5b34b701936dd1eaf7-1a9e 20241128060000414 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 11 30 2024 13 4 Institut Universitaire de Technologies, Uniersite de Parakou, Parakou, Benin. Dr. Mikael A. Mousse https://orcid.org/0000-0002-3326-6396 This paper addresses the critical need to enhance the management of pension payments at Benins 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. 11 30 2024 11 30 2024 19 23 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. This article has not been sponsored or funded by any organization or agency. The independence of this research is a crucial factor in affirming its impartiality, as it has been conducted without any external sway. The data provided in this article is exempt from the requirement for ethical approval or participant consent. The adequate resources of this article are publicly accessible. The authorship of this article is contributed solely. 10.35940/ijrte.D8161.13041124 https://www.ijrte.org/portfolio-item/D816113041124/