The Common Reporting Standard: an opportunity and a challenge for tax authorities


Cooperation and information sharing between tax authorities around the world can help ensure that taxpayers pay the right amount of tax to the right jurisdictions. The Common Reporting Standard (CRS) is an agreement between countries in the Organisation for Economic Co-operation and Development to collect and share data from their financial institutions annually.

455069151This remarkable achievement offers significant opportunities to eradicate financial crime and tax fraud. For example, if the standard is properly adapted by the tax authorities it will minimise risks involving non-residents tax evading on taxable assets and activity in non-residence countries.

OECD has proposed a timetable for when the agreement will become binding by each country, and it has published comprehensive guidance for the implementation of CRS. Current OECD guidance explains what information will be exchanged between tax authorities and the format with which the information should be exchanged.

Whilst the guidelines are very helpful and offer sufficient flexibility, the tax authorities need to decide how they will use and integrate the exchanged information in analytics solutions for detecting fraud. Although this might sound obvious, in practice is not. At least this is what the experience has shown from similar initiatives such as the use of third party bulk information by many tax authorities across OECD.

For example, the common reporting standard will make matching exchanged data with the internally held information easier. However, unaddressed challenges may delay the implementation of a working solution and the realisation of the benefits that CRS can bring.

If there is one lesson that tax authorities and other fraud detection organisations have learned from past experience of using third party information, it is that most taxpayer records / entities will not match to third party data by simply attempting matching fields containing names, surnames and addresses. This is a well-known fact and there are very few (if any) organisation or tax authority that will attempt such a primitive analytics implementation. But the question remains: What cutting edge analytics are available and how should they be leveraged for generating actionable insights leveraging CRS?

With a fraud detection framework that utilises hybrid analytics models and cutting edge entity resolution technology, tax authorities can develop global taxpayer analytical records for their residents, incorporating internally held data with the data provided by other tax authorities through CRS.

With this type of framework, the data provided by other tax authorities and third parties are bulk matched to the internal data through complex and bespoke layers of advanced analytics involving amongst other things social network analysis and indirect matching of entities based on common attributes that they share.

By not relying on the assumption that two records about an individual must share the same name and address, and by combining robust network development technology with analytics, your fraud framework can develop holistic taxpayer views. This forms the basis for risk scoring entities and networks through hybrid analysis utilising the entire portfolio of advanced analytics methods.

Ultimately, a fraud framework with data management and analytics built in can help agencies make the most of new information sources like CRS.


About Author

Principal Advisor

Thanos Alifantis is a principal Advisor at SAS, providing insight and thought leadership on compliance and fraud detection analytics. Thanos is an economist well-known for his work on advising HM Treasury & HM Revenue & Customs on how to design advanced analytics to inform compliance strategies. Thanos has 14 years of experience in consulting and advanced analytics having previously worked for the UK government as a lead economist. Thanos holds a PhD in Econometrics from the University of Warwick and a BSc in Economics and Operational Research from Lancaster University.

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