See how we helped a leading energy management company improve the accuracy and efficiency of their client invoice reviews. The challenge they faced was address false positive alerts in their rule-based monitoring system, which caused analysts to spend too much time on manual reviews. The result was slower processes, higher costs, increased resource allocation, and Service Level Agreements (SLAs) getting affected.
See how we helped a leading energy management company improve the accuracy and efficiency of their client invoice reviews. The challenge they faced was address false positive alerts in their rule-based monitoring system, which caused analysts to spend too much time on manual reviews. The result was slower processes, higher costs, increased resource allocation, and Service Level Agreements (SLAs) getting affected. We worked together to create an AI-driven platform that uses machine learning models trained on past exception data from billing cycles for accurate distinctions between valid exceptions and false alarms, significantly reducing false positives. The solution also cut manual review workloads by up to 80%, freeing up analysts’ time to focus on critical exceptions, improving operational efficiency, accelerating error resolutions, quicker turnaround, and higher client satisfaction. The solution also helped improve compliance with SLAs as exceptions were handled promptly and accurately. We also implemented ongoing model training to ensure continuous improvement, scalability, and reliability in the changing energy management landscape. Discover how machine learning can improve accuracy, detect errors automatically, reduce manual work, and increase efficiency in your energy management processes. Fill out the form to access the full case study and learn how this automation platform can reduce errors, control costs, and enhance effectiveness. Additional Collaterals Want to see our AI capabilities, solutions and the tools we work on? This page will tell you all about it. Read more about our AI Capabilities Get more insights about the advancements in clean tech, energy and utilities here. Explore the Clean Tech, Energy and Utilities page
As part of service delivery, an energy management company was reviewing the energy invoices of their clients to find savings from erroneous bills and bring in accuracy. Despite rules being implemented to raise exceptions and indicate that an invoice might have a potential error, analysts were required to manually review these bills for errors. The customer was getting high numbers of false positives, putting strains on time, effort, money and meeting SLA requirements.
We collaborated with the customer to create an end-to-end solution that focused on keeping machine learning models at its core. We trained the model on past exception data to help its resolutions determine if an exception is being recorded accurately. We also ensured that the solution allowed room for automation to reduce the amount of manual effort in the process as the solution could reduce false exceptions by as much as 80%.
