AI Optimization of Resolution Strategy in Utility Billing and Revenue Assurance

Abstract

Sustainable profitability for utility companies hinges on the reliability of their billing and revenue collection processes. While the majority of billing operations are efficiently managed through Robotic Process Automation (RPA), there remains a segment that eludes automation and will be delayed. This portion of the billing requires manual intervention to complete the billing process. The timely resolution of these bills is especially important for SOUTHERN CALIFORNIA EDISON since they might be subject to Tariff Rule 17 and result in permanent lost revenue. Unresolved bills also affect customer satisfaction adversely. Ensuring that these manual processes are handled promptly and accurately is crucial in maintaining the financial health of the company and fostering customer trust.Efficiently addressing these challenges can enhance operational efficiency and support the long-term growth of utility companies as well as excellence and continuous improvement. In this study, we explored the delayed bills accounts to identify patterns and trends. We combined our findings with machine learning models, such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model, to enhance the process of addressing these delayed bills. This method selectively targeted accounts for a more efficient resolution, reduced lost revenue and brought in greater profitability. Moreover, we expanded this analysis by utilizing predictive models to detect future accounts that are likely to encounter repeated issues. This proactive approach contrasts with the current reactive measures, providing opportunities for improving the efficiency and effectiveness of bill resolution.

Keywords: Billing, Delayed Billing, Revenue Assurance, Operation Optimization, Clustering Models, Predictive Models

DOI: 10.54941/ahfe1006035