Fraud-Free Green Finance: Using Deep Learning to Preserve the Integrity of Financial Statements for Enhanced Capital Market Sustainability - Metawa, Noura; Boujlil, Rhada; Alsunbul , Saad (International Journal of Energy Economics and Policy, 2023)
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.