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Intelligent detection of fraud in financial statements using deep learning and XGBoost
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Mehdi Farrokhbakht *   , Ali Akbar Akhavan |
| Department of Computer Engineering, FSh.C., Islamic Azad University, Fouman, Iran , me.farrokhbakht@iau.ac.ir |
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Abstract: (16 Views) |
Fraud is a phenomenon that involves deviations and manipulations in financial statements. These actions can lead to tax non-compliance and erode the trust of investors and other stakeholders. Given the intricate nature and vast amount of financial data within organizations, leveraging artificial intelligence as a sophisticated tool can greatly enhance fraud detection in financial statements and bolster confidence in the face of evolving fraudulent tactics. Fraud or deception in the financial information of individuals or organizations reduces the level of trust and confidence that people have in the reliability and integrity of this information. This can lead to serious negative impacts, including loss of trust from customers, investors, and other entities, negative financial and legal consequences, and the exposure of illegal or improper operations that may involve financial crimes. This paper introduces an intelligent method for detecting fraud in financial statements. Initially, the Apriori algorithm is utilized to select pertinent features in the financial data. Subsequently, the performance of the proposed method is enhanced by augmenting the dataset using the GAN-CNN network. Finally, fraud detection is executed with the assistance of XGBoost. The results demonstrate that the proposed method has achieved a fraud detection accuracy of 95.3%.
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| Keywords: Fraud Detection, Convolutional Neural Network, XGBoost Algorithm, Apriori Algorithm |
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Full-Text [PDF 435 kb]
(9 Downloads)
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Type of Study: Original |
Subject:
Decision Analysis and Decision Support Systems
Received: 2025/12/16 | Accepted: 2026/01/27 | Published: 2026/01/27
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