Curious if anyone has experimented with AI tools like ChatGPT or other machine learning approaches for utility bill analysis. I've been playing around with training models to flag potential billing errors and it's showing promise. Wondering if others have gone down this path and what results you've seen.
Anyone using AI tools for bill analysis yet?
Tina, this is fascinating stuff. I've been following the AI developments closely but haven't implemented anything yet. What kind of accuracy are you seeing with error detection? Main concern is false positives - last thing we need is to chase phantom billing issues. Are you training on historical data from specific utilities or trying to build something more universal?
We started using some basic ML algorithms to analyze Avista bills here in Spokane. Nothing too sophisticated but it's caught several calculation errors that traditional auditing might have missed. The key is feeding it enough clean training data. I'd estimate it's improved our error detection rate by maybe 30%. Still requires human verification of course.
Randy, I'm seeing about 85% accuracy on Consumers Energy bills, which is actually better than I expected. False positive rate is around 15% but they're usually edge cases that are worth double-checking anyway. Training on utility-specific data definitely helps - each utility has its own quirks in how they calculate charges. Willa, 30% improvement is significant! What algorithms are you using?
This is the future of our industry right here. I've been manually auditing Entergy bills for 15 years and the amount of data we process now compared to even 5 years ago is overwhelming. Any tool that can help pre-screen for anomalies would be huge. Are you guys building these in-house or using existing platforms?
Clem, mostly using scikit-learn in Python with some custom preprocessing. Nothing too fancy - random forest classifiers work surprisingly well for identifying outliers in demand charges. The hard part is feature engineering to capture utility-specific billing rules. Each rate schedule requires different training approaches.
One concern I have is liability. If we're relying on AI to flag issues and it misses something significant, how does that affect our professional responsibility? I love the efficiency gains but we still need human oversight at every step. Has anyone's E&O insurance weighed in on using AI tools for professional services?
Great point Randy. I treat AI as a screening tool, not a replacement for analysis. It helps me prioritize which bills need deeper review, but every finding still gets manual verification. Think of it like spell-check - helpful but you still need to read your work. My insurance carrier was actually interested in the risk reduction potential since it could catch errors humans might miss.
I've been hesitant to dive into AI tools but this discussion is convincing me to at least experiment. For Kentucky Power accounts, I spend probably 60% of my time just identifying which bills warrant detailed analysis. If AI can help with that triage process, it would free up time for the complex problem-solving work that really adds value.
As someone new to the industry, this AI discussion is exciting but also a bit intimidating. Should I be learning Python and machine learning alongside traditional auditing skills? Don't want to get left behind as the field evolves. Any recommendations for getting started with the technical side?
Faye, I'd say focus on mastering the fundamentals first - understanding rate structures, tariffs, and billing calculations. The AI tools are just that - tools. They're only as good as the domain knowledge behind them. Once you're solid on the basics, then exploring automation makes sense. FPL bills are complex enough without adding ML complexity on top!