Читать книгу Global Tax Governance. Taxation on Digital Economy, Transfer Pricing and Litigation in Tax Matters (MAPs + ADR) Policies for Global Sustainability. Ongoing U.N. 2030 (SDG) and Addis Ababa Agendas онлайн

84 страница из 100

This differs from traditional software development as an algorithm is learned rather than a programmer creating an algorithm that is then used to predict an outcome. We contrast the two approaches below:

Illustration 1

This process is known as supervised learning, and it relies on the assumption a representative dataset with correct input and outputs is available from which learning based upon pattern mining can occur. An example of this approach in use is Chile which implemented a national mandatory clearance-based e-invoicing system. Invoice data runs through a machine learning algorithm which picks up the patterns between the invoice item descriptions and tax rates. When the algorithm is applied to new invoices, it reads each invoice line item and (risk) assesses whether the tax is correct. In this manner, prior data can be used to inform predictions against new data. This capability can be applied to text, numbers, and images.

This approach provides not only a predicted result but also a probability between 0 and 1 of how much confidence should be placed in the prediction. This enables low probability predictions to be routed to humans and screened manually. This combination of machine learning prediction and manual screening of select items can vastly enhance breadth of audit risk assessment efforts. Item scores can aggregate to invoice and taxpayer scores. Limited human resources can then be applied to the most impactful reviews as indicated by scoring analytics.