ARTIFICIAL INTELLIGENCE IN DETECTING FINANCIAL AND TAX REPORTING ANOMALIES: EVIDENCE FROM ALBANIAN ENTERPRISES
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Mikel Alla
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The increasing digitalization of accounting systems and tax administration has created new opportunities for applying Artificial Intelligence (AI) in financial and tax risk assessment. This study investigates the effectiveness of Machine Learning techniques in detecting anomalies in financial and tax reporting among Albanian enterprises.
The analysis is based on a sample of 100 enterprises operating in the trade, manufacturing, construction, and service sectors. Financial and fiscal indicators were examined using descriptive statistics, multiple linear regression, Random Forest classification, and Isolation Forest anomaly detection.
The results show that the regression model explains 83.6% of the variation in Fiscal Risk Score (R² = 0.836). Profit Margin and Operating Cash Flow exhibit significant negative effects on fiscal risk, while the Debt-to-Assets Ratio demonstrates a positive relationship. Furthermore, the Random Forest model achieved an accuracy rate of 86.7% and an AUC value of 0.98, whereas the Isolation Forest algorithm identified 19 enterprises with anomalous reporting characteristics.
The findings demonstrate that Artificial Intelligence can significantly improve anomaly detection and fiscal risk assessment, providing valuable support for auditors, tax authorities, and policymakers.
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Authors
Mikel Alla
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