AI-Driven Tax Compliance Detection
DOI:
https://doi.org/10.5281/zenodo.22874941Keywords:
Tax Compliance Investigations, Electronic Tax Filing Systems, Pattern Recognition Techniques, Compliance Behavior Analysis, Business Tax Filers, Individual Income Tax Filers, Anomaly Detection In Tax Data, Empirical Tax Analytics, Revenue Department Data, Behavioral Pattern Identification, Objective Compliance Assessment, Irregularity Detection, Audit Targeting Frameworks, Data-Driven Tax Administration, Resource Allocation Optimization, Compliance Risk Analysis, Electronic Filing Analytics, Investigative Tax Frameworks, Public Revenue Management, Evidence-Based Tax Policy.Abstract
Research on tax compliance is often anecdotal in nature. This study addresses that gap by applying pattern recognition techniques to data from the Thai Revenue Department's electronic tax filing system, examining the compliance behavior of both business and individual income tax filers. Rather than explaining the causes and consequences of tax compliance, the aim is narrower: to establish a framework and database for investigating compliance patterns through pattern recognition analysis.
The analysis uncovers clear behavioral patterns across both datasets, with business filers showing greater consistency than individual filers. It also surfaces anomalies and irregularities that point to specific records warranting deeper investigation. This approach offers a broad, objective view of typical filer behavior while flagging outliers for targeted follow-up.
While the resulting insights into Thai tax filers' compliance behavior are modest in scope, they hold practical value for tax administrators. More broadly, the framework is adaptable to wider research on electronic tax filing compliance, helping identify behavior that is anomalous and worthy of scrutiny. In doing so, it supports more effective resource allocation, allowing tax authorities to maximize the returns from compliance investigations while minimizing associated costs.
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