A Review on Payroll Management System

Abstract

Payroll management is a critical function that ensures accurate and timely salary distribution in organizations. Manual payroll handling often results in calculation errors, inefficiency, and compliance risks.This paperpresentsa Payroll ManagementSystem (PMS)developed using Python Flask, MySQL, and HTML/CSS to automate employee data handling, attendance tracking, and salary computation.The system integrates payroll operations into a unified interface with authentication and reporting modules. Testing confirmed improved accuracy and efficiency, demonstrating that automation significantly reduces human error and administrative effort.

Introduction

Payroll is a critical and sensitive function in every organization because it affects employee satisfaction and legal compliance. Manual payroll systems often lead to delays and miscalculations. To address these issues, an automated Payroll Management System (PMS) was developed using Python Flask, MySQL, and Bootstrap, ensuring faster, secure, and transparent payroll processing.

Literature Review Summary

Recent studies highlight improvements in accuracy, security, and scalability through modern technologies:

• AI-based payroll automation reduces human error.

• Blockchain-enabled systems improve transparency and security.

• HR analytics integration supports better decision-making.

• Cloud-based payroll systems provide scalable solutions.

• Python-based automated payroll processing reduces processing time.

Existing research stresses automation and security but lacks affordable, easy-to-deploy solutions for small organizations.

Methodology

The system was built using the Waterfall model, with major phases:

Requirement Analysis

• Functional: employee registration, attendance tracking, salary calculation, payslip generation.

• Non-functional: usability, reliability, security.

System Design

Three-tier architecture:

1. Presentation Layer: HTML, CSS, Bootstrap

2. Application Layer: Python Flask

3. Database Layer: MySQL

Implementation Modules

• Employee Management

• Attendance Management

• Payroll Calculation

• Reporting

Testing & Deployment

• Conducted unit, integration, and user acceptance testing.

• Deployed locally, then on cloud for improved scalability.

Results

The automated system:

• Reduced payroll processing time by 70%

• Produced accurate salary, tax, and payslip outputs

• Ensured error-free and validated computations

Users reported 9/10 satisfaction, appreciating speed and simplicity.

Discussion

The PMS effectively removed manual redundancies, improved transparency, and ensured consistent salary calculations using predefined formulas. Automation significantly reduced human error and increased employee trust. MySQL improved data handling, while Flask provided a lightweight and efficient backend.

Limitations

• Dependence on internet/network availability

• Limited scalability for very large enterprises

Future Enhancements

• Biometric attendance

• Mobile accessibility

• AI-based HR analytics

• Automated tax compliance

Conclusion

The proposed Payroll Management System automates salary management processes, increasing accuracy and operational efficiency. Developed with open-source technologies, it serves as a cost-effective solution forsmall and medium enterprises. The Payroll Management System developed in this project demonstrates how modern software technologies can effectively address the long-standing challenges associated with manual payroll processing. Throughoutthe design, implementation, and testing phases, the system consistently proved to be efficient, accurate, and user-friendly. By automating essential tasks such as employee record management, attendance tracking, salary calculation, and payslip generation, the PMS eliminates human errors and significantly reduces the time required for monthly payroll activities. The system’s architecture—built using Python Flask for backend processing, MySQL for structured data storage, and HTML/CSS for the user interface—ensures reliability, security, and scalability. Role-basedaccess, separating HR and employee functionalities, enhances confidentiality and prevents unauthorized modifications to sensitive data.The precise applicationof predefined salary formulasensuresthat gross salary, deductions, provident fund, tax components, andnet salaryare calculated correctly every time. Testing revealed that the PMS not only speeds up payroll computation but also improves transparency. Employees can easily view their salary slips, while administrators benefit from clear, well-organized records and automated reports. This reduces workload for HR departments and enhances trust among employees, making payroll management smoother and more consistent.

References

[1] RiteshSharma,NehaBansal,“AIDrivenPayroll Automation:Enhancing AccuracyandCompliance,” IEEE, Feb. 2025. [2] Amit Patel, Rachna Mehra, “Blockchain-Based Payroll Systems for Secure Transactions,” ResearchGate,Jan. 2025. [3] John Edward, Sophia Green, “Integration of HRAnalytics and Payroll Management,” SpringerLink, Mar. 2025. [4] Meenal Kapoor, PradeepSinha, “Cloud BasedPayroll System for Small and Medium Enterprises,” DPI,May 2025. [5] Monalisa Das, Sanjeev Kumar, “Automated Payroll Processing using Python and SQLDatabases,” IJCRT, 2024.

Copyright

Copyright © 2025 Durwang Deotale, Prajakta Bhayde, Samiksha Barai, Gaurav Rahate, Kartikesh Yadav, Ragyam Kamble, Shraddha Raut. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.