Artificial Intelligence (AI)-Based Advance Models for Proactive Payroll Fraud Detection and Prevention

International Journal of Machine Learning, AI & Data Science Evolution [IJMLAIDSE]

Artificial Intelligence (AI)-Based Advance Models for Proactive Payroll Fraud Detection and Prevention

Authors : Sunil Jacob Enokkaren, Raghuvaran Kendyala, Jagan Kurma, Jaya Vardhani Mamidala, Varun Bitkuri, Avinash Attipalli

Open Access | Volume 1 Issue 1 | 2024

https://doi.org/10.63665/ijmlaidse-y1f1a001

How to Cite :

Sunil Jacob Enokkaren, Raghuvaran Kendyala, Jagan Kurma, Jaya Vardhani Mamidala, Varun Bitkuri, Avinash Attipalli, "Artificial Intelligence (AI)-Based Advance Models for Proactive Payroll Fraud Detection and Prevention", International Journal of Machine Learning, AI & Data Science Evolution [IJMLAIDSE], 2024, 1(1): pp. 1–11.
Journal Book

Abstract

Payroll fraud has remained one of the most widespread financial risks to organizations, directly affecting salary disbursement and undermining institutional trust. Conventional rule-based detection systems often struggle to handle the dynamic and evolving nature of such fraudulent activities. As payroll systems and financial transactions become increasingly digitalized, there is an urgent need for intelligent, adaptive detection mechanisms. This paper provides a comprehensive overview of payroll fraud types, traditional detection and prevention techniques, and the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in improving fraud detection efficiency. It explores the application of supervised, unsupervised, and reinforcement learning models, as well as deep learning architectures, for identifying anomalies and hidden patterns in payroll data. Furthermore, the study emphasizes best practices such as internal controls, employee awareness, and cybersecurity measures as complementary approaches to technological solutions. Challenges including data scarcity, privacy concerns, model explainability, and integration with legacy systems are analyzed. The paper concludes by outlining future research directions that highlight the importance of adaptive, explainable, and privacy-preserving AI systems for proactive and resilient payroll fraud prevention in modern digital ecosystems.

Keywords

Payroll Fraud, Fraud Detection, HR Systems, Anomaly Detection, Financial Security, ERP Systems, Machine Learning, Employee Trust.

Conclusion

Payroll fraud remains a major risk for organizations, especially as payroll systems grow more complex and digitized. Traditional approaches such as manual audits and rule-based checks offer limited protection and struggle to keep pace with evolving fraud tactics. AI and ML techniques particularly supervised, unsupervised, reinforcement, and deep learning models offer improved accuracy, adaptability, and scalability, enabling real-time detection of hidden fraud patterns and faster response to emerging threats. However, several challenges hinder large-scale adoption of AI in payroll fraud prevention. Key barriers include data scarcity, privacy concerns, regulatory compliance requirements, concept drift, explainability issues, and integration with legacy systems. The lack of publicly available payroll fraud datasets makes model training and benchmarking difficult, while opaque model decisions reduce trust in compliance-focused environments.

Future research should focus on privacy-preserving approaches such as federated learning and differential privacy to enable collaborative model training without exposing sensitive payroll data. Adaptive and incremental learning models can address concept drift, ensuring resilience against constantly evolving fraud schemes. Additionally, explainable AI techniques must be explored to enhance transparency, build trust with auditors and regulators, and support broader adoption of AI-driven payroll fraud prevention systems.

References

  1. C. C. Escolar-Jimenez, Data-Driven Decisions in Employee Compensation utilizing a Neuro-Fuzzy Inference System, Int. J. Emerg. Trends Eng. Res., vol. 7, no. 8, pp. 163–169, Aug. 2019, doi:s 10.30534/ijeter/2019/10782019.
  2. C. Jiang, J. Song, G. Liu, L. Zheng, and W. Luan, Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism, IEEE Internet Things J., vol. 5, no. 5, pp. 3637–3647, Oct. 2018, doi: 10.1109/JIOT.2018.2816007.
  3. V. Kanimozhi and T. P. Jacob, Artificial Intelligence-based Network Intrusion Detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing, ICT Express, 2019, doi: 10.1016/j.icte.2019.03.003.
  4. H. van Driel, Financial fraud, scandals, and regulation: A conceptual framework and literature review, Bus. Hist., vol. 61, no. 8, pp. 1259–1299, Nov. 2019, doi: 10.1080/00076791.2018.1519026.
  5. S. Qiu, H. Q. He, and Y. S. Luo, The value of restatement to fraud prediction, J. Bus. Econ. Manag., 2019, doi: 10.3846/jbem . 2019.10489.
  6. Gopi, Zero Trust Security Architectures for Large-Scale Cloud Workloads, Int. J. Res. Anal. Rev., vol. 5, no. 2, pp. 960–965, 2018.
  7. E. Stancheva, How Artificial Intelligence Is Challenging Accounting Profession, J. Int. Sci. Publ., vol. 12, pp. 126–141,2018.
  8. S. S. S. Neeli, Serverless Databases: A Cost-Effective and Scalable Solution, Int. J. Innov. Res. Eng. Multidiscip. Phys. Sci., vol. 7, no. 6, p. 7, 2019.
  9. P. A. Gutiérrez et al., Hybridizing logistic regression with product unit and RBF networks for accurate detection and prediction of banking crises, Omega, vol. 38, no. 5, pp. 333–344, Oct. 2010, doi: 10.1016/j.omega.2009.11.001.
  10. A. Kushwaha, P. Pathak, and S. Gupta, Review of optimize load balancing algorithms in cloud, Int. J. Distrib. Cloud Comput., vol. 4, no. 2, pp. 1–9, 2016.
  11. D. Choi and K. Lee, An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation, Secur. Commun. Networks, vol. 2018, no. 1, pp. 1–15, Sep. 2018, doi: 10.1155/2018/5483472.
  12. A. Thapliyal, P. S. Bhagavathi, T. Arunan, and D. D. Rao, Realizing Zones Using UPnP, in 2009 6th IEEE Consumer Communications and Networking Conference, 2009, pp. 1–5. doi: 10.1109/CCNC.2009.4784867.
  13. I. SADGALI, N. SAEL, and F. BENABBOU, Performance of machine learning techniques in the detection of financial frauds, Procedia Comput. Sci., vol. 148, pp. 45–54, 2019, doi: 10.1016/j.procs.2019.01.007.
  14. N. BenYoussef and S. Khan, Identifying fraud using restatement information, J. Financ. Crime, vol. 24, no. 4, pp. 620–627, Oct. 2017, doi: 10.1108/JFC-07-2016-0046.
  15. S. S. S. Neeli, The Significance of NoSQL Databases: Strategic Business Approaches and Management Techniques, J. Adv. Dev. Res., vol. 10, no. 1, p. 11, 2019.
  16. J. West and M. Bhattacharya, Intelligent financial fraud detection: A comprehensive review, Comput. Secur., vol. 57, pp. 47–66, Mar. 2016, doi: 10.1016/j.cose.2015.09.005.
  17. E. Boztepe and H. Usul, Using the Analysis of Logistic Regression Model in Auditing and Detection of Frauds, Khazar J. Humanit. Soc. Sci., vol. 22, no. 3, pp. 5–23, Dec. 2019, doi: 10.5782/2223-2621.2019.22.3.5.
  18. H. Kwon, Y. Kim, K.-W. ParkYoon, H. Yoon, and D. Choi, Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network, IEEE Access, vol. 6, pp. 46084–46096, 2018, doi: 10.1109/ACCESS.2018.2866197.
  19. M. Sanchez, J. Torres, P. Zambrano, and P. Flores, FraudFind: Financial fraud detection by analyzing human behavior, in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, Jan. 2018, pp. 281–286. doi: 10.1109/CCWC.2018.8301739.
  20. S. L. Domingos, R. N. Carvalho, R. S. Carvalho, and G. N. Ramos, Identifying it purchases anomalies in the Brazilian Government Procurement System using deep learning, in Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, 2017. doi: 10.1109/ICMLA.2016.106.
  21. E. H. Pascual, Continuous auditing to manage risks in payroll, in 2016 11th Iberian Conference on Information Systems and Technologies (CISTI), IEEE, Jun. 2016, pp. 1–6. doi:10.1109/CISTI.2016.7521578.
  22. V. Van Vlasselaer et al., APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions, Decis. Support Syst., vol. 75, pp. 38–48, Jul. 2015, doi: 10.1016/j.dss.2015.04.013.
  23. Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Tyagadurgam, M. S. V., & Gangineni, V. N. (2023). Scalable Deep Learning Algorithms with Big Data for Predictive Maintenance in Industrial IoT. International Journal of AI, BigData, Computational and Management Studies, 4(1), 88-97.
  24. Chalasani, R., Vangala, S. R., Polam, R. M., Kamarthapu, B., Penmetsa, M., & Bhumireddy, J. R. (2023). Detecting Network Intrusions Using Big Data-Driven Artificial Intelligence Techniques in Cybersecurity. International Journal of AI, BigData, Computational and Management Studies, 4(3), 50-60.
  25. Vangala, S. R., Polam, R. M., Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2023). A Review of Machine Learning Techniques for Financial Stress Testing: Emerging Trends, Tools, and Challenges. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 40-50.
  26. Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Tyagadurgam, M. S. V., Gangineni, V. N., & Pabbineedi, S. (2023). A Survey on Regulatory Compliance and AI-Based Risk Management in Financial Services. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 46-53.
  27. Bhumireddy, J. R., Chalasani, R., Vangala, S. R., Kamarthapu, B., Polam, R. M., & Penmetsa, M. (2023). Predictive Machine Learning Models for Financial Fraud Detection Leveraging Big Data Analysis. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 34-43.
  28. Gangineni, V. N., Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Tyagadurgam, M. S. V. (2023). AI-Enabled Big Data Analytics for Climate Change Prediction and Environmental Monitoring. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 71-79.
  29. Polam, R. M. (2023). Predictive Machine Learning Strategies and Clinical Diagnosis for Prognosis in Healthcare: Insights from MIMIC-III Dataset. Available at SSRN 5495028.
  30. Narra, B., Gupta, A., Polu, A. R., Vattikonda, N., Buddula, D. V. K. R., & Patchipulusu, H. (2023). Predictive Analytics in E-Commerce: Effective Business Analysis through Machine Learning. Available at SSRN 5315532.
  31. Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., Polu, A. R., Vattikonda, N., & Gupta, A. K. (2023). Advanced Edge Computing Frameworks for Optimizing Data Processing and Latency in IoT Networks. JOETSR-Journal of Emerging Trends in Scientific Research, 1(1).
  32. Patchipulusu, H. H. S., Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., & Buddula, D. V. K. R. (2023). Opportunities and Limitations of Using Artificial Intelligence to Personalize E-Learning Platforms. International Journal of AI, BigData, Computational and Management Studies, 4(1), 128-136.
  33. Madhura, R., Krishnappa, K. H., Shashidhar, R., Shwetha, G., Yashaswini, K. P., & Sandya, G. R. (2023, December). UVM Methodology for ARINC 429 Transceiver in Loop Back Mode. In 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-7). IEEE.
  34. Shashidhar, R., Kadakol, P., Sreeniketh, D., Patil, P., Krishnappa, K. H., & Madhura, R. (2023, November). EEG data analysis for stress detection using k-nearest neighbor. In 2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-7). IEEE.
  35. KRISHNAPPA, K. H., & Trivedi, S. K. (2023). Efficient and Accurate Estimation of Pharmacokinetic Maps from DCE-MRI using Extended Tofts Model in Frequency Domain.
  36. Krishnappa, K. H., Shashidhar, R., Shashank, M. P., & Roopa, M. (2023, November). Detecting Parkinson's disease with prediction: A novel SVM approach. In 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) (pp. 1-7). IEEE.
  37. Shashidhar, R., Balivada, D., Shalini, D. N., Krishnappa, K. H., & Roopa, M. (2023, November). Music Emotion Recognition using Convolutional Neural Networks for Regional Languages. In 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) (pp. 1-7). IEEE.
  38. Madhura, R., Krishnappa, K. H., Manasa, R., & Yashaswini, K. P. (2023, August). Slack Time Analysis for APB Timer Using Genus Synthesis Tool. In International Conference on ICT for Sustainable Development (pp. 207-217). Singapore: Springer Nature Singapore.
  39. Krishnappa, K. H., & Gowda, N. V. N. (2023, August). Dictionary-Based PLS Approach to Pharmacokinetic Mapping in DCE-MRI Using Tofts Model. In International Conference on ICT for Sustainable Development (pp. 219-226). Singapore: Springer Nature Singapore.
  40. Krishnappa, K. H., & Gowda, N. V. N. (2023, August). Dictionary-Based PLS Approach to Pharmacokinetic Mapping in DCE-MRI Using Tofts Model. In International Conference on ICT for Sustainable Development (pp. 219-226). Singapore: Springer Nature Singapore.
  41. Madhura, R., Krutthika Hirebasur Krishnappa. et al., (2023). Slack time analysis for APB timer using Genus synthesis tool. 8th Edition ICT4SD International ICT Summit & Awards, Vol.3, 207–217. https://doi.org/10.1007/978-981-99-4932-8_20
  42. Shashidhar, R., Aditya, V., Srihari, S., Subhash, M. H., & Krishnappa, K. H. (2023). Empowering investors: Insights from sentiment analysis, FFT, and regression in Indian stock markets. 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), 01–06. https://doi.org/10.1109/AIKIIE60097.2023.10390502
  43. Jayakeshav Reddy Bhumireddy, Rajiv Chalasani, Mukund Sai Vikram Tyagadurgam, Venkataswamy Naidu Gangineni, Sriram Pabbineedi, Mitra Penmetsa. Predictive models for early detection of chronic diseases in elderly populations: A machine learning perspective. Int J Comput Artif Intell 2023;4(1):71-79. DOI: 10.33545/27076571.2023.v4.i1a.169
  44. HK, K. (2020). Design of Efficient FSM Based 3D Network on Chip Architecture. INTERNATIONAL JOURNAL OF ENGINEERING, 68(10), 67-73.
  45. Krutthika, H. K. (2019, October). Modeling of Data Delivery Modes of Next Generation SOC-NOC Router. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.
  46. Ajay, S., Satya Sai Krishna Mohan G, Rao, S. S., Shaunak, S. B., Krutthika, H. K., Ananda, Y. R., & Jose, J. (2018). Source Hotspot Management in a Mesh Network on Chip. In VDAT (pp. 619-630).
  47. Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv preprint arXiv:1001.3781.
  48. Gopalakrishnan Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv e-prints, arXiv-1001.
  49. Krutthika H. K. & A.R. Aswatha. (2021). Implementation and analysis of congestion prevention and fault tolerance in network on chip. Journal of Tianjin University Science and Technology, 54(11), 213–231. https://doi.org/10.5281/zenodo.5746712
  50. Kuraku, Dr Sivaraju, et al. Exploring how user behavior shapes cybersecurity awareness in the face of phishing attacks. International Journal of Computer Trends and Technology (2023).
  51. Kuraku, D. S., & Kalla, D. (2023). Impact of phishing on users with different online browsing hours and spending habits. International Journal of Advanced Research in Computer and Communication Engineering, 12(10).
  52. Kalla, D., & Samaah, F. (2023). Exploring Artificial Intelligence And Data-Driven Techniques For Anomaly Detection In Cloud Security. Available at SSRN 5045491.
  53. Chandrasekaran, A., & Kalla, D. (2023). Heart disease prediction using chi-square test and linear regression. Comput. Sci. Inform. Technol., 13, 135-146.
  54. Kalla, D. (2022). AI-Powered Driver Behavior Analysis and Accident Prevention Systems for Advanced Driver Assistance. International Journal of Scientific Research and Modern Technology (IJSRMT) Volume, 1.
  55. Rajiv, C., Mukund Sai, V. T., Venkataswamy Naidu, G., Sriram, P., & Mitra, P. (2022). Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic. J Contemp Edu Theo Artific Intel: JCETAI/102.
  56. Sandeep Kumar, C., Srikanth Reddy, V., Ram Mohan, P., Bhavana, K., & Ajay Babu, K. (2022). Efficient Machine Learning Approaches for Intrusion Identification of DDoS Attacks in Cloud Networks. J Contemp Edu Theo Artific Intel: JCETAI/101.
  57. Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2020). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153–164.DOI: 10.31586/jaibd.2022.1341
  58. Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340
  59. Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2022). Designing an Intelligent Cybersecurity Intrusion Identify Framework Using Advanced Machine Learning Models in Cloud Computing. Universal Library of Engineering Technology, (Issue).
  60. Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2022). Leveraging Artificial Intelligence Algorithms for Risk Prediction in Life Insurance Service Industry. Available at SSRN 5459694.
  61. Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 2(3), 70-80.
  62. Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Tyagadurgam, M. S. V. Efficient Framework for Forecasting Auto Insurance Claims Utilizing Machine Learning Based Data-Driven Methodologies. International Research Journal of Economics and Management Studies IRJEMS, 1(2).
  63. Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H. S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of Applications, Challenges, and Innovations. International Journal of Emerging Research in Engineering and Technology, 3(3), 99-107.
  64. Narra, B., Vattikonda, N., Gupta, A. K., Buddula, D. V. K. R., Patchipulusu, H. H. S., & Polu, A. R. (2022). Revolutionizing Marketing Analytics: A Data-Driven Machine Learning Framework for Churn Prediction. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 112-121.
  65. Polu, A. R., Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., Vattikonda, N., & Gupta, A. K. BLOCKCHAIN TECHNOLOGY AS A TOOL FOR CYBERSECURITY: STRENGTHS, WEAKNESSES, AND POTENTIAL APPLICATIONS.
  66. Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2022). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153–164.DOI: 10.31586/jaibd.2022.1341
  67. Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340
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