Building AI-Driven Payroll Systems: Microservice Framework for Configurable Anomaly Detection and Real-Time Alerts
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 8 | Views: 1233 | Weekly Hits: ⮙5 | Monthly Hits: ⮙6

Research Paper | Information Technology | United States of America | Volume 14 Issue 1, January 2025 | Popularity: 5.6 / 10


     

Building AI-Driven Payroll Systems: Microservice Framework for Configurable Anomaly Detection and Real-Time Alerts

John Selvaraj Arulappan


Abstract: In the era of digital transformation, payroll systems play a critical role in ensuring accurate and efficient financial operations for organizations. However, these systems are prone to anomalies such as erroneous payments, fraudulent activities, and compliance violations, which can lead to significant financial and reputational risks. This paper presents a robust design framework for integrating AI-driven anomaly detection into payroll systems to optimize accuracy, efficiency, and security. By embedding anomaly detection into existing payroll automation frameworks, organizations can achieve proactive monitoring, rapid anomaly resolution, and enhanced decision-making capabilities. This study also addresses key challenges such as scalability, data quality, and integration complexities, while highlighting ethical considerations like data privacy and bias mitigation.


Keywords: Payroll processing Engine, Open AI, Anomaly Detection, Microservice


Edition: Volume 14 Issue 1, January 2025


Pages: 647 - 650


DOI: https://www.doi.org/10.21275/SR25116003203



Make Sure to Disable the Pop-Up Blocker of Web Browser


Text copied to Clipboard!
John Selvaraj Arulappan, "Building AI-Driven Payroll Systems: Microservice Framework for Configurable Anomaly Detection and Real-Time Alerts", International Journal of Science and Research (IJSR), Volume 14 Issue 1, January 2025, pp. 647-650, https://www.ijsr.net/getabstract.php?paperid=SR25116003203, DOI: https://www.doi.org/10.21275/SR25116003203

Similar Articles

Downloads: 1 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Research Paper, Information Technology, India, Volume 13 Issue 3, March 2024

Pages: 1943 - 1946

Leveraging Machine Learning for Personalization and Security in Content Management Systems

Venkata Sai Swaroop Reddy Nallapa Reddy

Share this Article

Downloads: 1 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Research Paper, Information Technology, United States of America, Volume 13 Issue 10, October 2024

Pages: 663 - 665

Healthcare Data Warehouses Empowered ML to Detect Anomalies

Arun Kumar Ramachandran Sumangala Devi

Share this Article

Downloads: 2 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Informative Article, Information Technology, India, Volume 9 Issue 5, May 2020

Pages: 1866 - 1869

Ensuring Data Integrity in Big Data Ingestion: Techniques and Best Practices for Data Quality Assurance

Sree Sandhya Kona

Share this Article

Downloads: 2 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Research Paper, Information Technology, United States of America, Volume 12 Issue 9, September 2023

Pages: 2219 - 2231

Zero-Day Threat Protection: Advanced Cybersecurity Measures for Cloud-Based Guidewire Implementations

Sateesh Reddy Adavelli

Share this Article

Downloads: 3 | Weekly Hits: ⮙1 | Monthly Hits: ⮙2

Research Paper, Information Technology, India, Volume 12 Issue 3, March 2023

Pages: 1855 - 1863

Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources

Muneer Ahmed Salamkar

Share this Article
Top