Data matching
106 Mandatory use of a unique identifier has long been fundamental to a revenue authority’s ability to track an individual’s compliance with tax obligations. The principle can be extended to business, even to the extent that all entities carrying on a business enterprise may be required to quote the number in business-to-business transactions and in reporting to the revenue authority.
Making the identifier openly accessible to the business community reinforces its value and assists in making it an integral part of normal business practice. The primary value to a revenue authority from the use of identifiers is the potential they offer for internal and external data matching to reveal areas of potential non-compliance.
Example
In 2000 Australia introduced a unique business number that is used by Government Agencies to identify businesses. This number is publicly disclosed via a Business Register for verification of a businesses authority to transact and is an essential element in matching data and information across Government Agencies and other external sources. This number must be present on every tax
invoice or else ‘No ABN’ withholding tax is incurred and no income tax credit for GST is available.
In Denmark the business number must be present on invoices and receipts according to the code of VAT. The number is publicly disclosed via a business register by name, address or number.
In Europe the VIES (VAT information exchange system) is in operation amongst all EU member countries. This network operates through a linked network / database structure and is supported by special liaison offices in each EU member country. An additional unique identifier, the VAT-ID that has to be used on tax invoices it allows to find out, if a supplier or customer is registered as a VAT entrepreneur or not, resulting in a different tax treatment (e.g. for VAT on services).
107 While the existence of a unique identifier is a valuable tool to assist the data matching process, it is not essential. Other data matching techniques that rely on the matching of combined pieces of data (for example, taxpayer name, address and date of birth) will also work.
108 Often data from disparate sources needs to be matched and linked together. The technology used needs to be sophisticated enough to create relevant comparisons from a large volume of data, if the value of that data is not to be diminished. Many techniques exist for the interpretation of data. Again, some may be more appropriate in some situations than in others.
Example
In Canada, a variety of linkages are made. Corporate income tax filers are linked to their major shareholders. They are also linked to their associated VAT, payroll, and importer accounts. Proprietors are linked to their spouses, and to the family income levels for the neighbourhoods in which they reside. Companies are also linked to a foreign affiliates and foreign transactions that were non-arms length.
In Austria, there is a link to a taxpayer's spouse and children through the use of the social security number (as a tax ID is regularly not available for this group). Furthermore any kind of company structures should be referenced to each other in the future by its ‘subject’-ID.
109 Relational databases and data warehouses facilitate risk identification and assessment by providing access to:
• disparate sources of data simultaneously;
• knowledge with which to make compliance inferences about the data using appropriate analytical techniques and methodologies; and
• strategic information and/or intelligence that allows rational prioritisation in researching and identifying risks.
110 Statistical analysis is often used to examine taxpayer data and to find the correlation between that data and non-compliance. It typically involves using the results of prior tax audits, which are then analysed in conjunction with taxpayer data.
Data mining
111 Data mining is the process of exploration and analysis, by automatic means, of large quantities of data in order to discover meaningful patterns and rules 7.
112 Typical techniques employed include such processes as neural networks and regression trees. Essentially, for a given set of data (including the results of past case audit activity), the data mining software is asked to distinguish the characteristics of taxpayers that have been non-compliant (usually on the basis of past audit results) from those that are compliant. The software can analyse thousands of characteristics simultaneously, and find patterns in the data that can be used to provide new criteria for identifying non-compliance. It is an example of how technology can be used to supplement human auditing experience.
113 Data mining typically involves the use of sophisticated computer software and integrated database structures.
Case review
114 Expert systems typically involve the use of experienced auditors to develop criteria that will identify possible non-compliance. When the developed criteria are matched against taxpayer data, potential non-compliance is identified and, in the case of some countries, the possible revenue risks estimated.
Example
In Canada, more than two hundred criteria-based risks have been identified. Here are two examples:
an area of concern in the rental income sector is the failure of landlords to use fair market pricing when making loss claims on residential property. The revenue authority identifies these situations in a number of ways, including the use of property tax and interest expense data to estimate the true market value of the property and its associated rental value;
unreported income is a key issue in the underground economy. A number of criteria are used to identify potential abuses, including comparison of the taxpayers reported income against what is reported from third parties, and comparison of taxpayers’ reported family incomes with those of families living in the same neighbourhood.
115 As an adjunct to electronic analysis of data, case-based systems can be used to help determine which cases deserve further examination. Experienced auditors generally access these systems. They use their local knowledge in deciding whether further compliance actions, such as audit, are warranted.
116 Systems should not ignore the value of local knowledge and front line involvement in risk identification, particularly when being used to support individual case selection. For example, in Canada, unusually low farming profits may be recognised as normal in certain areas owing to the prevalence of weather damage.
By definition, this knowledge is not available in national systems, so feedback
7 Berry and Linoff, 2000, Mastering Data Mining
generated by local analysis of risks identified in national systems is an invaluable aid in improving their quality.