Artificial Intelligence Based Risk Prediction System for Electricity Bill Recovery

The utilization of predictive models in conventional research has often faced challenges in adequately capturing the intricate factors influencing Electricity Bill Recovery (EBR) risks, leading to a diminished accuracy in prediction outcomes. To address this limitation, a cutting-edge approach involving advanced artificial intelligence algorithms has been introduced to formulate a more precise risk prediction model tailored specifically for EBR. In this innovative framework, a Bidirectional Long Short-Term Memory (LSTM) model has been meticulously constructed. This model is designed to incorporate temporal data, allowing for the comprehensive consideration of both past and future information in predicting the risk status of EBR. The bidirectional LSTM model demonstrates a remarkable average accuracy of 98.0% in classifying EBR risks, signifying a significant enhancement in predictive performance. By adopting the bidirectional LSTM model, the overall efficacy of risk classification for EBR is substantially improved. This advancement not only ensures a higher level of accuracy but also enables timely and effective management of EBR. The integration of artificial intelligence in this context showcases the potential for transformative improvements in predicting and mitigating risks associated with electricity bill recovery.