Financial Statements Fraud and Data Mining: A Review

Sanad, Zakeya; Al-Sartawi, Abdalmuttaleb M. A. Musleh

doi:10.1007/978-3-030-77246-8_38

Cited by 49 publications

(1 citation statement)

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“…In practice, selected financial ratios are more sensitive than others in detecting fraud in financial statements; based on which a logistic regression model for detecting financial statements fraud is developed (Kanapickienė and Grundienė, 2015). Additionally, traditional regression analysis has also been utilized to uncover fraud in financial statements through fraud signals, but more sophisticated data mining techniques have emerged to assist auditors, forensic accountants, and regulators in combatting fraud (Sanad and Al-Sartawi, 2021). Statistical and Machine Learning Algorithms have been widely used in detecting financial statement fraud, namely logistic regression, support vector machines, artificial neural network, bagging, C4.5, and stacking (Perols, 2011).…”

Section: Introductionmentioning

confidence: 99%

Fraud-Free Green Finance: Using Deep Learning to Preserve the Integrity of Financial Statements for Enhanced Capital Market Sustainability

Metawa,

Boujlil,

Alsunbul

2023

IJEEP

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In the era of green finance and sustainable energy markets, preserving the integrity of financial statements is paramount. This paper introduces an innovative approach to financial statement fraud detection through the application of deep learning techniques. We employ a Temporal Convolutional Network (TCN) to analyze stock market data and identify deceptive practices. Our study meticulously reviews related work, highlighting the critical intersection between financial statement fraud detection and the principles of green energy finance. The Materials and Methods section outlines our data sources, variable selection, and the TCN model's architecture, while the Results and Discussion section presents comprehensive evaluations and comparisons against traditional baselines. Our findings demonstrate the exceptional accuracy and reliability of the TCN model in detecting financial statement fraud, underscoring its potential to instill transparency in energy markets. In conclusion, our research contributes significantly to the promotion of financial integrity and sustainability, offering a powerful tool for investors and stakeholders committed to responsible and ethical investments within the framework of green finance.

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Section: Introductionmentioning

confidence: 99%