Articles
| Open Access |
Vol. 4 No. 12 (2024): Volume 04 Issue 12
| DOI:
https://doi.org/10.37547/marketing-fmmej-04-12-06
MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS
Biswanath Bhattacharjee , Department Of Management Science And Quantitative Methods, Gannon University, USA Sanjida Nowshin Mou , Department Of Management Science And Quantitative Methods, Gannon University, USA Md Shakhaowat Hossain , Department Of Management Science And Quantitative Methods, Gannon University, USA Md Khalilor Rahman , MBA In Business Analytics, Gannon University, Erie, Pennsylvania, USA Md Mehedi Hassan , Master Of Science In Information Technology, Washington University Of Science And Technology, USA Nabila Rahman , Master’s In Information Technology Management, Webster University, USA Refat Naznin , Department Of Business Administration, Bangladesh University Of Business And Technology (Bubt), Bangladesh Md Ariful Islam Sarkar , Department Of Business Administration Stamford University, Dhaka, Bangladesh Md Asaduzzaman , Department Of Business Administration, Northern University, Bangladesh Md Sayem Ul Haque , MBA In Business Analytics, Gannon University, USAAbstract
Accurate cost estimation and forecasting are critical for effective decision-making in the banking sector. This study evaluates the performance of machine learning algorithms, including Linear Regression, Ridge Regression, Random Forest, Gradient Boosting Machine (GBM), and Long Short-Term Memory (LSTM) networks, for cost prediction using a robust dataset comprising operational, transactional, and macroeconomic features. Our results demonstrate that while simpler models like Linear and Ridge Regression offer computational efficiency, their predictive accuracy is limited in handling complex data. Tree-based methods, particularly Random Forest and GBM, significantly enhance performance by capturing intricate patterns, albeit at a higher computational cost. The LSTM network outperformed all models, achieving the highest R² score and the lowest MAE and MSE values, highlighting its superiority in capturing temporal dependencies. This research provides actionable insights for banking institutions, emphasizing the trade-offs between accuracy, efficiency, and model complexity. The findings pave the way for optimized ML adoption in financial forecasting, enhancing operational resilience and strategic planning.
Keywords
Cost estimation, forecasting, machine learning, banking sector
References
M. S. Haque, M. S. Taluckder, S. Bin Shawkat, M. A. Shahriyar, M. A. Sayed and C. Modak, "A Comparative Study of Prediction of Pneumonia and COVID-19 Using Deep Neural Networks," 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), Yogyakarta, Indonesia, 2023, pp. 218-223, doi: 10.1109/ICE3IS59323.2023.10335362.
Zhao, L., Zhang, Y., Chen, X., & Huang, Y. (2021). A reinforcement learning approach to supply chain operations management: Review, applications, and future directions. Computers & Operations Research, 132, 105306. https://doi.org/10.1016/j.cor.2021.105306
Md Al-Imran, Eftekhar Hossain Ayon, Md Rashedul Islam, Fuad Mahmud, Sharmin Akter, Md Khorshed Alam, Md Tarek Hasan, Sadia Afrin, Jannatul Ferdous Shorna, & Md Munna Aziz. (2024). TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS. The American Journal of Engineering and Technology, 6(11), 20–32. https://doi.org/10.37547/tajet/Volume06Issue11-04
Tauhedur Rahman, Md Kafil Uddin, Biswanath Bhattacharjee, Md Siam Taluckder, Sanjida Nowshin Mou, Pinky Akter, Md Shakhaowat Hossain, Md Rashel Miah, & Md Mohibur Rahman. (2024). BLOCKCHAIN APPLICATIONS IN BUSINESS OPERATIONS AND SUPPLY CHAIN MANAGEMENT BY MACHINE LEARNING. International Journal of Computer Science & Information System, 9(11), 17–30. https://doi.org/10.55640/ijcsis/Volume09Issue11-03
Chen, Y., Zhang, W., & Li, H. (2021). Machine learning in financial risk management: A comprehensive review. Journal of Finance and Data Science, 7(2), 110-128.
Gupta, P., & Sharma, R. (2021). Predicting credit risk using machine learning: A comparison of traditional and modern approaches. International Journal of Financial Studies, 9(3), 123-136.
Al-Imran, M., Akter, S., Mozumder, M. A. S., Bhuiyan, R. J., Rahman, T., Ahmmed, M. J., ... & Hossen, M. E. (2024). EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE. The American Journal of Engineering and Technology, 6(09), 22-33.
Huang, X., Wang, Y., & Liu, J. (2021). Demand forecasting in retail banking using deep learning methods. Financial Analytics Journal, 5(4), 321-336.
Sweet, M. M. R., Ahmed, M. P., Mozumder, M. A. S., Arif, M., Chowdhury, M. S., Bhuiyan, R. J., ... & Mamun, M. A. I. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR ACCURATE LUNG CANCER PREDICTION. The American Journal of Engineering and Technology, 6(09), 92-103.
Jones, A., Smith, K., & Patel, R. (2020). Exploring linear models for operational cost forecasting in financial services. Management Science Quarterly, 36(1), 45-61.
DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY. (2024). International Interdisciplinary Business Economics Advancement Journal, 5(10), 13-27. https://doi.org/10.55640/business/volume05issue10-03
Kim, S., Park, D., & Lee, J. (2019). Stock price prediction using LSTM neural networks: A case study of the Korean financial market. Applied Intelligence, 49(2), 541-552.
Akter, S., Mahmud, F., Rahman, T., Ahmmed, M. J., Uddin, M. K., Alam, M. I., ... & Jui, A. H. (2024). A COMPREHENSIVE STUDY OF MACHINE LEARNING APPROACHES FOR CUSTOMER SENTIMENT ANALYSIS IN BANKING SECTOR. The American Journal of Engineering and Technology, 6(10), 100-111.
Lee, T., Chang, H., & Wong, P. (2020). Ensemble methods for operational cost forecasting: A study in financial institutions. Expert Systems with Applications, 160, 113656.
Nguyen, T., Tran, L., & Hoang, D. (2020). Fraud detection in banking using machine learning: A comparative study. Computers & Security, 95, 101869.
Md Parvez Ahmed, Md Arif, Abdullah Al Mamun, Fuad Mahmud, Tauhedur Rahman, Md Jamil Ahmmed, Sanjida Nowshin Mou, Pinky Akter, Muhammad Shoyaibur Rahman Chowdhury, & Md Kafil Uddin. (2024). A Comparative Study of Machine Learning Models for Predicting Customer Churn in Retail Banking: Insights from Logistic Regression, Random Forest, GBM, and SVM. Journal of Computer Science and Technology Studies, 6(4), 92–101. https://doi.org/10.32996/jbms.2024.6.4.12
Smith, R., & Brown, M. (2021). The evolution of cost forecasting methods in banking: From statistics to machine learning. Banking Review Journal, 44(3), 198-215.
Chowdhury, M. S., Shak, M. S., Devi, S., Miah, M. R., Al Mamun, A., Ahmed, E., ... & Mozumder, M. S. A. (2024). Optimizing E-Commerce Pricing Strategies: A Comparative Analysis of Machine Learning Models for Predicting Customer Satisfaction. The American Journal of Engineering and Technology, 6(09), 6-17.
Zhou, H., Lin, X., & Sun, Y. (2019). Credit scoring in the era of big data: Challenges and opportunities for machine learning. Big Data Research, 21(2), 78-90.
Miah, J., Ca, D. M., Sayed, M. A., Lipu, E. R., Mahmud, F., & Arafat, S. Y. (2023, November). Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction. In 2023 15th International Conference on Innovations in Information Technology (IIT) (pp. 49-54). IEEE.
Al-Imran, M., Ayon, E. H., Islam, M. R., Mahmud, F., Akter, S., Alam, M. K., ... & Aziz, M. M. (2024). TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS. The American Journal of Engineering and Technology, 6(11), 20-32.
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Copyright (c) 2024 Biswanath Bhattacharjee, Sanjida Nowshin Mou, Md Shakhaowat Hossain, Md Khalilor Rahman, Md Mehedi Hassan, Nabila Rahman, Refat Naznin, Md Ariful Islam Sarkar, Md Asaduzzaman, Md Sayem Ul Haque
This work is licensed under a Creative Commons Attribution 4.0 International License.