A Knowledge Graph for Assessing Aggressive Tax Planning Strategies

TL;DR

This paper introduces a knowledge graph of multinational companies to identify and analyze aggressive tax planning strategies through subgraph queries and anomaly detection, enhanced by federated data from Wikidata.

Contribution

It presents a large-scale knowledge graph and a novel method for detecting tax planning strategies and anomalies in multinational corporations.

Findings

Identified known tax planning strategies using subgraph queries.

Detected anomalies indicating potential tax avoidance.

Enhanced analysis with federated Wikidata queries.

Abstract

The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states. Laws in different states may have unforeseen interaction effects, which can be exploited by allowing multinational companies to minimize taxes, a concept known as tax planning. In this paper, we present a knowledge graph of multinational companies and their relationships, comprising almost 1.5M business entities. We show that commonly known tax planning strategies can be formulated as subgraph queries to that graph, which allows for identifying companies using certain strategies. Moreover, we demonstrate that we can identify anomalies in the graph which hint at potential tax planning strategies, and we show how to enhance those analyses by incorporating information from Wikidata using federated queries.