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New ‘Similar Business Test’ for Losses: Partnering with Data Analytics

New 'Similar Business Test' for Losses

KPMG have devised an automated technology solution to optimise the identification of business factors which can support the ‘similar business test’.


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With the introduction of the new ‘similar business test’ in Treasury Laws Amendment (2017 Enterprise Incentive No.1) Bill 2017, now is an opportune time to reconsider the traditional approach to applying the tax loss recoupment rules through an innovation lens.

A traditional approach to the analysis would involve labouring over documentation and manually gathering evidence to support the contention that a business is the ‘same’ or ‘similar’ over the relevant test period. KPMG have devised an automated technology solution to optimise the identification of business factors which can support the ‘same business test’ or ‘similar business test’ (collectively referred to as SBT). The combination of tax evidence gathering, tax technical, and data analytics perspectives ensure comprehensive, robust and efficient analysis is conducted to meet SBT.

How data analytics can optimise the SBT analysis

In carrying out a review of the SBT, KPMG has deployed data analytics solutions to optimise and regularly track business data to support an SBT analysis.

Data analytics has benefited SBT reviews in the following ways:

  • Using data extractors to quickly extract historic business-relevant data from the client’s Enterprise Resource Planning (ERP) systems and sub-systems to analyse trends and variances over time, with a view to address or identify risks of failing the SBT requirements.
  • Comparing and contrasting the footprint of Vendor, Customer and Product categories for the duration of the review.
  • Analysing and testing financial data to identify business change through income and expense movements, fixed assets purchases, disposals and transfers, goodwill, share capital movements and other financial trends.
  • Mining payroll and employee data and stratifying across the business to identify trends and variances.
  • Using publicly available data to compare market trends with business progress.

KPMG’s data analytics team utilises the latest information from our client’s business systems. Our data-driven approach allows detailed information to be tracked regularly and the results can be ‘refreshed’ or updated in future periods using the same source of information. Such results are presented in interactive dashboards which can then be used to support an SBT position paper.

Why is this important from a tax evidence perspective?

The use of data analytics allows us to work with a significant volume of raw data to assess trends in a company’s use of assets, its sources of revenue and other changes in its operations over time. These trends and any variances identified can be visually presented and documented in an SBT paper supported by the underlying data analysis.

In preparing evidence to support a taxpayer’s position to claim carry forward losses, it is necessary to be prepared with a depth of detailed analysis. This is especially important in a time when the Australian Taxation Office (ATO) has access to more taxpayer information than ever before. The ATO is, in today’s world:

  • increasing global co-operation and information sharing activities with revenue authorities around the world
  • ramping up its engagement of taxpayers through review and audit initiatives, for example, under the Justified Trust Initiative
  • demanding larger volumes of information than previously under its compliance and review activities with its requests for information being extensive and detailed
  • seeking real-time assurance of compliance by taxpayers using presently available company information.

In the context of satisfying the loss recoupment rules, being prepared with the level of detail and consistency of information that the ATO asks for, on tight timeframes, is more important now than ever. Given this, and with the ATO itself increasingly making use of electronic data gathering, businesses and their professional advisers can ensure they are on the front foot by doing the same.

KPMG’s other data analytics service offerings

KPMG routinely deploys deep dive business analytics. Our tax data analytics offerings include:

  • Capital Allowance Data Analytics (CADA) – Combines the expertise of KPMG tax specialists with sophisticated data analytics to identify issues and unlock opportunities for cash benefits in a client’s fixed asset register
  • GL Investigator Data Analytics (GLIDA) – Optimises the review of tax-sensitised general ledger accounts for the correct tax treatment at the transaction level
  • Employment Tax Data Analytics – Optimises the review, recalculation and recommendations of employment taxes, including superannuation contribution, PAYG, PRT, WorkCover, Employee Termination Payments, Contractors
  • Customs Data Analytics – Assesses the accuracy of declarations made by customs brokers on behalf of the company
  • GST Data Analytics – Aligns GST reviews with the testing performed by ATO Integrity of Business Systems (IBS) reviews
  • Tax Intelligence Solution (TIS) – KPMG’s global data analytics offering across Direct and Indirect Tax

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