Big-data Analytics for Tax Compliance



Federal, state and local tax administration agencies are faced with the challenge of effectively utilize their limited resources to achieve maximal tax compliance from their taxpayer population. Audits are the primary means by which tax administration agencies ensure compliance to the laws that govern the various tax-types, and maintain the health of the associated revenue streams. A well-managed audit function can help tax agencies to achieve their revenue objectives.

The objective of a tax audit is to determine the amount of any adjustments that might be required to the tax dues reported by a taxpayer.  There are many decision-making tasks involved in the management of an effective tax-audit function.  Some of the decisions could span across the tax types; for example, how should the audit resources be allocated across the different tax types (sales tax, franchise tax, customs tax, personal income tax, etc)?  Some tax-types may have a higher yield per audit, or a greater level of non-compliance as compared to others.  Within a particular tax type, the foremost decision is to select the right set of taxpayer filings to audit. The vast quantity of data collected by every tax agency enables an array of potential applications for advanced data mining and analytical techniques, including:

Audit Selection: audit selection is one of the most important tasks for every tax administration agency. In order to maximize collections it is critical to utilize audit selection strategies that identify the most likely under-reporting taxpayers. Data mining can be used to create predictive models that, learning from past audits, help audit selectors identify the best audit candidates.

Lead Prioritization: cross-database matching techniques have been recently used to identify non-filers of various tax types. These processes can generate thousands of leads, which require a substantial amount of resources to verify non-filing status and tax responsibilities.  Data mining can help prioritize leads by ranking cases on the basis of potential tax revenue.

Taxpayer Profiling for Cross-Tax Affinity: among existing taxpayers, associations can be found between the type of business and the tax types for which the taxpayer is currently liable. Furthermore, co-occurrence of certain tax types may be used to infer the nexus to another tax type for which the taxpayer is not currently filing. This type of data mining application can be used for identifying non-filers.

Outlier-Based Detection: this analytical approach is broadly used in various types of audit or investigation selection processes. The idea is to select candidates on the basis or one or more metrics being "out of norm" compared to the entire population or, more typically, compared to some relevant "peer group". Data mining techniques such as clustering can be used to automatically generate natural peer groups based on a possibly large number of metrics. Outlier-based detection can be used in audit selection applications.

Workflow Analysis: lead follow-up can be a lengthy process consisting of multiple mail exchanges between the tax agency and an organization classified as a potential non-filer. Tracking this process generates useful data to model the process itself. These models can then be used to forecast the value of the current pipeline as well as to optimize resource allocation.

Anomaly Detection: Taxpayers self-report several important attributes (SIC, organization type, etc.). Due to unavoidable data entry errors, however, some taxpayers are categorized incorrectly or even uncategorized. Since these attributes and categories drive audit selection and other processes, it is always a good idea to apply data mining’s rule induction techniques to detect errors and anomalies.

Strategy and Optimization: As in most traditional target selection applications, there exists a cost trade-off between the number of audits to perform and the associated labor cost. Thus, given the cost information, economic analysis can be applied in combination with selection models to suggest optimal resource assignment strategies that maximize the overall returns.

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