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How can Artificial Intelligence help close the VAT Gap

Persistent, huge VAT fraud issues push tax authorities to adopt Machine Learning and Generative Artificial Intelligence

National tax agencies in Europe and beyond are increasingly turning to Artificial Intelligence tools to help bring down stubborn Value Added Tax fraud leakage. The EU estimates it loses €50 billion per annum in VAT fraud, part of a wider multi-billion annual EU VAT Gap.

Watch VATCalc’s new AI VAT Advisor deliver tax advice (legislative references and tax cases) on complex VAT questions, and AI for VAT item classification.

To date, most of the AI models adopted are around Machine Learning (ML) – the use of huge amounts of data and algorithms to imitate the way that humans learn, gradually improving its accuracy of basic processes. For example, Poland’s System Teleinformatyczny Izby Rozliczeniowej (STIR), analysis data provided daily by banks and credit unions report account data and clearinghouse data on a daily basis for all transactions carried out by taxpayers. It enables the National Revenue Administration (KAS) to uncover potential missing-trader frauds in real-time, versus the two months that might have been needed previously.

Key areas where AI can assist tax authorities with VAT administration:

  • Risk Assessments: analysing huge volumes of sales and purchase transactions of taxpayers based on various factors such as historical data, industry benchmarks, and risk indicators. This helps tax authorities prioritize their resources and focus on high-risk areas that require closer scrutiny. In terms of the VAT Gap, this can include sudden surges of cross-border, intra-community transactions from new businesses, or abnormally large VAT deduction claims. This helps prioritise VAT audits and enforcement actions.
  • VAT fraud exposure: unearthing unusual or suspicious transactions, especially fraud-prone EU intra-community supplies. By analyzing vast amounts of data, AI algorithms can flag irregularities that may indicate fraudulent activities, such as false claims, tax evasion, or identity theft.
  • Large data analysis: AI, based on cloud-computing, can live process massive data loads to uncover potential fraudulent transactions. By adopting ML algorithms, tax authorities can detect patterns and anomalies in VAT returns, transactions, Intrastat, EC Sales Lists and similar, helping them identify potential areas of concern for further investigation.
  • VAT payer guidance: latest natural language tools can provide AI VAT advice to taxpayers without the need for large teams of VAT-expert. chatbots or virtual assistants can be used by tax authorities to provide automated responses to common VAT queries.  This can include:
    • VAT registration rules;
    • Returns obligations;
    • Deadlines;
    • Rights to deductibility; and
    • International VAT rules

Contact us to learn about how VATCalc is integrating Artificial Intelligence tools into our global tax engine to get invoice VAT calculations, audits and VAT return or e-invoices right.

Examples of tax authorities using Machine Learning and generative Artificial Intelligence

  • Romania claims to have boosted VAT receipts by up to 1% in the last 12 months via AI and robot adoption.
  • Italy, possibly the most fervent users of AI for detecting tax evasion, last year identified over 1 million high-risk cases with AI-driven data analysis. This includes a latest algorithm that cross references financial data to identify taxpayers at risk of not paying. Its VeRa algorithm compares tax filings, earnings, property records, bank accounts and electronic payments looking for discrepancies. High-risk taxpayers then receive a letter asking them to explain the differences. The more data VeRa processes, the smarter it becomes.
  • India from May 2023 is using AI to identify fraudulent applications for input tax credits via false GST registrations. The central government’s Business Intelligence and Fraud Analyst (BIFA) site, the e-way portal, and the Rajasthan government’s Business Intelligence Unit (BIU) would collaborate to detect GST numbers that appear to be false.
  • Malta, UK, Canada, the Netherlands and Ireland use an AI system that daily compares wealth based on public sources with that declared in their VAT and tax returns. It also sources public registers, bank accounts (in limited circumstances) to identify undeclared assets and spending.
  • Sweden deploys AI to identify and highlight tax risk issues when businesses apply for new incorporations. Since 2021, it has been able to review for tax avoidance flags in registration applications. This has also helped speed-up the application process by reducing the manual time required in reviewing documentation.
  • France uses AI satellite image scanning to identify signs conspicuous consumption. This can include multiple cars or swimming pools appearing at residents of person under tax investigation. This is particularly useful for local direct taxes (real estate tax).
  • Xenon is a tool used by six European countries to investigate tax evasion based on internet searches and surveillance. It was originally developed by The Netherlands
  • Brazil has been using AI behavioural insights to analyse the outcomes of varying standard tax letter requests to taxpayers.  It has been evaluating the response of the taxpayer based on their particular background and circumstance to determine the optimum tax communications tone and lever of affirmation. From this, the authorities are able to determine the best approach to take with future taxpayer queries or audits.
  • Aside from using AI to detect potential tax fraud or errors, most authorities are now using AI to assist the efficiency of their own compliance and administrative activities. This can include recruitment processes. Countries such as Canada and Singapore are leading the way on this.
  • As common on must large private sites, the tax authorities are increasingly using AI-driven virtual assistants. The list of countries includes: Spain, Peru, Australia, Canada, the United Kingdom, Ireland, Finland, Sweden, Latvia, Estonia, the Republic of China, Russia, Singapore, Guatemala, Chile, Mexico, Costa Rica, Colombia and Brazil.


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