Reduction of Insolvency Risk and Total Costs in Banking Sector using Partners Selection Approach with Genetic Algorithm and Multilayer Perceptron Neural Network

Document Type : Original Article

Authors

Department of Industrial Engineering, South Tehran Branch, Islamic Azad university, Tehran, Iran

Abstract

Banking, a vital economic pillar worldwide, thrives with effective management, aiding economic growth. Mitigating risks and addressing cost control are key challenges. Prioritizing strategies to enhance performance in both risk management and cost efficiency is crucial for the banking sector's success and economic stability. One approach is to select partners in such a way that the risk of bank insolvency and total costs are reduced, and the capital adequacy of the bank is increased. So, in this work, we first created a mathematical model to achieve the above goals in the field of banking using the approach of selecting partners. In this model, three objective functions are considered for the optimal selection of partners, two of which aim to minimize risk and cost, and the last objective is to maximize capital adequacy. To solve this multi-objective model, we implemented an integrated intelligent system. A combination of a multi-objective genetic algorithm and a neural network was used in this system. A multilayer perceptron neural network is used to calculate the nondeterministic parameters based on the data from different periods. The proposed method was evaluated using a numerical example in MATLAB software. The obtained results and their comparison with one of the classic algorithms show the superiority and reliability of this intelligent system. Using this system, the optimal partners can be selected to achieve the set goals. The most important factors in the field of risk have been identified. Then, a meta-heuristic multi-objective algorithm (NSGA-II) along with an intelligent neural network system has been used to optimally select partners. According to this intelligent system, a suitable methodology is presented along with the optimization algorithm.

Graphical Abstract

Reduction of Insolvency Risk and Total Costs in Banking Sector using Partners Selection Approach with Genetic Algorithm and Multilayer Perceptron Neural Network

Keywords

Main Subjects

References

  1. Yadav N, Kumar R. Performance and Economic Analysis of the Utilization of Construction and Demolition Waste as Recycled Concrete Aggregates. International Journal of Engineering, Transactions C: Aspects. 2024;37(3):460-7. 10.5829/ije.2024.37.03c.02
  2. Ghasemi R, Azimi Y, Ghasemi Z. Determining the optimal maintenance strategy for ammonium hydroxide‎ production unit using risk-based inspection‎ and analytic hierarchy process. International Journal of Engineering, Transactions C: Aspects. 2021;34(9):2087-96. 10.5829/ije.2021.34.09c.06
  3. Ruhland P, Wiese F. FinTechs and the financial industry: partnerships for success. Journal of Business Strategy. 2023;44(4):228-37. https://doi.org/10.1108/JBS-12-2021-0196
  4. Amin GR, Boamah MI. Modeling business partnerships: A data envelopment analysis approach. European Journal of Operational Research. 2023;305(1):329-37. https://doi.org/10.1016/j.ejor.2022.05.036
  5. Salahi F, Daneshvar A, Homayounfar M, Pourghader Chobar A. Presenting an Integrated Model for Production Planning and Preventive Maintenance Scheduling Considering Uncertainty of Parameters and Disruption of Facilities. Journal of Industrial Management Perspective. 2023;13(1, Spring 2023):105-39. https://doi.org/10.48308/jimp.13.1.105
  6. Asgari T, Daneshvar A, Chobar AP, Ebrahimi M, Abrahamyan S. Identifying key success factors for startups With sentiment analysis using text data mining. International Journal of Engineering Business Management. 2022;14:18479790221131612. https://doi.org/10.1177/18479790221131612
  7. Rezvanian M. The effect of financing structure on the risk of bank insolvency. Banking Iranian Institute of Banking Education. 2013.
  8. Konishi M, Yasuda Y. Factors affecting bank risk taking: Evidence from Japan. Journal of Banking & Finance. 2004;28(1):215-32. https://doi.org/10.1016/S0378-4266(02)00405-3
  9. Lin SL. Risk management of banking industry in Taiwan. International Journal of Services Technology and Management. 2007;8(4-5):421-51. 10.1504/IJSTM.2007.013928
  10. Zribi N, Boujelbène Y. The factors influencing bank credit risk: The case of Tunisia. Journal of accounting and taxation. 2011;3(4):70. 10.5897/JAT11.006
  11. Turan H. The weighting of factors affecting credit risk in banking. Procedia economics and finance. 2016;38:49-53. https://doi.org/10.1016/S2212-5671(16)30175-7
  12. Errico L, Sundararajan V. Islamic financial institutions and products in the global financial system: Key issues in risk management and challenges ahead. 2002. https://ssrn.com/abstract=1930788
  13. Srinivas N, Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation. 1994;2(3):221-48. 10.1162/evco.1994.2.3.221
  14. Tan Y, Anchor J. Does competition only impact on insolvency risk? New evidence from the Chinese banking industry. International Journal of Managerial Finance. 2017;13(3):332-54. https://doi.org/10.1108/IJMF-06-2016-0115
  15. Abolghasemian M, Darabi H. Simulation based optimization of haulage system of an open-pit mine: Meta modeling approach. Organizational resources management researchs. 2018;8(2):1-17. 20.1001.1.22286977.1397.8.2.2.6
  16. Abolghasemian M, Kanafi AG, Daneshmand-Mehr M. Simulation-based multiobjective optimization of open-pit mine haulage system: a modified-NBI method and meta modeling approach. Complexity. 2022;2022. https://doi.org/10.1155/2022/3540736
  17. Ghasemi P, Abolghasemian M. A Stackelberg game for closed-loop supply chains under uncertainty with genetic algorithm and gray wolf optimization. Supply Chain Analytics. 2023;4:100040. https://doi.org/10.1016/j.sca.2023.100040
  18. Goudarzi HM, Lim G, Grosshans D, Mohan R, Cao W. Incorporating variable RBE in IMPT optimization for ependymoma. Journal of applied clinical medical physics. 2024;25(1):e14207. https://doi.org/10.1002/acm2.14207
  19. Cao W, Rocha H, Mohan R, Lim G, Goudarzi HM, Ferreira BC, et al. Reflections on beam configuration optimization for intensity-modulated proton therapy. Physics in Medicine & Biology. 2022;67(13):13TR01. 10.1088/1361-6560/ac6fac
  20. Abolghasemian M, Ghane Kanafi A, Daneshmandmehr M. A two-phase simulation-based optimization of hauling system in open-pit mine. Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies). 2020;13(4):705-32. 10.22059/ijms.2020.294809.673898
  21. Jahangiri S, Abolghasemian M, Pourghader Chobar A, Nadaffard A, Mottaghi V. Ranking of key resources in the humanitarian supply chain in the emergency department of iranian hospital: a real case study in COVID-19 conditions. Journal of applied research on industrial engineering. 2021;8(Special Issue):1-10. https://doi.org/10.22105/jarie.2021.275255.1263
  22. Ruihao K. Predicting financial distress in debt contracting. University of California. 2012.
  23. Conlon T, Cotter J, Molyneux P. Beyond common equity: The influence of secondary capital on bank insolvency risk. Journal of Financial Stability. 2020;47:100732. https://doi.org/10.1016/j.jfs.2020.100732
  24. Ali S, Hussain N, Iqbal J. Corporate governance and the insolvency risk of financial institutions. The North American Journal of Economics and Finance. 2021;55:101311. https://doi.org/10.1016/j.najef.2020.101311
  25. Nalebuff B, Brandenburg A. Co-operation Harper Collins Business. Philadelphia, PA. 1996.
  26. Geringer JM, Hebert L. Measuring performance of international joint ventures. Journal of international business studies. 1991;22:249-63. https://doi.org/10.1057/palgrave.jibs.8490302
  27. Zineldin M. Co‐opetition: the organisation of the future. Marketing intelligence & planning. 2004;22(7):780-90. https://doi.org/10.1108/02634500410568600
  28. Bierly III PE, Gallagher S. Explaining alliance partner selection: fit, trust and strategic expediency. Long range planning. 2007;40(2):134-53. https://doi.org/10.1016/j.lrp.2007.03.001
  29. Cummings JL, Holmberg SR. Best-fit alliance partners: the use of critical success factors in a comprehensive partner selection process. Long Range Planning. 2012;45(2-3):136-59. https://doi.org/10.1016/j.lrp.2012.01.001
  30. Ayadi R, Schmidt RH. Investigating diversity in the banking sector in Europe: The performance and role of savings banks: CEPS; 2009.
  31. Groeneveld JM, De Vries B. European co-operative banks: First lessons of the subprime crisis. The International Journal of Cooperative Management. 2009;4(2):8-21.
  32. Brunner RD. Context-sensitive monitoring and evaluation for the World Bank. Policy Sciences. 2004;37(2):103-36. https://doi.org/10.1023/B:OLIC.0000048498.45467.31
  33. Hesse H, Čihák M. Cooperative banks and financial stability. 2007. https://ssrn.com/abstract=956767
  34. Becchetti L, Ciciretti R, Paolantonio A. The cooperative bank difference before and after the global financial crisis. Journal of International Money and Finance. 2016;69:224-46. https://doi.org/10.1016/j.jimonfin.2016.06.016
  35. Choi S, Gam YK, Park J, Shin H. Bank partnership and liquidity crisis. Journal of Banking & Finance. 2020;120:105958. https://doi.org/10.1016/j.jbankfin.2020.105958
  36. Alves J, Meneses R. Partner selection in co-opetition: a three step model. Journal of Research in Marketing and Entrepreneurship. 2015;17(1):23-35. https://doi.org/10.1108/JRME-10-2014-0026
  37. Hatak I, Hyslop K. Cooperation between family businesses of different size: A case study. Journal of co-operative organization and management. 2015;3(2):52-9. https://doi.org/10.1016/j.jcom.2015.11.003
  38. Lin Z, Wang L, editors. Multi-stage partner selection based on genetic-ant colony algorithm in agile supply chain network. 2008 The 9th international conference for young computer scientists; 2008: IEEE. 10.1109/ICYCS.2008.400
  39. Yeh W-C, Chuang M-C. Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Systems with applications. 2011;38(4):4244-53. https://doi.org/10.1016/j.eswa.2010.09.091
  40. Prakash C, Barua MK. A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption. 2016;7:66-78. https://doi.org/10.1016/j.spc.2016.04.001
  41. Gergin RE, Peker İ, Kısa ACG. Supplier selection by integrated IFDEMATEL-IFTOPSIS Method: A case study of automotive supply industry. Decision Making: Applications in Management and Engineering. 2022;5(1):169-93. https://doi.org/10.31181/dmame211221075g
  42. Gupta A, Singh RK, Mangla SK. Evaluation of logistics providers for sustainable service quality: Analytics based decision making framework. Annals of Operations Research. 2022;315(2):1617-64. https://doi.org/10.1007/s10479-020-03913-0
  43. Nayal K, Kumar S, Raut RD, Queiroz MM, Priyadarshinee P, Narkhede BE. Supply chain firm performance in circular economy and digital era to achieve sustainable development goals. Business Strategy and the Environment. 2022;31(3):1058-73. https://doi.org/10.1002/bse.2935
  44. Peng S. Sharing economy and sustainable supply chain perspective the role of environmental, economic and social pillar of supply chain in customer intention and sustainable development. Journal of Innovation & Knowledge. 2023;8(1):100316. https://doi.org/10.1016/j.jik.2023.100316
  45. Oubrahim I, Sefiani N, Happonen A. The influence of digital transformation and supply chain integration on overall sustainable supply chain performance: An empirical analysis from manufacturing companies in Morocco. Energies. 2023;16(2):1004. https://doi.org/10.3390/en16021004
  46. Rahman MS, Gani MO, Fatema B, Takahashi Y. B2B firms’ supply chain resilience orientation in achieving sustainable supply chain performance. Sustainable Manufacturing and Service Economics. 2023;2:100011. https://doi.org/10.1016/j.smse.2023.100011
  47. Haddadi H, Razavi SN, Babazadeh Sangar A. Deep learning-based risk reduction approach using novel banking parameters on a standardized dataset. Neural Computing and Applications. 2023;35(29):21663-73. https://doi.org/10.1007/s00521-023-08836-y
  48. Beade Á, Rodríguez M, Santos J. Evolutionary feature selection approaches for insolvency business prediction with genetic programming. Natural Computing. 2023;22(4):705-22. https://doi.org/10.1007/s11047-023-09951-4
  49. Kazemi HR, Khalili‐Damghani K, Sadi‐Nezhad S. Estimation of optimum thresholds for binary classification using genetic algorithm: An application to solve a credit scoring problem. Expert Systems. 2023;40(3):e13203. https://doi.org/10.1111/exsy.13203
  50. Aljadani A, Alharthi B, Farsi MA, Balaha HM, Badawy M, Elhosseini MA. Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach. Mathematics. 2023;11(19):4055. https://doi.org/10.3390/math11194055
  51. Badawy M, Balaha HM, Maklad AS, Almars AM, Elhosseini MA. Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs. Biomimetics. 2023;8(6):499. https://doi.org/10.3390/biomimetics8060499
  52. Feng L, Yang Y, Tan M, Zeng T, Li Z, Tang H, et al. Adaptive Multi-source Domain Collaborative Fine-tuning for Transfer Learning. 2023. https://doi.org/10.20944/preprints202311.0124.v1
  53. Chobar AP, Adibi MA, Kazemi A. Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability. 2022:1-28. https://doi.org/10.1007/s10668-022-02350-2
  54. Petropoulos A, Siakoulis V, Stavroulakis E, Vlachogiannakis NE. Predicting bank insolvencies using machine learning techniques. International Journal of Forecasting. 2020;36(3):1092-113. https://doi.org/10.1016/j.ijforecast.2019.11.005
  55. Reza Pourhassan M, Khadem Roshandeh MR, Ghasemi P, Seyed Bathaee MS. A multi echelon location-routing-inventory model for a supply chain network: NSGA II and multi-objective whale optimization algorithm. International Journal of Supply and Operations Management. 2023. 10.22034/IJSOM.2023.109996.2804
  56. Babaeinesami A, Tohidi H, Ghasemi P, Goodarzian F, Tirkolaee EB. A closed-loop supply chain configuration considering environmental impacts: a self-adaptive NSGA-II algorithm. Applied Intelligence. 2022;52(12):13478-96. https://doi.org/10.1007/s10489-021-02944-9
  57. Rashidi Komijan A, Ghasemi P, Khalili-Damghani K, HashemiYazdi F. A new school bus routing problem considering gender separation, special students and mix loading: a genetic algorithm approach. Journal of optimization in industrial engineering. 2021;14(2):23-39. 10.22094/JOIE.2020.1891023.1722
  58. Chen M, Tan Y. SF-FWA: A Self-Adaptive Fast Fireworks Algorithm for effective large-scale optimization. Swarm and Evolutionary Computation. 2023;80:101314. https://doi.org/10.1016/j.swevo.2023.101314
  59. Dulebenets MA. An Adaptive Polyploid Memetic Algorithm for scheduling trucks at a cross-docking terminal. Information Sciences. 2021;565:390-421. https://doi.org/10.1016/j.ins.2021.02.039
  60. Singh P, Pasha J, Moses R, Sobanjo J, Ozguven EE, Dulebenets MA. Development of exact and heuristic optimization methods for safety improvement projects at level crossings under conflicting objectives. Reliability Engineering & System Safety. 2022;220:108296. https://doi.org/10.1016/j.ress.2021.108296
  61. Dulebenets MA. A Diffused Memetic Optimizer for reactive berth allocation and scheduling at marine container terminals in response to disruptions. Swarm and Evolutionary Computation. 2023;80:101334. https://doi.org/10.1016/j.swevo.2023.101334
  62. Singh E, Pillay N. A study of ant-based pheromone spaces for generation constructive hyper-heuristics. Swarm and Evolutionary Computation. 2022;72:101095. https://doi.org/10.1016/j.swevo.2022.101095
International Journal of Engineering
Volume 37, Issue 8
TRANSACTIONS B: Applications
August 2024
Pages 1667-1690

Files

Share

How to cite

Statistics