28 November 2025Yavuz AKBULAK

The Fight Against Counterfeit Documents in Türkiye: A Brand New Organization-Supervised Analysis System (OSAS)

Summary

The purpose of this article is to announce that a new era in the fight against counterfeit documents was launched by the Tax Inspection Board of the Ministry of Treasury and Finance in Türkiye on October 1, 2025. This board implemented a paradigm shift by introducing a multi-layered surveillance and electronic audit (e-audit) model in the fight against counterfeit documents. They announced that tax audits would shift from a retrospective model to a current period model, and that from this date onward, the burden of proof would fall on the taxpayer as the transaction party. To this end, they established the philosophical foundation for the work to be done within the multi-layered approach model, and implemented the Organization-Supervised Analysis System (OSAS) as a risk analysis method.

The primary objective of the OSAS system is to combat those who create and use counterfeit documents. This fight requires suppressing the demand for counterfeit invoices. To achieve this goal, an approach will be implemented that prioritizes the use of counterfeit invoices, while unintentional use is the exception. The OSAS is a centralized tax risk analysis system that works with digital and electronic data. This new system will prevent significant tax evasion through forged documents. It is also important for tax administration professionals and tax advisers.

This article will focus on the general principles of the OSAS.

Box 1. Definitions of the Basic Concepts Used in the Text
Before we move on to discuss the basic principles, we will define some of the basic concepts mentioned in this article. Business philosophy: The set of principles by which the Turkish tax administration works to combat fraudulent documents and achieve success in its strategy. False document: A document that appears to describe an actual transaction or situation, even though it does not. False document user ('user'): A person who enters a false document into accounting records and/or uses it for other purposes in other transactions. Perception: Perception is the process of organising and interpreting sensory data in order to make sense of the objects and events around us. Perception also involves becoming aware of something, attributing it to our knowledge system and judging and evaluating the phenomenon in question, both qualitatively and quantitatively. Tax perception is part of tax psychology and reflects people's views on taxes. Viewing taxes as the price of public services is crucial for understanding and correctly perceiving the logic of taxes. Recognising that taxes benefit society and that they will be returned to society in the form of services fosters a positive outlook on taxes. Perception management: Perception management is the most important control tool in the fight against counterfeit documents. This involves raising awareness of all policies relating to the new control measures for counterfeit document revenue, communicating these policies to all relevant individuals through various channels and media, and collaborating with organisations such as the judiciary, law enforcement and the Chambers of Industry and Commerce to support the fight against counterfeiting. Risk analysis: This involves increasing the efficiency of audit activities by optimally utilising limited time and resources, and enhancing functionality by utilising existing data in the data warehouse more effectively for analysis. Tax: Money collected from citizens by the state or local governments in accordance with the law, either directly or indirectly through adding it to the prices of certain goods and services. This money is then spent on public services. Tax administration: Turkish Tax Administration. Tax audit: The examination of a taxpayer's books, accounts, documents and other records to investigate, determine and ensure the accuracy of taxes due. Taxpayer: A natural or legal person who has committed an act that gives rise to a tax liability and is thus obligated to pay the tax debt.

Box 1. Definitions of the Basic Concepts Used in the Text

Before we move on to discuss the basic principles, we will define some of the basic concepts mentioned in this article.

Business philosophy: The set of principles by which the Turkish tax administration works to combat fraudulent documents and achieve success in its strategy.

False document: A document that appears to describe an actual transaction or situation, even though it does not.

False document user ('user'): A person who enters a false document into accounting records and/or uses it for other purposes in other transactions.

Perception: Perception is the process of organising and interpreting sensory data in order to make sense of the objects and events around us. Perception also involves becoming aware of something, attributing it to our knowledge system and judging and evaluating the phenomenon in question, both qualitatively and quantitatively. Tax perception is part of tax psychology and reflects people's views on taxes. Viewing taxes as the price of public services is crucial for understanding and correctly perceiving the logic of taxes. Recognising that taxes benefit society and that they will be returned to society in the form of services fosters a positive outlook on taxes.

Perception management: Perception management is the most important control tool in the fight against counterfeit documents. This involves raising awareness of all policies relating to the new control measures for counterfeit document revenue, communicating these policies to all relevant individuals through various channels and media, and collaborating with organisations such as the judiciary, law enforcement and the Chambers of Industry and Commerce to support the fight against counterfeiting.

Risk analysis: This involves increasing the efficiency of audit activities by optimally utilising limited time and resources, and enhancing functionality by utilising existing data in the data warehouse more effectively for analysis.

Tax: Money collected from citizens by the state or local governments in accordance with the law, either directly or indirectly through adding it to the prices of certain goods and services. This money is then spent on public services.

Tax administration: Turkish Tax Administration.

Tax audit: The examination of a taxpayer's books, accounts, documents and other records to investigate, determine and ensure the accuracy of taxes due.

Taxpayer: A natural or legal person who has committed an act that gives rise to a tax liability and is thus obligated to pay the tax debt.

Introduction

The business strategy for combatting counterfeit documents was implemented in Türkiye on 1 October 2025.

The most important aspect of the business strategy for combatting counterfeit documents is establishing a philosophical basis for addressing the issue. In this context, the Organisation for Economic Co-operation and Development's (OECD) Ten Global Principles for Combating Tax Crimes and Institutional Memory were utilised and successful country practices were observed to develop a new strategy. The root of the problem lies with taxpayers who issue counterfeit documents. Current methods of tackling this issue struggle to identify the real perpetrators, while organised criminal organisations remain hidden in the background. In line with the OECD Principles, treating the issuance of counterfeit documents as a predicate offence would enable the use of a range of police powers, including technical surveillance, to combat criminal organisations more effectively. Measures such as confiscation, seizure and freezing are intended to disrupt the economic aspect of this crime and cut off criminal organisations' financing sources.

As emphasised above, this initiative is heavily influenced by the OECD's Fighting Tax Crime: The Ten Global Principles[1], first published in 2017 and updated in 2021. The principles are:

Ensure that tax offences are criminalised.
Devise an effective strategy for addressing tax crimes.

Have adequate investigative powers.
Have effective powers to freeze, seize and confiscate assets.
Put in place an organisational structure with defined responsibilities.
Provide adequate resources for tax crime investigation.
Make tax crimes a predicate offence for money laundering.

Have an effective framework for domestic inter-agency cooperation.
Ensure that international cooperation mechanisms are available.
Protect suspects' rights.

The Basic Fundamentals of the Business Strategy for Combating Counterfeit Documents[2]

The root cause of the problem of forged documents is a lack of regulatory oversight. Current methods of tackling this issue pose significant challenges in identifying the true perpetrators, while organised crime groups continue to operate in the background. Treating forged documents as a predicate offence[3], in line with OECD principles, would allow a range of police powers, including technical surveillance, to be utilised more effectively against criminal organisations. Measures such as the confiscation, seizure and freezing of assets are intended to discourage the economic aspect of this crime and disrupt the financing of criminal organisations.

In Türkiye, Article 140/A was added to the Tax Procedure Law (TPL) No. 213[4] in order to distinguish between tax investigations and investigations relating to tax crimes. This distinguishes the procedures and principles to be followed in regular tax investigations from those involving criminal acts.

Combating fraudulent documents requires modern risk and data analysis techniques. The Organisation Controlled Analysis System[5] (OSAS), which uses the most up-to-date data to measure transaction risk, is set to be implemented on 1 October 2025. The aim of this system is to quickly identify potential fraud risks. Another system, the Tax Intelligence System (TIS), will actively monitor taxpayers involved in fraudulent activities and provide key input[6] to the OSAS.

Beyond the field inspections conducted through the OSAS system, the remainder of current audits consists of adversarial investigations based on the authority to request information. The Tax Inspection Board sends information request letters to taxpayers. These letters, the information obtained from them, and interviews with taxpayers are used as input for the risk analysis system. Unnecessary audits are prevented by identifying taxpayers whose accounting records do not contain false documents or who submit correction declarations[7]. These letters also ensure that taxpayers are aware of their own risk status, helping them to avoid a potential audit by enabling them to assess their own risk and make choices such as submitting a correction return.

In the new period after the OSAS comes into force on 1 October 2025, measures such as reporting automation and existing audits will shorten the review phase for regulators. Reviews will be enhanced and appropriately timed to ensure that, despite all signals[8], taxpayers who reach the review stage on suspicion of being a user are subject to fair assessment. The workflow for investigating cases of fraudulent documents will be restructured from the user to the issuer, starting with current users and then moving to current issuers, with the help of the OSAS and existing audits. The advantage of using fraudulent documents due to the lengthy audit time will be eliminated with the help of the report automation system.

All the recommended measures, policies and strategies set out in the Anti-Counterfeiting Strategy and the OSAS essentially aim to change attitudes towards counterfeit documents. Perception management is the most important tool for overseeing the fight against counterfeit documents. The Turkish Tax Inspection Board's[9] new auditing practices regarding counterfeit documents will be communicated to all stakeholders through various channels[10]. Collaborative efforts will be made with key stakeholders, including the judiciary, law enforcement, the Union of Chambers of Certified Public Accountants of Türkiye[11], the Revenue Administration[12] and the Chambers of Industry and Commerce[13], to ensure participation from all stakeholders in the fight against counterfeit documents.

2. Situations of Being Deemed a Risky Taxpayer

By assigning risk scores to taxpayers, the OSAS does not designate either the buyer or the seller as high-risk. In other words, neither the buyer nor the seller is coded as a user or issuer of forged documents, nor is a risk score assigned to either of them. The OSAS merely attempts to measure the risk of the transaction. The information requests sent by the OSAS do not indicate that taxpayers are at risk; rather, they warn about the risk of the transaction. These information requests serve as an additional review. The responses to these requests are used as input for the risk analysis system. Taxpayers are asked about actions that are difficult or impossible to detect through electronic and digital data. Topics for which taxpayers are asked for information include whether documents related to risky transactions should be recorded in the books, included in the declarations or subsequently removed from the records by filing a correction. This information is referred to as 'reconciliation risk', which the system cannot grasp. Information requests under the OSAS do not constitute an audit, but are rather a precursor to a potential audit.

Now, let's look at the situations in which taxpayers are generally considered to be risky.

(i) First and foremost, a company is considered a risky taxpayer if it is found to have used forged or misleading documents. Return invoices, nylon invoices and unchanged payment documents are in the highest risk category. An invoice is an official document that accurately represents a genuine transaction or situation. If this is not the case, the invoice will not be issued, and the invoice used will be considered a 'fake invoice' (nylon invoice). Reports of fake invoices are generated when a legal relationship is presented. For the crime of issuing or using a fake invoice to be proven, the invoice must contain all the elements required for a legitimate invoice. In cases involving counterfeit invoices or corruption, the Tax Administration investigates the actual failure due to the nature of the crime and the associated penalty. Specifically, the identity of the individual who used or issued the fake invoice must be established in order to uncover the truth. This involves identifying the handwriting and signatures on various documents, including forged invoices, information forms and delivery and receipt documents. Individuals or taxpayers using forged invoices must also be questioned about their commercial relationships and whether they are aware of the perpetrator. Interviews will be conducted with employees from the company's accounting and other departments. Ultimately, a panel of experts will need to reach a decision on the outcome of the trial.

(ii) Making inconsistent declarations can also lead to you being considered a risky taxpayer. This could be due to requesting a Value Added Tax (VAT) refund but not delivering the goods, or discrepancies between the income statement and the VAT return.

(iii) Inconsistencies in goods and inventory status can also lead to this outcome. This could include goods that are claimed to have been delivered but have not yet been shipped, goods that are out of stock, multiple sales of the same goods or discrepancies between recorded and actual inventory. Other examples include goods claimed to have been delivered to the warehouse but which are missing, the same goods being shown as sold repeatedly and stock movements not matching the accounting records. Since all these examples point to inventory discrepancies, taxpayers are considered high risk.

(iv) Incompatibility with the electronic system is also considered a risk factor for taxpayers. Incorrect or incomplete records in the e-invoice, e-ledger and e-archive systems, as well as different views in the electronic systems, are among the reasons for incompatibility.

(v) Relevant criteria include whether the taxpayer has used fraudulent documents in the past, if they have been subject to any related sanctions or investigations, and if they have repeatedly filed corrective returns. Taxpayers in this situation will have a higher risk score in the OSAS.

(vi) Sectoral comparisons are another relevant criterion. This involves analysing and comparing other companies operating in the same sector as the taxpayer. If the comparison reveals abnormal profit or loss discrepancies, significantly higher expenses than the sector average, or discrepancies between transaction volume and declared income, the taxpayer is also considered a risk factor.

(vii) Cash flows that do not align with bank records and large cash transactions are another relevant criterion.

The Tax Inspection Board's strategy and policy for tackling counterfeit documents covers more than just the topics listed above. In this context, the Tax Administration is developing various measures to address users' financial reputation in the eyes of financial institutions, as well as the intermediary role of professionals. Once these policy components reach a certain level of maturity, they will be implemented promptly in the field. It is expected that criminal organisations will adapt to each new component as it is introduced. As technology advances, the nature of crime changes and the variety of tools used in these crimes or to conceal them increases. To be effective, the administration must be several steps ahead of the criminals. Technical, practical and psychological superiority must be achieved and maintained. Regardless of the implemented strategy, if innovation is abandoned in favour of methodologies involving repetitive business processes, the strategy will become obsolete and ineffective over time. Therefore, the new-era strategy encourages tax inspectors to find and develop new components.

Consequently, in light of the statements made by the Tax Inspection Board, it is crucial to establish criteria for identifying high-risk taxpayers in order to anticipate potential issues during tax audits or inspections. The key criteria for identifying high-risk taxpayers are as follows:

Forged document: This refers to a document that has been issued or used in place of a genuine document.
Inconsistent declaration: This refers to discrepancies between VAT returns, income statements and declarations.
Goods stock discrepancy: This refers to goods being unavailable in the warehouse or the same goods being resold.
Electronic incompatibility: This refers to e-Ledger records that are inaccurate or incomplete compared to e-Invoices or e-Archives.
Past record: This refers to whether there have been any corrective declarations or audits in previous periods and if there are any penalties or reports relating to the taxpayer.
Sectoral anomalies: This refers to profit/loss or expense declarations that do not conform to industry averages.

3. Fundamentals of OSAS

3.1 General introduction of the system

OSAS stands for Organisation-Supervised Analysis System. It is an artificial intelligence application that analyses the financial transactions and commercial activities of businesses in real time. Unlike traditional audits, which take place years[14] after the fact, checks are conducted as transactions occur.

The OSAS system electronically tracks all of a business's financial transactions step by step. It analyses issued invoices, bank transfers, inventory records and declarations in real time. If the system detects an unusual or risky transaction, it invites the taxpayer to provide an explanation.

The primary purpose of the OSAS is to prevent tax losses to the state and the use of fraudulent documents.

To combat fraudulent documents effectively, it is crucial to identify these transactions quickly. In response to the need for a risk analysis system that can support current audits and actual detection, the OSAS project began development at the Tax Inspection Board in early 2024. The OSAS is a tax risk analysis system focusing on taxpayers in the establishment phase. Operating on a zero-day logic, it performs calculations with current data and measures transaction risk.

The OSAS is integrated with the Ministry of Treasury and Finance's big data platform and analyses billions of data points from various sources.

The analysis component of the system comprises numerous criteria that measure transaction risk. Transactions that are inaccurate or unrealistic in terms of quantity or nature pose the most fundamental risk. Furthermore, taxpayers who purchase or sell goods or services with related taxpayers in a way that violates their benchmarks also pose a risk. Currently, the system conducts analyses focused on transaction validity.

The OSAS aims to detect fraudulent documents as quickly as possible. This valuable application facilitates current audits and alerts taxpayers to risk exposure through information request letters. The system enables taxpayers to assess their own risk exposure.

It can also work with historical data and draw inferences from it using machine learning techniques. In short, retroactive scoring is possible.

The OSAS uses the outputs of the Tax Intelligence System, which was established in 2024, as input for risk analysis. Initially, the Tax Intelligence System involved the systematic transfer of all the Tax Inspection Board's fraud-related audit experience into an electronic environment. However, it can also be used for other tax and intelligence purposes.

In summary, this system:

Forged or misleading documents are detected immediately.
Inconsistencies between declarations and financial statements are revealed.
Discrepancies between inventory records and the actual status are identified.
Inconsistencies in electronic documents (e-invoices, e-ledgers and e-archives) are identified.
Sectoral comparisons are made and unusual profit/loss ratios are highlighted.

Incidentally, institutional analysis is a method of systematically examining businesses' financial statements, declarations and transactions. In the tax audit process, it is a critical tool for uncovering inaccurate records and forged documents. The OSAS automates institutional analysis, ensuring that taxpayers' transactions are constantly monitored.

3.2 Tax audit process

The OSAS tax audit process consists of the following steps:

The system scans transactions and calculates a risk score.
An e-notification is sent to the taxpayer for high-risk transactions.
The taxpayer is asked to provide documents and explanations.
If the explanation is deemed sufficient, the case is closed. If not, it is referred for review.

3.3 Who does this system cover?

The system covers all taxpayers. However, the following taxpayers are specifically included in the OSAS:

Companies requesting VAT refunds.
Businesses with high transaction volumes.
Taxpayers with a history of using false documents or amending tax returns.

3.4 The consequences of being a high-risk taxpayer

Being considered a high-risk taxpayer under the OSAS regime can have serious consequences. Businesses face penalties of up to three times their tax liability for tax loss and risk imprisonment ranging from three to eight years. Furthermore, financial sanctions such as collateral obligations and precautionary liens may be imposed. This can result in a loss of trust among business partners, financial institutions and customers, damaging the company's reputation.

To avoid becoming a high-risk taxpayer, businesses should take the following precautions:

Regularly check e-Invoice, e-Ledger and e-Archive records.
Keep actual and recorded inventories consistent.
Compare declarations with the income statement and balance sheet.
Conduct transactions through banks and financial institutions.
Regularly archive documents.
Maintain constant communication with a financial advisor.

4. Collaboration with other stakeholders

In the context of the OSAS, the workforce and organisation must be restructured in line with the new term strategy. Departments within the Tax Inspection Board will be redesigned to incorporate automated reporting, risk analysis systems, and auditing processes. Current risk analysis systems allow audits to be conducted based on risk analysis. If a transaction is flagged as high-risk due to the use of forged documents, for example, an agile audit team can swiftly identify the underlying dynamics and have a significant impact on taxpayer perception. Organisational and coordination efforts will be undertaken to achieve this. Furthermore, as previously emphasised, all proposed measures, policies and strategies essentially aim to reshape the culture surrounding forged documents and how they are perceived. The most important tool for combatting counterfeit documents is perception management. All stakeholders will be informed of the revised oversight practices of the Tax Inspection Board regarding counterfeit documents, and all relevant channels and media will be utilised for this purpose. Within this framework, joint efforts will be made with stakeholders such as the judiciary, law enforcement, the Union of Chambers of Certified Public Accountants of Türkiye, the Revenue Administration, and the Chambers of Industry and Commerce, to ensure that all stakeholders participate in and support the process.

5. What should taxpayers do when they receive an OSAS letter?

The Ministry of Treasury and Finance of the Republic of Türkiye has started sending letters based on the OSAS system to taxpayers requesting information. These letters do not mention voluntary compliance; they simply serve as a warning before official procedures commence.

Taxpayers have three main legal options in response to these letters:

Declaration with regret: This warning letter, which is sent before official proceedings begin, enables you to make a declaration of regret. Filing a declaration of regret has the advantage of avoiding a tax loss penalty and, if convicted of tax evasion, even avoiding imprisonment. However, this creates a significant financial liability, including tax and a surcharge/interest for the relevant period, which must be paid within 15 days.
Declaration with reservation: This means that the taxpayer reserves the right to file a lawsuit regarding the declaration even after submitting it. This eliminates the risk of investigation and the obligation to pay taxes outright, as with a declaration of regret.
Explanation or no response: The taxpayer can submit a letter stating, 'We have no problem.' However, this does not completely eliminate the risk of investigation. If the request for information is not responded to within the timeframe, or if the information provided is incomplete or misleading, the special irregularity penalty stipulated in Article 355 of the Tax Procedure Law may be imposed.

A sample taxpayer information letter is provided below.

“(…) As a result of the analysis carried out for the year “XXXX”, your purchases listed below have been found to be risky in terms of whether they are based on an actual delivery of goods and/or performance of services.

Seller Tax Identification Number	Seller Title	Invoice Quantity	Total Invoice Amount (Excluding VAT)

This letter is not intended to initiate an investigation; rather, it aims to inform taxpayers of risky situations as early as possible and form the basis for the final assessment made by our risk analysis unit. To accurately assess transaction risk in this context, it is necessary to obtain information regarding the following purchases of goods and/or services. The requested information is as follows:

We need to know if the documents listed in the above table have been entered into your legal ledgers, and if so, we need the goods/service description. If the documents have been entered, please fill in the journal number section of the table below. If not, please write 'not entered' in the journal number section.
If the amounts listed in the documents have been deducted from your tax returns, or if they were deducted and subsequently removed from the deduction items by submitting a correction declaration, please enter the appropriate phrase: 'No deduction', 'Deduction made' or 'Correction declaration filed' in the 'Declaration status' section of the table below.

Invoice Issuer Tax Identification Number	Invoice Issuer	Title Invoice Date	Serial Number	Invoice Amount	VAT Amount	Goods/Service Description	Journal Date and Number	Declaration Status

Conclusion

Tax auditing in Türkiye has undergone a radical transformation in recent years. Due to the impact of digitalisation on tax administration, taxpayers are now reviewed through not only previous years' books, but also via the analysis of transactions in real time. At the heart of this transformation lies the Organisation-Supervised Analysis System (OSAS).

Developed by the Tax Inspection Board under the Ministry of Treasury and Finance, the OSAS came into effect on 1 October 2025. The OSAS instantly identifies commercial and invoice transactions, unregistered expenses, and the risk of forged documents. Taxpayers are then sent an OSAS letter.

This letter serves as a warning to taxpayers, and ignoring it can result in substantial tax penalties and even imprisonment. Therefore, businesses must keep personal expenses separate from their accounting records and maintain thorough documentation of every step.

At the heart of the OSAS is an artificial intelligence-powered learning algorithm. This analyses each business's historical transaction data and compares it with that of other businesses in the same sector. This creates a dynamic 'risk profile' for each taxpayer. The system is far more advanced than traditional auditing methods because it considers both numerical differences and behavioural patterns. For example, if a business's sales and purchases over the past six months are 30 per cent above the industry average, or if sales volume suddenly drops while bank transactions remain stable, the algorithm interprets this as a risk signal.

The OSAS is a system that assigns risk scores to transactions, rather than taxpayers, by conducting instant data analysis and issuing advance warning letters in the event of potential audits. 1 October 2025 is considered a milestone in the fight against forged documents: after this date, the 'unknowingly used' defence will be largely ineffective, and taxpayers will be responsible for proving their innocence. Although OSAS letters are non-enforceable, they enable taxpayers to assess their own risk and avoid criminal penalties, including a triple tax loss and the risk of imprisonment, by opting for legal remedies such as Declaration with Leniency or Declaration with Reservation. In this new era of auditing, the key to success is for companies to strengthen their internal audit mechanisms and seek expert financial consultancy services.

Furthermore, if the system detects any inconsistencies between the bank and accounting records or significant differences in VAT rates between purchase and sales invoices, the transactions are automatically classified as 'high risk'. The taxpayer may then be placed on the OSAS's priority audit list. One of the most important aspects of the system, however, is that it gives taxpayers the right to self-disclose before penalties are imposed. This process is integrated with the 'invitation to explanation' mechanism set out in the Tax Procedure Law. In other words, if a business recognises inconsistencies identified by the system and submits their own declaration, they can correct them without facing heavy penalties.

This development marks a new era in tax auditing for companies and financial advisors alike. The OSAS enables the tax administration to instantly identify risky transactions, thereby protecting the state's tax revenues and promoting tax fairness.

Consequently, the OSAS has transformed the way tax audits are conducted in Türkiye. The focus is now on preventing errors before they occur rather than detecting them later.

Box 2. Use of Technology and Artificial Intelligence in Tax Audits: Examples from Various Countries
Artificial intelligence (AI) has the potential to reshape industries, economies, governments and societies. Yet, its progress in the government has been limited. AI can help governments in three key opportunity areas: productivity, responsiveness and accountability. At each stage of the policy cycle, AI can bring highly complementary benefits: · automating mundane and repetitive tasks · improving productivity in analytical or creative tasks · tailoring services to address personalised citizen needs · tailoring approaches to strengthen the civil service · enhancing decision-making and sense-making of the present · better forecasting of the future · improving information management and accessibility · detecting improper transactions and assessing integrity risks · enabling non-governmental actors to understand and engage with government and promote accountability · unlocking opportunities for external stakeholders through AI as a good for all. These benefits are not mutually exclusive and can be categorised into four broad areas: automated, streamlined and tailored processes and services better decision-making, sense-making and forecasting enhanced accountability and anomaly detection unlocking opportunities for external stakeholders. Governments should manage the risks of AI that are specific to government use, which are: ethical risks, operational risks, exclusion risks, public resistance risks and risks of inaction. Digital government is essential to transforming processes and services in ways that improve the public sector's responsiveness and reliability and bring governments closer to their people. The COVID-19 pandemic underscored the importance of digital technologies and data in building economic and social resilience through strategic, agile and innovative government approaches. Today, governments worldwide are facing decreasing levels of public trust, while simultaneously experiencing growing and rapidly accelerating changes brought about by the digital age. In this time of fast-paced disruption — rapid technological evolution, changing societal needs, unexpected crises — it is crucial governments be capable and equipped to use digital technologies and data to increase productivity and resilience in their public administrations, and enhance the quality of public services. By the way, for many years, tax administrations have been using AI to support activities across their operating model and have been actively exploring its potential to further enhance their operations, improve taxpayer services, increase tax compliance and prevent tax fraud. Since 2022, the OECD Inventory of Tax Technology Initiatives survey has provided insight into how AI is being applied across tax administrations around the world. The latest survey shows that the main areas of application in OECD countries are detection of tax evasion and fraud, decision-making assistance and improving tax services. As one example, Greece's Independent Authority for Public Revenue is leveraging AI to combat tax evasion by detecting compliance issues, automating complex procedures and enabling auditors to respond in real time. On the other hand, Brazil had around USD 140 billion in assessed taxes waiting for decisions in administrative court tax appeals. It takes about six years for the appeal ruling. Under the AI Litigation Project, Brazil employed supervised machine learning (ML) when distributing groups of similar files to the same officers, in order to increase their speed in administering the file and taking decisions. The first trials, conducted with a sample of 2 000 manually labelled files, showed that supervised algorithms can attain sensitivity and specificity of over 80%. Brazil also employed clustering algorithms to complete files either in full or in part. Additionally, a web-based report assistant tool is being developed to support officers' analysis and help in their goal of reusing blocks of text. A significant driver of tax administrations' embracing AI is its ability to analyse data to score and prioritise risks. With AI, tax administrations can process large volumes of taxpayer data — from historical filings to transaction records and digital payment information — to build sophisticated risk models and then assign risk scores to specific types of behaviour or transactions. These models help tax administrations quickly identify possible non-compliance and focus resources on them. Take Austria as an example. Since 2014, the Austrian tax administration has applied machine‐learning algorithms through the Predictive Analytics Competence Centre (PACC), a specialised unit within the Federal Ministry of Finance. Tasked with modernising risk management, the PACC works to improve tax collection, auditing and fraud detection. Organised into four subject areas (Predictive Analytics, Advanced Analytics, Tax Analytics, and Customs Analytics), the PACC addresses a broad range of challenges across the tax system. In 2023, the PACC's risk models analysed around 6.5 million cases across income, corporate and value added tax sectors as well as customs transactions. The analyses detected instances of false reporting in employee tax assessments and identified fraudulent activities, resulting in additional tax revenues of approximately EUR 185 million. Nearly 27.5 million cases were also examined for compliance, with 375 000 cases flagged for further review due to implausible risk profiles. Advanced techniques, including decision trees, regression models and text mining, support both retrospective and real‐time audits while ongoing projects seek to expand analytical capabilities to include, for instance, generative models. The Inland Revenue Authority of Singapore (IRAS) has also employed end-user automation (i.e. tools that simplify things for service users), data and AI tools to deliver seamless and personalised taxpayer services. In 2021, IRAS launched a chatbot to more efficiently handle common queries on individual income tax, corporate tax, goods and services tax, property tax, stamp duty, withholding tax and employer tax matters. The chatbot is powered by the Singapore Government Virtual Intelligent Chat Assistant platform. There are other examples besides these. The Australian Taxation Office (ATO) is employing advanced technologies to enhance the accuracy and efficiency of tax return submissions. These include real-time analytics, pre-filled forms and anomaly detection systems to assist taxpayers in meeting their obligations and reducing errors. As part of its efforts, the ATO provides a pre-filling service that automatically populates individual tax returns with data sourced from employers, banks, government agencies and other third parties. This includes information such as salary, bank interest, dividends and private health insurance details. Taxpayers are required to review and confirm the pre-filled data before submission. This system not only reduces administrative burdens but also improves accuracy by minimising manual entry errors. Additionally, the ATO uses real-time prompts during the submission process to address potential anomalies. For instance, if a taxpayer's reported figures are flagged by a model as abnormal, , the system generates a message encouraging them to double-check their inputs. A message might state, for example: “You have not reported any interest income. Please review and ensure any interest earned has been reported.” In 2023-24, more than 636 000 prompts were issued to individuals, helping to protect approximately AUD 78.9 million in revenue. The Spanish Tax Agency was among the first tax agencies to start using AI-driven virtual assistants (chatbots) which can answer frequently asked questions about tax filing deadlines, general understanding of taxes, VAT and e-invoicing, etc. The Spanish Tax Agency also uses AI to facilitate the delivery of warning messages, including those regarding possible errors when modifying information on employment income and those to entrepreneurs filing during the voluntary filing period. Moreover, the tax administration uses AI for risk detection and predicting the likelihood of taxpayer non-compliance. The Dutch Tax and Customs Administration uses machine learning algorithms to perform web scraping, which is used to gather and match data with pre-existing tax authority data. Another use of web scraping is to identify interconnected websites and determine their ultimate owners. This way, unknown taxpayers are detected, and potential non-compliance is identified. The Dutch tax authority also uses AI to allocate a risk score to taxpayers and then sort them into different categories based on the possibility of non-compliance. This information is then used to decide the audit and treatment strategies for the different groups. In addition to the aforementioned usages, the Belgium Tax Agency also uses AI to monitor, flag and block suspicious VAT transactions. The French Tax Administration has also integrated AI into its property tax system, using aerial imagery and machine learning to detect undeclared swimming pools, property extensions and other taxable assets. Poland has had AI and a machine-learning model in place since 2017, used in fighting VAT fraud by analysing large taxpayer datasets to identify suspicious transactions. The STIR model helps the Polish tax authority assess taxpayer risk levels and liabilities and send automatic and tailored reminders inviting taxpayers to pay their taxes on time. STIR passes the data to the tax authority, which can block a business's bank accounts if there is suspicion of VAT fraud. These measures to secure tax compliance have helped Poland reduce its VAT gap of EUR 6.6 billion in 2017 to EUR 1.7 billion in 2021. Italy is a leader in using AI to detect tax breaches. Its VeRa algorithm helps the Italian tax authority compare financial data, including tax returns and bank accounts, identify taxpayer risk levels and make high-risk taxpayers explain any discrepancies detected. Using AI algorithms to compare financial data helped Italy identify more than a million high-risk cases and prevent fraud worth EUR 6.8 million in 2022. According to the European Commission's data for 2021, Romania has one of the largest VAT gaps. To reduce it, the Romanian tax authority uses machine-learning algorithms in processing large data volumes and assessing risks, while AI systems are used for data consolidation to build a taxpayer's financial profile, with robotic solutions for systems automation improving the accuracy of tax audits. According to the available information, the use of AI pushed Romanian VAT revenues up by about 1% in 2023. His Majesty's Revenue and Customs's (HMRC) Connect system was first introduced in 2010. The system gathers data from a vast array of sources and looks for any anomalous entries, analysing billions of data points each day. For example, Connect might detect that an individual has a source of PAYE income that was not reported on their self assessment tax return and prompt an enquiry, but it can also analyse behavioural trends such as spending levels versus level of overall income. HMRC uses this data-led risk assessing to help identify areas of tax losses, and where risk areas are identified this might generate an HMRC enquiry or a taxpayer might receive a nudge letter. Lastly, the Internal Revenue Service (IRS) is now deploying AI models across multiple taxpayer segments, shifting from broad statistical scoring to more sophisticated, relationship-driven analysis in the USA. Each model is designed to identify high-risk returns with greater precision, reduce wasted audits, and direct resources where they matter most. For individual taxpayers, the IRS has introduced a machine-learning (ML) classification model that automatically analyzes each return and recommends the top three issues most likely to require adjustment. The IRS has proactively been integrating these ML models prior to 2020, even before the presidential EO: “Promoting the Use of Trustworthy AI in the Federal Government”. For corporations with assets between $10 million and $250 million, the IRS has replaced the outdated Discriminant Analysis System (DAS) with the new Line Anomaly Recommender (LAR). Unlike its predecessor, LAR looks at the relationships among line items—such as income, deductions, and credits—rather than flagging isolated anomalies. Early testing shows lower “no-change” audit rates and better coverage, as the model evaluates the entire return population instead of relying on limited samples. Large, complex partnerships such as hedge funds, private equity, and real estate investment groups have historically been difficult for the IRS to audit effectively. To address this, the IRS developed the Large Partnership Compliance (LPC) model, which applies machine learning to the entire population of large partnership returns and then incorporates expert review by tax specialists. This is the IRS's first effective enforcement tool for this taxpayer segment. In Tax Year 2021, the LPC flagged 82 high-risk partnership returns for examination, compared to near-zero audits in prior years. Sources used: AI in tax, is it a double-edged sword? Menzies, 26th August 2025, < https://www.menzies.co.uk/ai-in-tax-is-it-double-edged-sword/ > Accessed on November 25, 2025. Elizabete Lizete Lapsina, Matiss Auzins, Role of AI in transforming how tax authorities work, PwC, Undated, < https://www.pwc.com/lv/en/about/services/IT-services/related-articles/Role-of-AI-in-transforming-how-tax-authorities-work.html > Accessed on November 25, 2025. Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions, OECD's report, 18 September 2025, < https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/governing-with-artificial-intelligence_398fa287/795de142-en.pdf > Accessed on November 25, 2025. Katerina Ilieva, Artificial Intelligence Usage in EU Tax Administrations, IBFD European Knowledge Group, 10 January 2025, < https://www.ibfd.org/sites/default/files/2025-01/artificial-intelligence-usage-in-eu-tax-administrationsv7.pdf > Accessed on November 25, 2025. Rosie Cheng, IRS Uses AI to Target High-Risk Tax Returns: Are You Prepared? Ryan & Wetmore PC, 08 September, 2025, < https://www.ryanandwetmore.com/insights/irs-using-ai-for-tax-audits-in-2025-what-businesses-must-know > Accessed on November 25, 2025.

Box 2. Use of Technology and Artificial Intelligence in Tax Audits: Examples from Various Countries

Artificial intelligence (AI) has the potential to reshape industries, economies, governments and societies. Yet, its progress in the government has been limited. AI can help governments in three key opportunity areas: productivity, responsiveness and accountability. At each stage of the policy cycle, AI can bring highly complementary benefits:

· automating mundane and repetitive tasks

· improving productivity in analytical or creative tasks

· tailoring services to address personalised citizen needs

· tailoring approaches to strengthen the civil service

· enhancing decision-making and sense-making of the present

· better forecasting of the future

· improving information management and accessibility

· detecting improper transactions and assessing integrity risks

· enabling non-governmental actors to understand and engage with government and promote accountability

· unlocking opportunities for external stakeholders through AI as a good for all.

These benefits are not mutually exclusive and can be categorised into four broad areas:

automated, streamlined and tailored processes and services

better decision-making, sense-making and forecasting

enhanced accountability and anomaly detection

unlocking opportunities for external stakeholders.

Governments should manage the risks of AI that are specific to government use, which are: ethical risks, operational risks, exclusion risks, public resistance risks and risks of inaction.

Digital government is essential to transforming processes and services in ways that improve the public sector's responsiveness and reliability and bring governments closer to their people. The COVID-19 pandemic underscored the importance of digital technologies and data in building economic and social resilience through strategic, agile and innovative government approaches. Today, governments worldwide are facing decreasing levels of public trust, while simultaneously experiencing growing and rapidly accelerating changes brought about by the digital age. In this time of fast-paced disruption — rapid technological evolution, changing societal needs, unexpected crises — it is crucial governments be capable and equipped to use digital technologies and data to increase productivity and resilience in their public administrations, and enhance the quality of public services.

By the way, for many years, tax administrations have been using AI to support activities across their operating model and have been actively exploring its potential to further enhance their operations, improve taxpayer services, increase tax compliance and prevent tax fraud. Since 2022, the OECD Inventory of Tax Technology Initiatives survey has provided insight into how AI is being applied across tax administrations around the world. The latest survey shows that the main areas of application in OECD countries are detection of tax evasion and fraud, decision-making assistance and improving tax services. As one example, Greece's Independent Authority for Public Revenue is leveraging AI to combat tax evasion by detecting compliance issues, automating complex procedures and enabling auditors to respond in real time.

On the other hand, Brazil had around USD 140 billion in assessed taxes waiting for decisions in administrative court tax appeals. It takes about six years for the appeal ruling. Under the AI Litigation Project, Brazil employed supervised machine learning (ML) when distributing groups of similar files to the same officers, in order to increase their speed in administering the file and taking decisions. The first trials, conducted with a sample of 2 000 manually labelled files, showed that supervised algorithms can attain sensitivity and specificity of over 80%. Brazil also employed clustering algorithms to complete files either in full or in part. Additionally, a web-based report assistant tool is being developed to support officers' analysis and help in their goal of reusing blocks of text.

A significant driver of tax administrations' embracing AI is its ability to analyse data to score and prioritise risks. With AI, tax administrations can process large volumes of taxpayer data — from historical filings to transaction records and digital payment information — to build sophisticated risk models and then assign risk scores to specific types of behaviour or transactions. These models help tax administrations quickly identify possible non-compliance and focus resources on them. Take Austria as an example. Since 2014, the Austrian tax administration has applied machine‐learning algorithms through the Predictive Analytics Competence Centre (PACC), a specialised unit within the Federal Ministry of Finance. Tasked with modernising risk management, the PACC works to improve tax collection, auditing and fraud detection. Organised into four subject areas (Predictive Analytics, Advanced Analytics, Tax Analytics, and Customs Analytics), the PACC addresses a broad range of challenges across the tax system. In 2023, the PACC's risk models analysed around 6.5 million cases across income, corporate and value added tax sectors as well as customs transactions. The analyses detected instances of false reporting in employee tax assessments and identified fraudulent activities, resulting in additional tax revenues of approximately EUR 185 million. Nearly 27.5 million cases were also examined for compliance, with 375 000 cases flagged for further review due to implausible risk profiles. Advanced techniques, including decision trees, regression models and text mining, support both retrospective and real‐time audits while ongoing projects seek to expand analytical capabilities to include, for instance, generative models.

The Inland Revenue Authority of Singapore (IRAS) has also employed end-user automation (i.e. tools that simplify things for service users), data and AI tools to deliver seamless and personalised taxpayer services. In 2021, IRAS launched a chatbot to more efficiently handle common queries on individual income tax, corporate tax, goods and services tax, property tax, stamp duty, withholding tax and employer tax matters. The chatbot is powered by the Singapore Government Virtual Intelligent Chat Assistant platform.

There are other examples besides these. The Australian Taxation Office (ATO) is employing advanced technologies to enhance the accuracy and efficiency of tax return submissions. These include real-time analytics, pre-filled forms and anomaly detection systems to assist taxpayers in meeting their obligations and reducing errors. As part of its efforts, the ATO provides a pre-filling service that automatically populates individual tax returns with data sourced from employers, banks, government agencies and other third parties. This includes information such as salary, bank interest, dividends and private health insurance details. Taxpayers are required to review and confirm the pre-filled data before submission. This system not only reduces administrative burdens but also improves accuracy by minimising manual entry errors.

Additionally, the ATO uses real-time prompts during the submission process to address potential anomalies. For instance, if a taxpayer's reported figures are flagged by a model as abnormal, , the system generates a message encouraging them to double-check their inputs. A message might state, for example: “You have not reported any interest income. Please review and ensure any interest earned has been reported.” In 2023-24, more than 636 000 prompts were issued to individuals, helping to protect approximately AUD 78.9 million in revenue.

The Spanish Tax Agency was among the first tax agencies to start using AI-driven virtual assistants (chatbots) which can answer frequently asked questions about tax filing deadlines, general understanding of taxes, VAT and e-invoicing, etc. The Spanish Tax Agency also uses AI to facilitate the delivery of warning messages, including those regarding possible errors when modifying information on employment income and those to entrepreneurs filing during the voluntary filing period. Moreover, the tax administration uses AI for risk detection and predicting the likelihood of taxpayer non-compliance.

The Dutch Tax and Customs Administration uses machine learning algorithms to perform web scraping, which is used to gather and match data with pre-existing tax authority data. Another use of web scraping is to identify interconnected websites and determine their ultimate owners. This way, unknown taxpayers are detected, and potential non-compliance is identified. The Dutch tax authority also uses AI to allocate a risk score to taxpayers and then sort them into different categories based on the possibility of non-compliance. This information is then used to decide the audit and treatment strategies for the different groups.

In addition to the aforementioned usages, the Belgium Tax Agency also uses AI to monitor, flag and block suspicious VAT transactions. The French Tax Administration has also integrated AI into its property tax system, using aerial imagery and machine learning to detect undeclared swimming pools, property extensions and other taxable assets.

Poland has had AI and a machine-learning model in place since 2017, used in fighting VAT fraud by analysing large taxpayer datasets to identify suspicious transactions. The STIR model helps the Polish tax authority assess taxpayer risk levels and liabilities and send automatic and tailored reminders inviting taxpayers to pay their taxes on time. STIR passes the data to the tax authority, which can block a business's bank accounts if there is suspicion of VAT fraud. These measures to secure tax compliance have helped Poland reduce its VAT gap of EUR 6.6 billion in 2017 to EUR 1.7 billion in 2021.

Italy is a leader in using AI to detect tax breaches. Its VeRa algorithm helps the Italian tax authority compare financial data, including tax returns and bank accounts, identify taxpayer risk levels and make high-risk taxpayers explain any discrepancies detected. Using AI algorithms to compare financial data helped Italy identify more than a million high-risk cases and prevent fraud worth EUR 6.8 million in 2022.

According to the European Commission's data for 2021, Romania has one of the largest VAT gaps. To reduce it, the Romanian tax authority uses machine-learning algorithms in processing large data volumes and assessing risks, while AI systems are used for data consolidation to build a taxpayer's financial profile, with robotic solutions for systems automation improving the accuracy of tax audits. According to the available information, the use of AI pushed Romanian VAT revenues up by about 1% in 2023.

His Majesty's Revenue and Customs's (HMRC) Connect system was first introduced in 2010. The system gathers data from a vast array of sources and looks for any anomalous entries, analysing billions of data points each day. For example, Connect might detect that an individual has a source of PAYE income that was not reported on their self assessment tax return and prompt an enquiry, but it can also analyse behavioural trends such as spending levels versus level of overall income. HMRC uses this data-led risk assessing to help identify areas of tax losses, and where risk areas are identified this might generate an HMRC enquiry or a taxpayer might receive a nudge letter.

Lastly, the Internal Revenue Service (IRS) is now deploying AI models across multiple taxpayer segments, shifting from broad statistical scoring to more sophisticated, relationship-driven analysis in the USA. Each model is designed to identify high-risk returns with greater precision, reduce wasted audits, and direct resources where they matter most. For individual taxpayers, the IRS has introduced a machine-learning (ML) classification model that automatically analyzes each return and recommends the top three issues most likely to require adjustment. The IRS has proactively been integrating these ML models prior to 2020, even before the presidential EO: “Promoting the Use of Trustworthy AI in the Federal Government”. For corporations with assets between $10 million and $250 million, the IRS has replaced the outdated Discriminant Analysis System (DAS) with the new Line Anomaly Recommender (LAR). Unlike its predecessor, LAR looks at the relationships among line items—such as income, deductions, and credits—rather than flagging isolated anomalies. Early testing shows lower “no-change” audit rates and better coverage, as the model evaluates the entire return population instead of relying on limited samples. Large, complex partnerships such as hedge funds, private equity, and real estate investment groups have historically been difficult for the IRS to audit effectively. To address this, the IRS developed the Large Partnership Compliance (LPC) model, which applies machine learning to the entire population of large partnership returns and then incorporates expert review by tax specialists. This is the IRS's first effective enforcement tool for this taxpayer segment. In Tax Year 2021, the LPC flagged 82 high-risk partnership returns for examination, compared to near-zero audits in prior years.

Sources used:

AI in tax, is it a double-edged sword? Menzies, 26th August 2025, < https://www.menzies.co.uk/ai-in-tax-is-it-double-edged-sword/ > Accessed on November 25, 2025.

Elizabete Lizete Lapsina, Matiss Auzins, Role of AI in transforming how tax authorities work, PwC, Undated, < https://www.pwc.com/lv/en/about/services/IT-services/related-articles/Role-of-AI-in-transforming-how-tax-authorities-work.html > Accessed on November 25, 2025.

Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions, OECD's report, 18 September 2025, < https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/governing-with-artificial-intelligence_398fa287/795de142-en.pdf > Accessed on November 25, 2025.

Katerina Ilieva, Artificial Intelligence Usage in EU Tax Administrations, IBFD European Knowledge Group, 10 January 2025, < https://www.ibfd.org/sites/default/files/2025-01/artificial-intelligence-usage-in-eu-tax-administrationsv7.pdf > Accessed on November 25, 2025.

Rosie Cheng, IRS Uses AI to Target High-Risk Tax Returns: Are You Prepared? Ryan & Wetmore PC, 08 September, 2025, < https://www.ryanandwetmore.com/insights/irs-using-ai-for-tax-audits-in-2025-what-businesses-must-know > Accessed on November 25, 2025.

Financial support: Yavuz Akbulak, the author of this study, has not received any financial support for the research, authorship or publication of this study.

Author contributions: This article was prepared solely by the author.

Declaration of Conflict of Interest/Common Interest: The author declares that there is no conflict of interest in relation to the content of the article.

Use of AI: The author declares that no artificial intelligence tools were used in creating this article.

(The opinions expressed in this article are solely those of the author and do not represent the views of the institution with which he is affiliated. They should not be used to imply any connection between the author and the institution. Any errors, flaws, deficiencies, or shortcomings in the article are the responsibility of the author.)

[1] See: <https://www.oecd.org/content/dam/oecd/en/publications/reports/2017/11/fighting-tax-crime_c9374f32/63530cd2-en.pdf> Accessed on November 25, 2025.
[2] In the context of this topic, please refer to section #.
- (in Turkish): Fatih Aras, Attorney at Law, KURGAN Nedir? Kuruluş Gözetimli Analiz Sistemi (in English: What is the OSAS? Organization-Supervised Analysis System), <https://www.fatiharas.com/kurgan-nedir-kurulus-gozetimli-analiz-sistemi/> Accessed on November 25, 2025.
- (in Turkish) KURGAN Sistemi ve Sistem Kapsamında Yazılan Yazılar Hk. (in English: The OSAS and Regarding Articles Written Within the Scope of the System), Nelsus, October 3, 2025, <https://nelsus.com.tr/sirkueler-2025-44-kurgan-sistemi-ve-sistem-kapsaminda-yazilan-yazilar-hk/> Accessed on November 25, 2025.
- (in Turkish) Kuruluş Gözetimli Analiz (KURGAN) Nedir? (in English: What is the OSAS?), Noyax, <https://www.noyax.com/blog/guncel-icerikler/kurgan-nedir> Accessed on November 25, 2025.
- (in Turkish) Yeni Dönemde Kurgan ile Mali Disiplin Kıskacı (in English: The Clamp of Financial Discipline with the OSAS in the New Era), October 3, 2025, <https://www.businessweek.com.tr/haberler/yeni-donemde-kurgan-ile-mali-disiplin-kiskaci-3758478> Accessed on November 25, 2025.
- (in Turkish) Musa Çakır, Kuruluş Gözetimli Analiz Sistemi (KURGAN) Nedir? Nasıl Önlem Almamız Gerekir? (in English: What is the Organization-Supervised Analysis System? How should we take precautions?) MuhasebeTR, October 2, 2025, <https://www.muhasebetr.com/yazarlarimiz/musacakir/005/> Accessed on November 25, 2025.
[3] According to the United Nations Convention Against Transnational Organised Crime, a 'predicate offence' is defined as any offence from which proceeds have been generated that may become the subject of an offence as defined in Article 6 of the Convention.
[4] See (in Turkish): # <https://www.gib.gov.tr/mevzuat/kanun/434> Accessed on November 25, 2025.
[5] See (in Turkish): # <https://ms.hmb.gov.tr/uploads/sites/17/2025/10/1-Ekim-2025-Sahte-Belgeyle-Mucadele-Stratejisi-Kurulus-Gozetimli-Anali._-57a16748029990fa.pdf> Accessed on November 25, 2025.
[6] The following examples illustrate this:
- Whether the activity and supply purchasing accounts are related.
- Whether a place (such as a warehouse) is available for storing the goods.
- How purchase payments are made (e.g. via bank transfer or cheque).
- The proportional decrease in total costs due to fake invoices or Value Added Tax (VAT) reductions.
[7] The purpose of this is to ensure that the forged document is not included in the accounting records and that corrections are made to the tax base by taking into account the amount of the forged document in the previously submitted tax return.
[8] Signals for taxpayers may include information request letters sent to taxpayers by the tax administration, information obtained about the taxpayer, conversations with the taxpayer, the taxpayer's failure to include a forged document in their accounting records and the correction of a previously filed tax return. All of these serve as signals that reveal the taxpayer's risk status.
[9] See: # <https://en-vdk.hmb.gov.tr/>.
[10] Various channels: A means of communication through newspapers, telephones, telegraphs, radios, televisions, the internet, etc.
[11] See: # <https://www.turmob.org.tr/English>.
[12] See: # <https://www.gib.gov.tr/>.
[13] See: # <https://www.tobb.org.tr/Sayfalar/Eng/AnaSayfa.php>.
[14] This statement can be found in Chapter 15 of the 'Questions and Answers' section of the guidelines issued by the Tax Inspection Board. The text states: 'Technological investments have shortened the time taken to detect and inspect forged documents from years to days. They have also brought thousands of potential users, who were unaware of this situation, to the attention of the Tax Inspection Board.' (…)

Articles

The Fight Against Counterfeit Documents in Türkiye: A Brand New Organization-Supervised Analysis System (OSAS)

Comment form