Advanced analytics and AI Revolutionise Tax Risk Management

TaxCore© is at the forefront of digital tax transformation, integrating data from fiscal devices into a unified, real-time system that allows tax authorities to track financial transactions, identify irregularities (using AI), and plan audits effectively. Given the volume of collected data, which includes information on hundreds of thousands of taxpayers and records over 15 million new invoices daily, the system requires the application of advanced processing and analysis methods, such as data mining and modern analytical approaches. Advanced analytical methods enable detailed and rapid analysis, pattern identification, and decision-making based on a large volume of data. The large database provides a comprehensive overview of tax activities but simultaneously poses a challenge for efficient and timely searching and analysis.

For tax authorities to identify irregularities and plan inspections, the new advanced functionality of TaxCore© in the form of an AI module employs sophisticated technologies and algorithms for more detailed processing and data analysis. The application of machine learning (ML) methods to large databases allows in-depth analysis of the chronological behaviour of taxpayers, based on which the system forms risk categories for taxpayers. Patterns of taxpayer behaviour serve as historical indicators of various attributes, according to which, depending on the degree of alignment with recognised patterns, the system assigns taxpayers an appropriate risk level. Some of the behavioural patterns on which TaxCore© profiles taxpayers include the percentage of working days with no issued invoices, deviations in taxpayers’ financial transactions compared to the average trend of financial transactions in their activities, active working hours of the taxpayer during a period, etc.

AI-Powered Risk Assessment and Tax Audits

Since each type of fraud leaves certain “traces” in the data, TaxCore© offers a 360-degree view of taxpayer behaviour, monitoring their actions through defined patterns. Based on the established risk level for each defined pattern, we obtain a cumulative risk score for the taxpayer as an aggregate of their individual risk levels in behavioral patterns. This part of the system represents a significant business value of the TaxCore© platform, especially concerning support for audit work in tax administrations. Such a view of the taxpayer enables detailed analysis and is particularly useful in the risk analysis sector, aiding in the identification of potential tax evasion and determining priorities for tax controls and actions. This level of automation and analytical power directly contributes to the success of tax administrations in recognising tax risks and acting timely, thus optimising processes and increasing efficiency in conducting tax controls.

Artificial Intelligence for Irregularity Detection

One of the key innovations in TaxCore® is its use of AI algorithms to analyse fiscal data and flag potential cases of tax evasion. These AI models automate the detection of unusual financial transactions, reducing the need for  manual analysis and minimising the risk of human error. Tax Inspectors and analysts can now focus their efforts on high-priority cases, leading to more efficient allocation of resources and a better overall management of tax risks.

Traditional methods often required time-consuming manual work, which left room for oversight. In contrast, TaxCore’s AI-driven system offers automation and improved precision, allowing for quicker and more accurate detection of irregularities and significantly greater accuracy in predictions.

Balancing Artificial Intelligence and Human Expertise

While the AI algorithms in TaxCore© provide deep insights into taxpayer behavior, human judgment still plays a critical role. The system flags potential irregularities, but the final decision on what actions to take rests with tax inspectors. This balance ensures that AI enhances the decision-making process without completely removing the human element, allowing tax authorities to maintain a fair and contextual approach when addressing non-compliance.

Isolation Forest and KMeans for Anomaly Detection

TaxCore© employs a powerful combination of KMeans clustering and Isolation Forest algorithms (enhanced by the use of AI) to detect irregularities. KMeans groups taxpayers based on similarities—such as the number of fiscal invoices issued daily—while Isolation Forest identifies anomalies within these clusters. This dual approach allows for efficient detection of outliers, ensuring that tax authorities can quickly identify taxpayers whose operations deviate significantly from the norm.

For instance, if a taxpayer’s invoice volume suddenly spikes or drops compared to their historical average, the system flags them for further review. This method significantly improves accuracy in detecting fraudulent activity while reducing the number of false positives.

Forecasting Tax Obligations with the Prophet Algorithm

Beyond anomaly detection, TaxCore© also utilises the Prophet algorithm (based on AI) to forecast taxpayer sales volumes and predict when will the system cross certain thresholds for instance on a monthly or annual basis. This is particularly useful for identifying taxpayers who may try to stay just below certain tax brackets to avoid higher liabilities. By predicting sales trends, tax authorities can monitor businesses more effectively and ensure compliance with tax regulations.

Clustering Similar Taxpayer Behaviour with Kohonen Networks

The system uses Kohonen networks to cluster taxpayers based on similarities in their transactions, recognizing and analyzing patterns in operations. This approach generates a two-dimensional map where each point represents a cluster of similar transactions, facilitating visualisation and identification of specific taxpayer behavior patterns. This effectively identifies deviations that may indicate potential irregularities. By utilizing input parameters such as invoice issue date and time, sales amounts, and refunds, the system groups taxpayers based on similarities. For such parameter selection, dominant time periods for invoice issuance are identified, as well as the analysis of the sales structure in relation to refunds. Clusters also aid in recognizing taxpayers showing deviations, such as those with disproportionately high refunds relative to sales. Taxpayers appearing in multiple clusters require closer examination, while those stable within one cluster may be less suspicious, but it remains important to analyse the reasons for their activity during certain periods.

Conclusion

As we strive for digitalization and modernization, leading to an increased use of information technologies, big data becomes a new type of resource in business. The TaxCore© system uses large databases as a key tool for improving business processes and increasing productivity in the public sector, as well as for preventing tax evasion. Through sophisticated algorithms and machine learning, TaxCore© efficiently detects irregularities at the sales points, enabling tax authorities to monitor fiscal transactions in near real-time and quickly identify attempts at tax evasion. Rather than relying on subjective judgment, tax administrators now have access to data-driven insights that help them allocate resources more effectively and focus their efforts where they are needed most.

In-Depth Analysis and Management of Tax Risks

By forming and processing these databases, detailed analysis of collected data on financial transactions and the application of machine learning are enabled. Machine learning methods are ideal for mining large databases and identifying patterns based on historical indicators of tax operations.

More Objective Risk Assessment and More Efficient Tax Controls

Identifying and managing risks, as a significant part of the tax system, is often a subjective process. However, by applying certain methodologies and patterns, it is possible to achieve a higher level of objectivity and precision in decision-making. By using real data from the TaxCore© database and defined scenarios, a taxpayer would be assigned a certain categorization of importance or risk level. This would allow ranking taxpayers according to their risk scores and, with expert experience, achieve a more reliable and objective risk assessment.

Macro and Micro Perspectives: From the Tax System to Individual Taxpayers

With the verification of financial transactions and the collection of data on all taxpayer transactions within the tax system of a country, TaxCore© enables a shift from a macro view, i.e., analysis of the entire financial system of the country, to a micro level, where individual taxpayers representing potential risks are singled out and proposed for further inspection. The capability that the system provides is an insight into the historical behavior of taxpayers, assessing the extent to which their operations align with defined patterns, as well as applying machine learning to identify and isolate taxpayers whose financial activities would require further analysis. The data generated in this way serve as a basis for additional investigations, significantly improving the efficiency and accuracy of tax control.

The Future

TaxCore© has been focused on collecting fiscal data from the field under all conditions, excelling through the application of innovative technologies, such as private blockchain for security elements, as well as enabling systems that generate invoices created using various technologies over a long time span to easily adapt and connect with TaxCore©.

With the development of AI and ML, the focus of the entire development is shifting from acquisition to data analysis, pattern recognition, and rounding out the entire TaxCore© into a tool that will enable inspectors to fight against tax evasion most efficiently, while assisting taxpayers in easily and affordably complying with fiscalization rules and preventing unintentional errors that may occur in business.With the advancement of Large Language Models, for the first time, communication between humans and systems using real natural language has become possible. This has opened the opportunity for TaxCore©, in the next step, to unify raw data about taxpayers, transactions, analysis results derived from machine learning, other public data sources, and expose all of this to individuals who will communicate with TaxCore© in the simplest way – in their native language.