranian banks have also been affected. Calculating and understanding the cost of money is very important because, by calculating and analyzing it, you can estimate and implement the amount and price of paying bank facilities as well as interest on deposits. Considering that the cost of money represents the correct management of the resources and costs of a bank, the investigation of ways to reduce the cost of money in state-owned banks can be an indicator of the efficiency of managers and the performance of a bank.The best way to calculate the optimal cost is to use data mining techniques. In this research, decision tree models, Bayesian rule, neural networks, and the RoughSet model have been analyzed using data mining methods through Weka, Rosetta, and Excel software.The accuracy criterion was using decision tree J48 (0.919), Bayesian theory (0.843), neural networks (0.274), and rough set model (0.0952), which was in the form of a law by genetic algorithm, Johnson, Holt was presented. These rules enable bank managers to adopt policies based on the discovered models to better understand their resources and costs and to balance finances in their branches to achieve better value for money.

Keywords

Main Subjects

References

  • Cleary, R. J. (2006). Applied data mining. Statistical methods for business and industry.
  • David Hand, Heikki Mannila, & Smyth, P. (2001). Principles of Data Mining. The MIT Press.
  • Delen, D., Walker, G., & Kadam, A. (2005). Predicting breast cancer survivability: a comparison of three data mining methods. Artificial intelligence in medicine, 34(2), -113 .127
  • Eisazadeh, S., & Shaeri, Z. (2010). The effect of institutional factors on efficiency and cost in the Iranian banking system. The20 is the conference on banking and money research.
  • G, R., & E, J. (2011). Data Mining Your Data for Healthcare Quality Improvement [Online]. Retrieved from http://www2.sas.com/proceedings/sugi22/EMERGING/PAPER139.PDf/
  • Giudici, P. (2005). Applied data mining: statistical methods for business and industry. John Wiley & Sons.
  • Koh, H. C., &Tan, G. (2011). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), .65
  • Marbán, O., Segovia, J., Menasalvas, E., & Fernández-Baizán, C. (2009). Toward data mining engineering: a software engineering approach. Information Systems, 34(1), 107-87. Retrieved from https://www.sciencedirect.com/science/article/pii/S0306437908000355
  • Patil, B. M., Toshniwal, D., & Joshi, R. C. (2009). Predicting burn patient survivability using decision tree in Weka environment. In 2009IEEE International Advance Computing Conference (pp. 1356-1353). IEEE.
  • Qudsi, D. H. (2016). Predictive Analytics Data Mining in Imbalanced Medical Dataset. Journal Computer Terapan .204-195 2 ,2,
  • Rajesh, R., Reena, M., & Maiti, J. (2018). Decision Tree for Manual Material Handling Tasks
  • Using WEKA. Ergonomic Design of Products and Work Systems - 21st Century Perspectives of Asia, 24-13
  • Trybula, W. J. (1997). Data mining and knowledge discovery. Annual Review of Information Science and Technology (ARIST), 32, .229-197
  • Yousofi, M. H., Esmaeili, M., & Sharifian, M. S. (2016). A study on image mining; its importance and challenges. American Journal of Software Engineering and Applications, 5(1-3), .9-5  
  • Abrishami, H. (2006). Fundamentals of econometrics. (University of Tehran Press)
  • Hassanvand, R. (2016). The cost of money. Paper presented at the 6th International Conference on Accounting and Management and the 3rd International Conference on Entrepreneurship and Open Innovation. https://civilica.com/doc/577568.
  • Hayati, Z., Sadeghi Mojarad, M., & Jafari, N. (2010). Discovering the movement of electronic information users using the dependency law algorithm in data mining: A case study of the UTS University Library website, Australia. Library and Information Science, (No. 1 (consecutive 49)), 1-4. Retrieved from http://lis.aqr-ed.pdf0818e2e0da09504bbd44af934b5397_43576libjournal.ir/article
  • Rahmani, M., & Haji Zin Al-Abedini, M. (2015). Applications of data mining in information science and epistemology.
  • Rashidian, S., & Mohammadi-Pour, and. V. R. A. (2017). Investigating the optimal combination of resources in Ilam Post Bank and determining its effects on the cost of money. The first conference on accounting, management, and economics with a dynamic approach to the national economy, Malayer.
  • Sadeghi, K., & Azam Sharifi, V. (2016). Investigating the Impact of Money Cost on Market Inflation Based on Banking Resources. Paper presented at the 5th International Conference on Accounting and Management and the 2nd International Conference on Entrepreneurship and Open Innovation. https://civilica.com/doc/501479.
  • Attaran, J., Diwandari, A., & Adinov, H. (2012). Identifying the effective factors on market consolidation (achieving sustainable competitive advantage) Banking services in Bank Mellat based on a resource-based perspective. Retrieved from Business Management (Management Knowledge), 4 (2 (12)). https://www.sid.ir/fa/journal/ViewPaper.aspx?id=205955
  • Issazadeh, S., & Shaeri, Z. (2012). Investigating the effect of bank profit margins on the efficiency of the banking industry (Case study: Middle East and North Africa). Twenty-second Annual Conference on Monetary and Foreign Exchange Policies, Tehran.
  • Fallah Shams, M., & Mahdavi Rad, H. (2012). Designing a credit model and forecasting the credit risk of leasing facility customers: Case Study: Iran Khodro Leasing Company). Retrieved from .234-213, (44) 12 Econo.
  • Rokach, L., & Maimon, O. (2005). Decision trees. Data mining and knowledge discovery handbook, 165–192. Retrieved from https://doi.org/10.1007/0-387- 25465-X_9
  • Quinlan, J.R., (1987). Simplifying decision trees, International Journal of Man-Machine Studies, 27, 221-234.
  • Shahvaroughi Farahani, Milad, (2021). Prediction of Interest Rate Using Artificial Neural Network and Novel Meta-Heuristic Algorithms, Vol. 5 No. 1, https://civilica.com/doc/1328211
  • Pei, D., Yang, T., & Zhang, C. (2020). Estimation of diabetes in a high-risk adult Chinese population using J48 decision tree model. Diabetes, Metabolic Syndrome and Obesity, 4621-4630.
  • Kapoor, P., Rani, R., & JMIT, R. (2015). Efficient decision tree algorithm using J48 and reduced error pruning. Int. J. Eng. Res. Gen. Sci, 3(3), 1613-1621.
  • Aljawarneh, S., Yassein, M. B., & Aljundi, M. (2019). An enhanced J48 classification algorithm for the anomaly intrusion detection systems. Cluster Computing, 22, 10549-10565.‏
  • Cruz, A. P. D., & Tumibay, G. M. (2019). Predicting tuberculosis treatment relapse: a decision tree analysis of J48 for data mining. Journal of Computer and Communications, 7(7), 243-251.‏
  • Ahishakiye, E., Taremwa, D., Omulo, E. O., & Niyonzima, I. (2017). Crime prediction using decision tree (J48) classification algorithm. International Journal of Computer and Information Technology, 6(3), 188-195.‏
  • Rahi, M., Ebrahimnejad, A., & Motameni, H. (2024). Evaluation of predicted fault tolerance based on C5. 0 decision tree algorithm in irrigation system of paddy fields. International Journal of Intelligent Computing and Cybernetics, 17(2), 253-305.
  • Ayinla, I. B., & Akinola, S. O. (2020). An improved collaborative pruning using ant colony optimization and pessimistic technique of C5. 0 decision tree algorithm. International Journal of Computer Science and Information Security (IJCSIS), 18(12), 111-123.
  • Sum, R. M., Ismail, W., Abdullah, Z. H., FATHIHIN, N., SHAH, M. N., & HENDRADI, R. (2022). A New Efficient Credit Scoring Model for Personal Loan Using Data Mining Technique Toward for Sustainability Management. Journal of Sustainability Science and Management, 17(5), 60-76.‏
  • Hamid, A. J., & Ahmed, T. M. (2016). Developing prediction model of loan risk in banks using data mining. Machine Learning and Applications: An International Journal, 3(1), 1-9.
  • Zhu, F., Tang, M., Xie, L., & Zhu, H. (2018). A Classification Algorithm of CART Decision Tree based on MapReduce Attribute Weights. International Journal of Performability Engineering, 14(1), 17.
  • Moslehi, F., Haeri, A., & Gholamian, M. R. (2019). Investigation of effective factors in expanding electronic payment in Iran using data mining techniques. Journal of Industrial and Systems Engineering, 12(2), 61-94.‏
  • Dhakar, A., Singh, B., & Gupta, P. (2023). Fault diagnosis of air compressor set-up using decision tree-based J48 classification algorithm. Journal of Engineering Research.

Files

History

  • Receive Date: 02 February 2024
  • Revise Date: 19 July 2024
  • Accept Date: 28 August 2024

Share

How to cite

Statistics