Published November 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

A Comprehensive Approach to Improving Enterprise Management Processes Through Applying Deep Learning Algorithms: A Case Study

Creators

  • 1. Institut Universitaire de Technologies, Uniersité de Parakou, Parakou, Benin.

Description

Abstract: This paper addresses the critical need to enhance the management of pension payments at Benin’s National Social Security Fund (NSSF), highlighting the pivotal role of deep learning methodologies in achieving optimization. As a key institution responsible for safeguarding the financial well-being of the population through social security programs, the NSSF faces unique challenges in managing the large volume of regular pension payments. This study introduces an innovative deep learning-based approach specifically tailored to the complexities of pension payment management at the NSSF in Benin. By leveraging advanced neural network architectures, our methodology aims to streamline and optimize the processes involved in pension disbursements, focusing on improving accuracy, efficiency, and overall operational effectiveness. Key components of this approach include analyzing historical pension payment data, demographic trends, and relevant socioeconomic indicators to develop predictive models. These models help forecast and adapt to changing patterns, ensuring timely and accurate pension payments. Through a detailed case study on the NSSF of Benin, we demonstrate the practical application of deep learning in addressing the specific challenges faced by public entities managing pension payments.

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Additional details

Identifiers

DOI
10.35940/ijrte.D8161.13041124
EISSN
2277-3878

Dates

Accepted
2024-11-15
Manuscript received on 26 September 2024 | Revised Manuscript received on 15 October 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024.

References

  • L. Liu, C. Chen, and B. Wang. Predicting financial crises with machine learning methods. Journal of Forecasting, 41(5), 871–910, doi: 10.1002/for.2840, 2022. https://doi.org/10.1002/for.2840
  • Marhwal, V., Bamel, P., & Agarwal, T. (2019). Predicting Crisis in Global Trade Network: An Enhanced Decision Tree Based Methods. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1s3, pp. 314–314). https://doi.org/10.35940/ijeat.a1059.1291s319
  • Jain, S., Saluja, Dr. N. K., Pimplapure, Dr. A., & Sahu, Dr. R. (2024). Exploring the Future of Stock Market Prediction through Machine Learning: An Extensive Review and Outlook. In International Journal of Innovative Science and Modern Engineering (Vol. 12, Issue 4, pp. 1–10). https://doi.org/10.35940/ijisme.e9837.12040424
  • Iyad Katib, Fatmah Y Assiri, Turki Althaqafi, Zenah Mahmoud AlKubaisy, Diaa Hamed, and Mahmoud Ragab. Hybrid hunter–prey optimization with deep learning-based fintech for predicting financial crises in the economy and society. Electronics, 12(16):3429, doi: 10.3390/electronics12163429, 2023. https://doi.org/10.3390/electronics12163429
  • Peiwan Wang and Lu Zong. Does machine learning help private sectors to alarm crises? Evidence from China's currency market. Physica A: Statistical Mechanics and its Applications, 611:128470, doi: 10.1016/j.physa.2023.128470, 2023. https://doi.org/10.1016/j.physa.2023.128470
  • Elizabeth Jane Casabianca, Michele Catalano, Lorenzo Forni, Elena Giarda, and Simone Passeri. A machine learning approach to rank the determinants of banking crises over time and across countries. Journal of International Money and Finance, doi: 10.1016/j.jimonfin.2022.102739, 2022. https://doi.org/10.1016/j.jimonfin.2022.102739
  • Aristeidis Samitas, Elias Kampouris, and Dimitris Kenourgios. Machine learning as an early warning system to predict financial crisis. International Review of Financial Analysis, 71:101507, doi: 10.1016/j.irfa.2020.101507, 2020. https://doi.org/10.1016/j.irfa.2020.101507
  • Bruno Miranda Henrique, Vinicius Amorim Sobreiro, and Herbert Kimura. Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124:226–251, doi: 10.1016/j.eswa.2019.01.012, 2019. https://doi.org/10.1016/j.eswa.2019.01.012
  • Mikael A Mousse, Bethel C A R K Atohoun, Cina Motamed. Deep learning-based approach for tomato classification in complex scenes, International Journal of Computer Theory and Engineering, 16(1), pp 29-34, doi: 10.7763/IJCTE.2024V16.1351, 2024. https://doi.org/10.7763/IJCTE.2024.V16.1351
  • Frejus A A Laleye, Mikael A Mousse. Attention-based recurrent neural network for automatic behavior laying hen recognition, Multimedia Tools and Applications, 83(22), pp. 62443-62458, doi: 10.1007/s11042-024-18241-9, 2024. https://doi.org/10.1007/s11042-024-18241-9
  • Pramila P Shinde and Seema Shah. A review of machine learning and deep learning applications. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pages 1–6. IEEE, doi: 10.1109/ICCUBEA.2018.8697857, 2018. https://doi.org/10.1109/ICCUBEA.2018.8697857
  • K P, S., & M, M. (2020). Prediction and Clustering Techniques used in the Development of Stock Forecasting Model. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 3, pp. 1937–1945). https://doi.org/10.35940/ijitee.c8922.019320