Exploring the Use of Predictive Analytics by Austrian Tax Authorities: A Qualitative Study within the Task-Technology Fit Model

TL;DR

This study investigates how Austrian tax authorities utilize predictive analytics for tax audits, assessing the technology's fit with the task of detecting tax evasion through a qualitative analysis within the task-technology fit framework.

Contribution

It provides insights into the application of predictive analytics in tax auditing and evaluates its effectiveness within the task-technology fit model.

Findings

Predictive analytics helps identify suspicious tax cases more efficiently.

The technology shows a good fit with the task of tax evasion detection.

Qualitative insights reveal areas for improving analytics deployment.

Abstract

Taxes finance important government services that are now taken for granted in our society, such as infrastructure, health care, or retirement pensions. Tax authorities everywhere strive to ensure that all individuals and organizations comply with applicable tax laws. In this regard, tax authorities must prevent individuals and organizations from evading taxes in an illegal manner. To this end, Austrian tax authorities employ state-of-the-art predictive analytics technology for the selection of suspicious cases for tax audits, thus making efficient use of scarce resources for tax auditing. In this paper, we explore how Austrian tax authorities employ predictive analytics technology in tax auditing and how well the use of such technology fits the characteristics of the task at hand. We collaborated with the Austrian Federal Ministry of Finance's Predictive Analytics Competence Center to obtain insights into the application of predictive analytics technology by Austrian tax authorities. The thus obtained insights serve as the basis for a qualitative analysis in the context of the task-technology fit framework.