LEVERAGE AND PROFITABILITY THROUGH AI-ENHANCED DUPONT IN INDONESIA’S OIL AND GAS SECTOR - Novita Ratna Satiti; Tingqian Pu; Fitri Putri Lestari (EKUITAS (Jurnal Ekonomi dan Keuangan), 2026)
This study revisits the relationship between leverage and profitability within the DuPont framework by integrating Artificial Intelligence (AI) into financial performance evaluation. The study addresses the limitations of conventional financial analysis in capturing complex and non-linear relationships among financial variables, particularly in capital-intensive and high-risk industries such as oil and gas. It aims to examine the effects of leverage, profitability, and asset efficiency on Return on Equity (ROE) and to assess whether AI can strengthen the analytical value of the DuPont framework. Using a quantitative approach, this research analyzes secondary data from 12 oil and gas companies listed on the Indonesia Stock Exchange during 2022-2024. ROE is decomposed into Net Profit Margin (NPM), Total Asset Turnover (TATO), and Financial Leverage Multiplier (FLM) through DuPont analysis, while an Artificial Neural Network (ANN) is used to identify non-linear relationships. The findings show that leverage has the strongest influence on ROE, followed by profitability, while asset efficiency has a weaker effect. The study concludes that AI-enhanced DuPont analysis improves financial performance evaluation and supports better strategic decision-making.