Rule-Based Decision Engines for Automated Audit Sample Selection
Keywords:
Automated Audit Sampling, Rule-Based Decision Systems, Audit Automation, Computerized Auditing, Evidence-Based Audit Methods, Sampling Rule Design, Audit Decision Support, Professional Judgment Integration, Human-In-The-Loop Auditing, Sampling Threshold Specification, Audit Data Analytics, Efficiency And Effectiveness Evaluation, False Positive And False Negative Analysis, Audit Coverage Optimization, Processing Time Reduction, Resource Utilization Metrics, Intelligent Accounting Systems, Adaptive Audit Automation, Assurance Analytics, Technology-Enabled Auditing.Abstract
Automation of Audit Sampling Using Rule-Based Decision Systems: an objective, scholarly analysis of automated sampling approaches, evidence-based assessment, and formal structure. Automation and computerization in auditing are ubiquitous and offer unparalleled assistance to auditors in improving efficiency, effectiveness, and overall cost. Audit sampling is an effective means to make audit decisions based on part of the evidence rather than the whole. Rule-based decision systems are popular in many business and accounting areas, but not widely deployed for audit sampling yet.
Audit sampling can be automated by creating sampling rules based on audit data, professional guidelines, and/or judgment. Such rules specify sampling conditions and thresholds, the population elements that trigger the rule-set, the sampling specification produced, and the sample sizes required and whether the auditor should consider additional information of other related decisions. The approach also supports a human-in-the-loop function, providing the auditor with automation assistance but allowing judgment to deviate from the rules. The extent of automation can vary to meet auditors’ needs and is not limited only to the mention mode of rule-based systems. Effectiveness and efficiency can be evaluated in terms of accuracy, coverage, false positives, false negatives, processing time, and resource utilization.
References
1. Henderson, P., Chugg, B., Anderson, B., Altenburger, K., Turk, A., Guyton, J., Goldin, J., & Ho, D. E. (2023). Integrating reward maximization and population estimation: Sequential decision-making for Internal Revenue Service audit selection. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5087–5095.
2. Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2023). Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model. Financial Innovation, 9, 117.
3. Perdana, A., Lee, W. E., & Chu, M. K. (2023). Prototyping and implementing robotic process automation in accounting firms: Benefits, challenges and opportunities to audit automation. International Journal of Accounting Information Systems, 51, 100641.
4. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
5. Mökander, J. (2023). Auditing of AI: Legal, ethical and technical approaches. Digital Society, 2, 49.
6. Mökander, J., & Axente, M. (2023). Ethics-based auditing of automated decision-making systems: Intervention points and policy implications. AI & Society, 38(1), 153–171.
7. Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high-stakes AI predictive models. American Psychologist, 78(1), 36–49.
8. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
9. Lenders, D., & Calders, T. (2023). Functional requirements for interactive bias-audit tools. Frontiers in Artificial Intelligence and Applications, 369, 131–140.
10. Costanza-Chock, S., Harvey, E., Raji, I. D., Czernuszenko, M., & Buolamwini, J. (2023). Who audits the auditors? Recommendations from a field scan of the algorithmic auditing ecosystem.
11. Groves, L., Metcalf, J., Kennedy, A., Vecchione, B., & Strait, A. (2024). Auditing work: Exploring the New York City algorithmic bias audit regime.
12. Vasudevan, S., & Natarajan, V. (2026). Automated population-level audit assurance via AI-based document intelligence.
13. Inala, R., Sheelam, G. K., Aitha, A. R., Lakshmi, A. U., Nagabhyru, K. C., & Segireddy, A. R. (2026). Architecting Hybrid Data Products Using AI/ML and Agentic AI for Group Insurance and Retirement Solution Platforms with Advanced Data Governance. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1–6). IEEE. 2026 IEEE International Conference on AI Engineering and Innovations (AIEI). https://doi.org/10.1109/aiei69164.2026.11497971
14. Jones, E., Dragan, A., Raghunathan, A., & Steinhardt, J. (2023). Automatically auditing large language models via discrete optimization. Proceedings of the 40th International Conference on Machine Learning, 202, 15307–15329.
15. Henderson, P., Chugg, B., Anderson, B., Altenburger, K., Turk, A., Guyton, J., Goldin, J., & Ho, D. E. (2023). Sequential decision-making for intelligent audit selection using reward maximization. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5087–5095.
16. Perdana, A., Lee, W. E., & Chu, M. K. (2023). Robotic process automation for repetitive audit procedures in accounting firms. International Journal of Accounting Information Systems, 51, 100641.
17. Sudha Rani, P. R., Amistapuram, K., Pamisetty, V., Singireddy, S., Kummari, D. N., & Sheelam, G. K. (2025). Hybrid Knowledge Graph–Deep Learning Framework for Automated Exception Handling and Investigation in Complex Insurance Claims. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1–6). IEEE. 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN). https://doi.org/10.1109/gcwcn66157.2025.11448301
18. Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2023). AI governance and rule-based decision models for business auditing. Financial Innovation, 9, 117.
19. Mökander, J. (2023). Legal, ethical, and technical frameworks for AI auditing. Digital Society, 2, 49.
20. Mökander, J., & Axente, M. (2023). Policy implications of ethics-based audits for automated decision systems. AI & Society, 38(1), 153–171.
21. Landers, R. N., & Behrend, T. S. (2023). Fairness auditing for AI-supported decision systems. American Psychologist, 78(1), 36–49.
22. Lenders, D., & Calders, T. (2023). Interactive software tools for auditing automated decision systems. Frontiers in Artificial Intelligence and Applications, 369, 131–140.
23. Baladari, V., Nagubandi, A. R., Charan Teja Tadi, S. R. C., Selvi, A. T., Sreedevi, V., & Nithya, M. (2026). Predictive AI Model for Financial Risk Assessment in Dynamic Market Environments. In 2026 International Conference on Communication, Computing and Emerging Technologies (IC3ET) (pp. 748–753). IEEE. 2026 International Conference on Communication, Computing and Emerging Technologies (IC3ET). https://doi.org/10.1109/ic3et64989.2026.11467452
24. Groves, L., Metcalf, J., Kennedy, A., Vecchione, B., & Strait, A. (2024). Algorithmic bias auditing in employment decision systems. Working paper.
25. Vasudevan, S., & Natarajan, V. (2026). AI-based document intelligence for continuous audit assurance and transaction verification.
26. Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial intelligence in auditing: A conceptual framework for auditing practices. Administrative Sciences, 14(10), 238.
27. Sheu, G.-Y., & Liu, N.-R. (2024). Symmetrical and asymmetrical sampling audit evidence using a Naive Bayes classifier. Symmetry, 16(4), 500.
28. Gupta, D. K., Purushotham, K., Dheer, G., P, S., Gottimukkala, V. R. R., & Kapoor, S. (2025). Semantic Feature Learning Using Transformer-Based Deep Neural Networks. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1–6). IEEE. 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG). https://doi.org/10.1109/ictbig68706.2025.11323734
29. Wassie, F. A., & Lakatos, L. P. (2024). Artificial intelligence and the future of the internal audit function. Humanities and Social Sciences Communications, 11, 386.
30. Mokander, J. (2024). Auditing of AI: Legal, ethical and technical approaches. Digital Society.
31. Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024). AI auditing: The broken bus on the road to AI accountability.
32. Naveen, K., Lingam, R., Kunduru, G. R., Mangala, N., Reddy, N. N., & Balaji, P. (2026). Intelligent Loan Approval System: Machine Learning for Home and Education Loan Eligibility. In 2026 Contemporary Computing Innovations Conference (CCIC) (pp. 1–6). IEEE. 2026 Contemporary Computing Innovations Conference (CCIC). https://doi.org/10.1109/ccic68129.2026.11486013
33. Casper, S., Ezell, C., Siegmann, C., Kolt, N., Curtis, T. L., Bucknall, B., Haupt, A., Wei, K., Scheurer, J., Hobbhahn, M., Sharkey, L., Krishna, S., Von Hagen, M., Chan, A., Sun, Q., Gerovitch, M., Bau, D., Tegmark, M., Krueger, D., & Hadfield-Menell, D. (2024). Black-box access is insufficient for rigorous AI audits.
34. Ali, S., Kumar, V., & Breazeal, C. (2024). AI audit: A card game to reflect on everyday AI systems. Proceedings of the AAAI Conference on Artificial Intelligence, 38.
35. Narasimharao Davuluri, P. S. L., Segireddy, A. R., & Sheelam, G. K. (2026). Comment on “Effectiveness of AI-assisted ESI triage on accuracy and selected outcomes in emergency nursing: A systematic review.” International Emergency Nursing, 87, 101846. https://doi.org/10.1016/j.ienj.2026.101846
36. Sheu, G.-Y., & Liu, N.-R. (2024). Sampling audit evidence using a Naive Bayes classifier.
37. Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2023). Governance of artificial intelligence applications in business auditing via a fusion fuzzy multiple rule-based decision-making model. Financial Innovation, 9, 117.
38. Perdana, A., Lee, W. E., & Chu, M. K. (2023). Prototyping and implementing robotic process automation in accounting firms: Benefits, challenges and opportunities to audit automation. International Journal of Accounting Information Systems, 51, 100641.
39. Mökander, J., & Axente, M. (2023). Ethics-based auditing of automated decision-making systems: Intervention points and policy implications. AI & Society, 38(1), 153–171.
40. Bhavani, B. D., SR, S., Loganathan, R., & Nagaraj, S. (2026, April). Evolutionary Gravitational Neocognitron Neural Network, Snow Leopard Optimization and Deep Graph Reinforcement Learning for Routing Protocol in WSN. In 2026 2nd International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-8). IEEE.
41. Landers, R. N., & Behrend, T. S. (2023). Auditing AI systems: A framework for evaluating fairness and bias in high-stakes predictive models. American Psychologist, 78(1), 36–49.
42. Lenders, D., & Calders, T. (2023). Functional requirements for interactive bias-audit tools. Frontiers in Artificial Intelligence and Applications, 369, 131–140.
43. Henderson, P., Chugg, B., Anderson, B., Altenburger, K., Turk, A., Guyton, J., Goldin, J., & Ho, D. E. (2023). Integrating reward maximization and population estimation: Sequential decision-making for Internal Revenue Service audit selection. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5087–5095.
44. Jones, E., Dragan, A., Raghunathan, A., & Steinhardt, J. (2023). Automatically auditing large language models via discrete optimization. Proceedings of the International Conference on Machine Learning.
45. Peddi, R. K. Architecting Experience-Driven Infrastructure: Integrated Systems for Modern Service Delivery. JEC PUBLICATION.
46. Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial intelligence adoption in external auditing: Frameworks and implementation challenges. Administrative Sciences, 14(10), 238.
47. Sheu, G.-Y., & Liu, N.-R. (2024). Machine learning-based audit evidence selection for big data environments. Symmetry, 16(4), 500.
48. Wassie, F. A., & Lakatos, L. P. (2024). AI-enabled transformation of internal audit: Opportunities and governance challenges. Humanities and Social Sciences Communications, 11, 386.
49. Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry. (2026). Journal of European Economic History, 7(2), 68–88. https://doi.org/10.61336/jeeh/26-2-7 (Original work published as Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry)
50. Mokander, J. (2024). Contemporary approaches to AI auditing and governance. Digital Society.
51. Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024). Towards accountable AI auditing frameworks for high-risk decision systems.
52. Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56, 100734.
53. Garapati, R. S., Aitha, A. R., & Singireddy, S. (2026). Secure Cloud Architecture for Privacy-Preserving Machine Learning on Electronic Health Records with Web-Based Analytics Tools. In International Conference on Intelligent Human Computer Interaction (pp. 407-417). Springer, Cham.
54. Li, Y., & Goel, S. (2025). Bridging IT auditors and AI auditing: Understanding pathways to effective IT audits of AI-driven processes. Advances in Accounting, 69, 100842.
55. Tan, J., Chang, S., Zheng, Y., & Chan, K. C. (2025). Effects of artificial intelligence in the modern business: Client artificial intelligence application and audit quality. International Review of Financial Analysis, 104, 104271.
56. Subramanian, N., & Kolla, T. Equity in Healthcare Access Policy Lessons from Universal Coverage Models.
57. Law, K. K. F., & Shen, M. (2025). How does artificial intelligence shape audit firms? Management Science, 71(5), 3641–3666.
58. Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial intelligence in auditing: A conceptual framework for auditing practices. Administrative Sciences, 14(10), 238.
59. Mangalampalli, B. M., Kolla, S. K., Bandi, V. D. V. K., Yandamuri, U. S., & Rani, P. S. (2025). Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems. Vascular and Endovascular Review, 8(20s), 330-347.
60. Sheu, G.-Y., & Liu, N.-R. (2024). Symmetrical and asymmetrical sampling audit evidence using a Naïve Bayes classifier. Symmetry, 16(4), 500.
61. Wassie, F. A., & Lakatos, L. P. (2024). Artificial intelligence and the future of the internal audit function. Humanities and Social Sciences Communications, 11, 386.
62. Mattaparthi, R. (2022). From Raw Sensor to Business Signal: An Azure Databricks Framework for Diesel Engine Performance Analytics Across Global Fleet Operations. Journal of Artificial Intelligence & Cloud Computing, 1(4), 1.
63. Mökander, J. (2024). Auditing of AI: Legal, ethical and technical approaches. Digital Society.
64. Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024). AI auditing: The broken bus on the road to AI accountability.
65. Lam, K., Lange, B., Blili-Hamelin, B., Davidovic, J., Brown, S., & Hasan, A. (2024). A framework for assurance audits of algorithmic systems.
66. Kolla, S. K., Bandi, V. D. V. K., & Meda, R. (2026). Comment on" Serum FSTL-1 and AI-assessed muscle parameters in cancer-related malnutrition". Nutrition (Burbank, Los Angeles County, Calif.), 113259.
67. Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2023). Governance of artificial intelligence applications in business auditing via a fusion fuzzy multiple rule-based decision-making model. Financial Innovation, 9, 117.
68. Perdana, A., Lee, W. E., & Chu, M. K. (2023). Prototyping and implementing robotic process automation in accounting firms: Benefits, challenges and opportunities to audit automation. International Journal of Accounting Information Systems, 51, 100641.
69. Krishnan, M., Bandi, V. D. V. K., Mangala, N., Kolla, S. H., & Mangalampalli, B. M. Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry.
70. Henderson, P., Chugg, B., Anderson, B., Altenburger, K., Turk, A., Guyton, J., Goldin, J., & Ho, D. E. (2023). Integrating reward maximization and population estimation: Sequential decision-making for Internal Revenue Service audit selection. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5087–5095.
71. Jones, E., Dragan, A., Raghunathan, A., & Steinhardt, J. (2023). Automatically auditing large language models via discrete optimization. Proceedings of the International Conference on Machine Learning.
72. Mökander, J., & Axente, M. (2023). Ethics-based auditing of automated decision-making systems: Intervention points and policy implications. AI & Society, 38(1), 153–171.
73. Inala, R., Garapati, R. S., Aitha, A. R., Komaragiri, V. B., Gottimukkala, V. R. R., Recharla, M., ... & Varri, D. B. S. (2026). U.S. Patent Application No. 19/389,108.
74. Landers, R. N., & Behrend, T. S. (2023). Auditing AI systems: A framework for evaluating fairness and bias in high-stakes predictive models. American Psychologist, 78(1), 36–49.
75. Lenders, D., & Calders, T. (2023). Functional requirements for interactive bias-audit tools. Frontiers in Artificial Intelligence and Applications, 369, 131–140.
76. Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Artificial intelligence adoption in auditing: Challenges, governance, and future opportunities. International Journal of Accounting Information Systems, 56, 100734.
77. Reddy, V. A. R. Healthcare Systems Engineering Unified Platforms for Operational and Population Intelligence. JEC PUBLICATION.
78. Li, Y., & Goel, S. (2025). AI auditability measures and auditor competencies for AI-driven business processes. Advances in Accounting, 69, 100842
79. Tan, J., Chang, S., Zheng, Y., & Chan, K. C. (2025). Client AI applications and their implications for audit quality and assurance. International Review of Financial Analysis, 104, 104271.
80. Bandi, V. D. V. K. Autonomous Data Platforms: Converging AI, MLOps, and Cloud Engineering for Digital.
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