Optimization of Energy Finance Risk Compliance Early Warning System Based on Machine Learning

The energy finance sector is characterized by its complexity and volatility, driven by fluctuating commodity prices, regulatory changes and evolving market dynamics. Energy companies face financial, regulatory and operational risks, relying on historical data and static risk assessment frameworks that may not accurately reflect real-time market changes. This research aims to develop an energy finance risk compliance early warning system, leveraging a machine learning approach to enhance the early detection of compliance risks, enabling proactive decision-making and improving organizational resilience. Initially, data were collected from various sources, including historical financial records, market trends and regulatory frameworks. These data are essential for developing an early warning system that aims to enhance compliance risk detection in the energy sector. The collected data are preprocessed using cleaning and normalization and prepared for analysis. Exploratory Data Analysis (EDA) was conducted using statistical methods such as correlation analysis and regression analysis to identify patterns and relationships between variables. The study proposes a novel Revenue Optimizer with a weighted Support Vector Machine (RO-WSVM) model to enhance risk detection and ensure regulatory compliance in energy finance. Optimizing revenue while adhering to compliance standards provides proactive insights for effective risk management and decision-making. The result demonstrates that the application of the proposed RO-WSVM model works successfully and this is because RO-WSVM has higher accuracy (90%), error value (0.1%) and less time taken to process the data than any other model (6s). The study highlights that the innovative approach RO-WSVM enhances the prediction model in the finance risk compliance early warning of energy finance.