AI-Powered and Machine Learning for Tax Compliance and Fraud Detection in U.S. Small Businesses
Abstract
Small businesses are the backbone of the U.S. economy, yet they face growing pressure to
navigate an increasingly complex tax environment. As regulations evolve, digital
transactions expand, and audit scrutiny intensifies, traditional compliance methods are no
longer enough. Errors, inefficiencies, and fraud risks—whether intentional or accidental—
can threaten the survival of even the most well-run enterprises. At the same time, advances
in artificial intelligence and machine learning offer powerful new tools capable of
transforming how small businesses manage their financial obligations.
This book, AI-Powered and Machine Learning for Tax Compliance and Fraud
Detection in U.S. Small Businesses, aims to demystify these technologies and show how
they can bring clarity, accuracy, and security to the compliance process. Rather than
focusing on theory alone, the chapters explore practical applications: automated transaction
classification, anomaly detection, predictive risk scoring, document extraction, and
intelligent audit readiness.
Designed for entrepreneurs, accountants, technologists, and policymakers, this book provides
a roadmap for adopting AI responsibly and effectively. Its core message is simple: intelligent
automation can reduce burdens, prevent costly mistakes, and strengthen financial integrity.
By embracing AI, small businesses can shift from reactive compliance to proactive, data
driven decision-making—and build a more resilient future in an increasingly digital
financial landscape.
References
1. Dalal, R. S., Howard, D. J., Bennett, R. J., Posey, C., Zaccaro, S. J., & Brummel, B. J. (2022). Organizational science and cybersecurity: Abundant opportunities for research at the interface. Journal of Business Psychology, 37(1), 1–29. https://doi.org/10.1007/s10869-021-09732-9
2. Ifinedo, P. (2022). Effects of security knowledge, self-control, and countermeasures on cybersecurity behaviors. Journal of Computer Information Systems, 63(2), 380–396. https://doi.org/10.1080/08874417.2022.2065553
3. Tursunbayeva, A., et al. (2022). AI in human resource management and cybersecurity implications. International Journal of Information Management, 65, 102518.
4. Gkorezis, P., & Bellou, V. (2022). AI adoption and employee cybersecurity behavior. Journal of Business Research, 145, 874–885.
5. Marler, J. H., & Boudreau, J. W. (2022). An evidence-based review of AI and HRM security risks. Human Resource Management Review, 32(4), 100898.
6. Huang, M. H., & Rust, R. T. (2023). AI transformation of service work and employee behavior. Journal of Service Research, 26(2), 125–140.
7. Gajula, S. (2025). Cybersecurity Risk Prediction Using Graph Neural Networks. Authorea Preprints.
8. Malik, A., Budhwar, P., & Srikanth, N. (2023). Artificial intelligence and cybersecurity in human resource management. Human Resource Management Review, 33(1), 100910.
9. Dwivedi, Y. K., et al. (2023). Artificial intelligence: Multidisciplinary perspectives on cybersecurity and organizational impacts. International Journal of Information Management, 69, 102553.
10. Stahl, B. C. (2023). Ethical implications of AI-driven cybersecurity in organizations. Journal of Business Ethics, 182(3), 721–735.
11. Lee, J., & Shin, S. (2024). AI-driven decision-making and organizational structure in cybersecurity. Decision Support Systems, 172, 113982.
12. Verganti, R., Vendraminelli, L., & Iansiti, M. (2023). AI platforms and organizational redesign in security systems. Organization Science, 34(5), 1821–1840.
