Integration of AI and ML in Payroll Processing System: Automating and Optimising Payroll Management through Intelligent Technologies
DOI:
https://doi.org/10.15662/IJEETR.2026.0802026Keywords:
Artificial Intelligence, Machine Learning, Payroll Management, Automation, Optimisation, Intelligent Systems, Human Resources (HR)Abstract
Payroll processing is a vital component of organisational management, encompassing salary computation, tax deduction, statutory compliance, and employee compensation management. Traditional payroll systems are largely rule-based and dependent on manual verification, which makes them vulnerable to errors, inefficiencies, and delayed compliance updates. The increasing complexity of payroll regulations and dynamic workforce requirements necessitates the adoption of intelligent and adaptive automation techniques
This paper presents an integrated Artificial Intelligence (AI) and Machine Learning (ML)-based payroll processing system designed to enhance accuracy, efficiency, and regulatory compliance. The proposed framework incorporates supervised learning models for salary and incentive prediction, anomaly detection algorithms for identifying payroll fraud and inconsistencies, and Natural Language Processing (NLP) techniques to automate employee payroll-related queries through an intelligent helpdesk
The system is evaluated using key performance metrics such as accuracy, processing time, and error rate, and is compared with conventional payroll systems. Experimental results indicate that the proposed AI-ML-based system significantly improves payroll accuracy, reduces processing time, and minimises manual intervention. The findings confirm the effectiveness of AI-driven payroll automation and highlight its potential for scalable deployment in modern enterprise environments
References
1. S. Dumancic, W. Meert, S. Goethals, T. Stuyckens, J. Huygen, and K. Denies, “Automated Reasoning and Learning for Automated Payroll Management”, AAAI, vol. 35, no. 17, pp. 15107-15116, May 2021.
2. J. R. Vummadi, H. Volikatla, and S. Dodda, “Smart HR for Smart Enterprises: A Machine Learning-Based Approach to Payroll Automation and Time Optimization”, IJAIDSML, vol. 6, no. 3, pp. 80–89, Aug. 2025, doi: 10.63282/3050-9262.IJAIDSML-V6I3P113.
3. R. Korrapati, "AI-Driven Transformation of Payroll Management: Enhancing Efficiency, Accuracy, and Compliance," SSRN, 2025.
4. H. K. Sriram, “Generative AI and Neural Networks in Human Resource Management: Transforming Payroll, Workforce Insights, and Digital Employee Payments through AI Innovations,” Advances in Consumer Research, vol. 2, no. 1, pp. 250-260, 2025.
5. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
6. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
7. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
8. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
9. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
10. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
11. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
12. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
13. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
14. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
15. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
16. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
17. H. K. Sriram, “Generative AI and Neural Networks in Human Resource Management: Transforming Payroll, Workforce Insights, and Digital Employee Payments through AI Innovations,” Advances Advances in Consumer Research, vol. 2, no. 1, 2025.
