Social Network Analysis to Optimize Tax Enforcement Effort

The tax gap is a phenomenon experienced by revenue collection agencies which describes the difference between the taxes due, as prescribed by legislation, and the actual taxes collected. The tax gap is mostly a result of taxpayer non-compliance, such as the failure to submit a tax return. Recent theories suggest that a taxpayer’s social structure is a significant determinant of a taxpayer’s attitude towards tax compliance. This study explores the proposal that social network analysis through decision support systems can facilitate the objective of revenue collection agencies to minimize the tax gap. The results suggest that an agency’s limited enforcement capacity can achieve a greater impact on tax compliance by focusing on non-compliant social structures as opposed to single instances of non-compliance. The research fills a gap in literature by demonstrating IT’s value proposition towards government financial services

A decision support system for rule discovery in social networks

Automatic rule discovery of membership in and relations between classes or categories, from given examples and counter-examples, is an important objective in many areas of computing. We have established a fairly general purpose programming environment for inductive rule discovery. It can be used, for example, as a high-level decision support system for social and cultural anthropologists. The program hypothesizes and then asserts rules that govern (permit or prohibit) social interactions and relations in human societies. We also describe the domain-independent facilities of the decision support system offered to the user and how the program works. 1