HRIS-Integrated Fraud Detection for U.S. Home Health Agency Workforce Compliance: Identifying Ghost Worker Schemes and Payroll Irregularities in Medicare and Medicaid-Funded Care Programs
DOI:
https://doi.org/10.22399/ijcesen.5496Keywords:
Human Resource Information Systems (HRIS), Healthcare fraud detection, ghost workers, Electronic Visit Verification (EVV), Medicare and Medicaid compliance, payroll irregularitiesAbstract
Background: Workforce-related fraud — ghost workers, post-termination payments, duplicate hours, and payroll–service mismatches — poses a distinct compliance risk for U.S. home health agencies operating under Medicare and Medicaid, separate from claims-level billing fraud. Human resource information systems (HRIS), scheduling, electronic visit verification (EVV), and payroll platforms are typically operated independently, making cross-system inconsistencies hard to detect through any single system's audit.
Objective: This study proposes and empirically evaluates an HRIS-integrated fraud detection framework that cross-reconciles employee identity, scheduling, EVV, and payroll data to detect ghost-worker schemes and payroll irregularities, comparing rule-based detection against machine learning and network-based collusion indicators.
Methods: A reproducible synthetic dataset of 20,264 employee-pay-period records was constructed for 900 employees and 500 patients over 12 months, including 14 simulated collusion rings sharing bank accounts or addresses and 719 fraudulent records (3.55%) across five documented typologies. Rule-based cross-system indicators, Random Forest, XGBoost, and Isolation Forest were comparatively evaluated on a temporally held-out test period, with SHAP explainability applied to the strongest classifier.
Results: Rule-based indicators alone achieved only 45.6% recall (F1 = 0.416) despite perfect precision, missing more than half of injected fraud. XGBoost achieved the strongest overall performance (F1 = 0.974, ROC-AUC = 0.9998), detecting 100% of ghost-worker and post-termination-payment cases and above 89% recall on every typology. The payroll–EVV hour gap and maximum inter-visit travel distance were the dominant predictors.
Conclusions: Cross-system reconciliation alone is necessary but insufficient; layering machine learning on top of HRIS-EVV-payroll integration substantially improves ghost-worker and payroll-irregularity detection over threshold rules alone, while network-based collusion indicators require care to avoid conflating legitimate shared household identifiers with fraud. Validation on operational agency data is the essential next step.
References
1. Cherkaoui, O., Anoun, H., & Maizate, A. (2024). A benchmark of health insurance fraud detection using machine learning techniques. IAES International Journal of Artificial Intelligence, 13(2), 1925–1934. https://doi.org/10.11591/ijai.v13.i2.pp1925-1934 DOI: https://doi.org/10.11591/ijai.v13.i2.pp1925-1934
2. Clark, K., George, A., & Lloyd, K. (2018). Trust (your employees), but verify (what they are doing) and keep the verification. Home Healthcare Now, 36(2), 132–133. https://doi.org/10.1097/NHH.0000000000000663 DOI: https://doi.org/10.1097/NHH.0000000000000663
3. du Preez, A., Bhattacharya, S., Beling, P., & Bowen, E. (2025). Fraud detection in healthcare claims using machine learning: A systematic review. Artificial Intelligence in Medicine, 160, 103061. https://doi.org/10.1016/j.artmed.2024.103061 DOI: https://doi.org/10.1016/j.artmed.2024.103061
4. Einav, L., Finkelstein, A., Ji, Y., Mahoney, N., & Moore, G. (2026). Medicare home health fraud: How much, where, and who? (NBER Working Paper No. 35280). National Bureau of Economic Research. https://doi.org/10.3386/w35280 DOI: https://doi.org/10.3386/w35280
5. Ekin, T. (2021). Health care fraud classifiers in practice. Applied Stochastic Models in Business and Industry, 37(1), 5–25. https://doi.org/10.1002/asmb.2633 DOI: https://doi.org/10.1002/asmb.2633
6. Evans, R. D., & Porche, D. A. (2005). The nature and frequency of Medicare/Medicaid fraud and neutralization techniques among speech, occupational, and physical therapists. Deviant Behavior, 26(3), 253–270. DOI: https://doi.org/10.1080/01639620590915167
7. Gallopyn, N., & Iezzoni, L. I. (2020). Views of Electronic Visit Verification (EVV) among home-based personal assistance services consumers and workers. Disability and Health Journal, 13(4), 100938. https://doi.org/10.1016/j.dhjo.2020.100938 DOI: https://doi.org/10.1016/j.dhjo.2020.100938
8. Goel, R. K. (2020). Medical professionals and health care fraud: Do they aid or check abuse? Managerial and Decision Economics, 41(6), 1071–1080. https://doi.org/10.1002/mde.3117 DOI: https://doi.org/10.1002/mde.3117
9. Hamid, Z., Khalique, F., Mahmood, S., Daud, A., & Bukhari, A. (2024). Healthcare insurance fraud detection using data mining. BMC Medical Informatics and Decision Making, 24, 112. https://doi.org/10.1186/s12911-024-02512-4 DOI: https://doi.org/10.1186/s12911-024-02512-4
10. Hancock, J. T., Bauder, R. A., Wang, H., & Khoshgoftaar, T. M. (2023). Explainable machine learning models for Medicare fraud detection. Journal of Big Data, 10, 154. https://doi.org/10.1186/s40537-023-00821-5 DOI: https://doi.org/10.1186/s40537-023-00821-5
11. Hong, B., Lu, P., Xu, H., Lu, J., Lin, K., & Yang, F. (2024). Health insurance fraud detection based on multi-channel heterogeneous graph structure learning. Heliyon, 10(9), e30045. https://doi.org/10.1016/j.heliyon.2024.e30045 DOI: https://doi.org/10.1016/j.heliyon.2024.e30045
12. Johnson, J. M., & Khoshgoftaar, T. M. (2019). Medicare fraud detection using neural networks. Journal of Big Data, 6, 63. https://doi.org/10.1186/s40537-019-0225-0 DOI: https://doi.org/10.1186/s40537-019-0225-0
13. Lu, J., Lin, K., Chen, R., Lin, M., Chen, X., & Lu, P. (2023). Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism. BMC Medical Informatics and Decision Making, 23, 62. https://doi.org/10.1186/s12911-023-02152-0 DOI: https://doi.org/10.1186/s12911-023-02152-0
14. Mardani, S., & Moradi, H. (2024). Using graph attention networks in healthcare provider fraud detection. IEEE Access, 12, 132786–132800. https://doi.org/10.1109/ACCESS.2024.3425892 DOI: https://doi.org/10.1109/ACCESS.2024.3425892
15. Nabrawi, E., & Alanazi, A. (2023). Fraud detection in healthcare insurance claims using machine learning. Risks, 11(9), 160. https://doi.org/10.3390/risks11090160 DOI: https://doi.org/10.3390/risks11090160
16. O'Malley, A. J., Bubolz, T. A., & Skinner, J. S. (2021). The diffusion of health care fraud: A network analysis (NBER Working Paper No. 28560). National Bureau of Economic Research. https://doi.org/10.3386/w28560 DOI: https://doi.org/10.3386/w28560
17. O'Malley, A. J., Bubolz, T. A., & Skinner, J. S. (2023). The diffusion of health care fraud: A bipartite network analysis. Social Science & Medicine, 327, 115927. https://doi.org/10.1016/j.socscimed.2023.115927 DOI: https://doi.org/10.1016/j.socscimed.2023.115927
18. Razzaq, K., & Shah, M. (2025). Next-generation machine learning in healthcare fraud detection: Current trends, challenges, and future research directions. Information, 16(9), 730. https://doi.org/10.3390/info16090730 DOI: https://doi.org/10.3390/info16090730
19. Shekhar, S., Leder-Luis, J., & Akoglu, L. (2026). Can machine learning target health care fraud? Evidence from Medicare hospitalizations. Journal of Policy Analysis and Management, 45(1), e70078. https://doi.org/10.1002/pam.70078 DOI: https://doi.org/10.1002/pam.70078
20. Wang, Z., Chen, X., Wu, Y., Jiang, L., & Lin, S. (2025). A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud. Scientific Reports, 15, 218. https://doi.org/10.1038/s41598-024-82062-x DOI: https://doi.org/10.1038/s41598-024-82062-x
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
