Evaluating the performance of machine learning algorithms in predicting the best bank customer
Abstract
The best customer refers to the potential interaction of customers with the company during certain time periods. When companies understand the best customer and realize that the best customer can provide customized services for different customers, then they will achieve effective customer relationship management. This research is focused on the banking industry and systematically integrates data mining techniques and management topics to analyze the best customers. This study first uses the fuzzy hierarchical analysis method to weight the existing variables and then examines the DFMT model as an input to the k-means technique for clustering customers based on the desired criteria in the DFMT model. By using the proposed scoring model, it starts forming a customer value pyramid and categorizes customers into 4 value spectrums. Finally, in order to analyze the classes obtained from the customer value pyramid and implement the learning process from the available data, it uses the tenor classification techniques of decision tree, support vector machines and random forest along with the six characteristics and among They introduce the most appropriate model-characteristic based on available criteria.
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P. Agarwal, Redefining Banking and Financial Industry through the application of Computational Intelligence, In: 2019 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 2019, 1-5.
B. Alsajjan, C. Dennis, Internet banking acceptance model: Cross-market examination, Journal of business research 63 (2010), no. 9-10, 957-963.
A. Ashta, H. Herrmann, Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance, Strategic Change 30 (2021), no. 3, 211-222.
Y. Bai, N. Krishnamoorthy, K.R. Patel, I. Rosas, M.J. Sanderson, X. Ai, Cryopreserved human precision-cut lung slices as a bioassay for live tissue banking. A viability study of bronchodilation with bitter-taste receptor agonists, American journal of respiratory cell and molecular biology 54 (2016), no. 5, 656-663.
J. Beutel, S. List, G. von Schweinitz, Does machine learning help us predict banking crises?, Journal of Financial Stability 45 (2019), 100693.
T. Boobier, AI and the Future of Banking, John Wiley & Sons, 2020.
M.S. Caron, The transformative effect of AI on the banking industry, Banking & Finance Law Review 34 (2019), no. 2, 169-214.
B. Deepthi, P. Gupta, P. Rai, H. Arora, Assessing the Dynamics of AI Driven Technologies in Indian Banking and Financial Sector, Vision (2022).
M. Dehghan Nayeri, M. Khazaei, F. Alinasab-Imani, The critical heuristics of iranian banking credit system: analysis of the antithetical opinions of the beneficiaries, Systemic Practice and Action Research 33 (2020), no. 3, 363-392.
F. Dekamini, M. Ehsanifar, Comparison of the Eficiency of Data Mining Algorithms in Predicting the Diagnosis of Diabetes, IJDLD 21 (2021), no. 4, 264-275.
P.K. Donepudi, AI and machine learning in banking: a systematic literature review, Asian Journal of Applied Science and Engineering 6 (2017), no. 3, 157-162.
P.K. Donepudi, Machine learning and artificial intelligence in banking, Engineering International 5 (2017), no. 2, 83-86.
R. Golparvar, M. Seidjal, Al-Din Mousavi, Comparative study of the most used classification algorithms in customer prediction and validation, The fifth national conference of applied research in management and accounting, Tehran, 2017.
N. Hamta, M. Ehsanifar, B. Mohammadi, Assessing Credit Risk in the Banking System Using Data Mining Techniques, Journal of Development & Evolution Mnagement 10 (2019), 141-150.
S.V. Jaiwant, Artificial intelligence and personalized banking, In: V. Garg, R. Goel (Eds.)Handbook of Research on Innovative Management Using AI in Industry 5.0, IGI Global, (2022), pp. 74-87.
D. Kaur, S.L. Sahdev, D. Sharma, L. Siddiqui, Banking 4.0:`The inuence of artificial intelligence on the banking industry & how AI is changing the face of modern day banks', International Journal of Management 11 (2020), no. 6, 577-585.
M. Khazaei, M. Ramezani, A. Padash, D. DeTombe, Creating shared value to redesigning IT-service products using SYRCS; Diagnosing and tackling complex problems, Information Systems and e-Business Management 19 (2021), no. 3, 957-992.
M. Khazaei, M. Ramezani, A. Padash, D. DeTombe, The quantification role of BWM in problem structuring methods: SYRCS methodology, In: Advances in Best-Worst Method, Proceedings of the Second The International Workshop on Best-Worst Method, Springer International Publishing, 2021, 252-271.
W.S. Lee, G.H. Tzeng, J.L. Guan, K.T. Chein, J.M. Huang, Combined MCDM Techniques for Exploring Stock Selection Based on Gordon Model, Expert Systems with Applications 36 (2009), no. 7, 6421-6430.
M. Leo, S. Sharma, K. Maddulety, Machine learning in banking risk management: A literature review, Risks 7 (2019), no. 1, 29.
M. Mehrara, M. Moussai, M. Imagini, A. Hassanzadeh, Credit rating of legal customers of Parsian Bank, Economic Modeling Quarterly 3 (2011), 150-121.
V. Mrass, C. Peters, J.M. Leimeister, Managing complex work systems via crowdworking platforms: how Deutsche Bank explores AI trends and the future of banking with Jovoto, In: Proceedings of the 51st Hawaii International Conference on System Sciences (2018), 11 p.
A. Nahidi, R. Mohammad, M. Alizadeh, Investigating factors affecting the attraction of financial resources of banks, a case study: Hekmat Iranian Bank, Shahratbriz, International Conference on Economic Management and Human Sciences, Athens, 2016.
P. Najmi, A. Rad, M. Motto, Prediction of bank customer turnover using data mining method, industrial management (Azad Sanandaj), 13th year, summer 2017, number 44 (2018).
A.P. Omoge, P. Gala, A. Horky, Disruptive technology and AI in the banking industry of an emerging market, International Journal of Bank Marketing 40 (2022), no. 6, 1217-1247.
E.H. Manser Payne, J. Peltier, V.A. Barger, Enhancing the value co-creation process: artificial intelligence and mobile banking service platforms, Journal of Research in Interactive Marketing 15 (2021), no. 1, 68-85.
S.M. Piryonesi, T.E. El-Diraby, Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index, Journal of Infrastructure Systems 26 (2020), no. 1.
M. Rahman, T.H. Ming, T.A. Baigh, M. Sarker, Adoption of artificial intelligence in banking services: An empirical analysis, International Journal of Emerging Markets (2021).
M. Ramezani, A. Azar, M. Khazaei, Gap analysis through a hybrid method: Critical systems heuristics and best worst method, In: Advances in Best-Worst Method, Proceedings of the Second The International Workshop on Best-Worst Method, Springer International Publishing, 2021, 272-286.
J.N. Sheth, V. Jain, G. Roy, A. Chakraborty, AI-driven banking services: the next frontier for a personalised experience in the emerging market, International Journal of Bank Marketing 40 (2022), no. 6.
A. Suresh, N.J. Rani, Role of Artificial Intelligence (AI) in the Indian Banking Scenario, Journal of Information Technology & Economic Development 11 (2020), no. 2.
A. Taghipour, M. Khazaei, A. Azar, A. RajabzadehGhatari, M. Hajiaghaei-Keshteli, M. Ramezani, Creating Shared Value and Strategic Corporate Social Responsibility through Outsourcing within Supply Chain Management, Sustainability 14 (2022), no. 4, 1940.
M. Thisarani, S. Fernando, Artificial Intelligence for Futuristic Banking, In: 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE 2021, 1-13.
M.H. Thowfeek, S.N. Samsudeen, M.B.F. Sanjeetha, Drivers of Artificial Intelligence in Banking Service Sectors, Solid State Technology 63 (2020), no. 5, 6400-6411.
M. Xue, G. Xiu, V. Saravanan, C.E. Montenegro-Marin, Cloud computing with AI for banking and e-commerce applications, The Electronic Library 39 (2020), no. 4.
P. Appiahene, Y.M. Missah, U. Najim, Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks, Advances in Fuzzy Systems 2020 (2020), Article ID 8581202.
DOI: https://doi.org/10.52846/ami.v50i2.1781