Billing controls & financial governance
Here's what AI can do for this role — and what still needs a human. Built straight from ENSEK's own job advert, running live on real data. Not a slide about AI. The job, getting done.
Every line on the left is lifted from ENSEK's actual job ad. If a card lacks a harvested JD line, it is omitted. On the right is the AI doing it — with eligible cards running live against the warehouse and offline inspection clearly labelled in the workspace.
“Own the financial relationship with assigned B2B clients, acting as their trusted advisor on energy accounting and revenue assurance matters.”
What is the debt book — gross receivables by customer segment, average balance, and the high-balance outlier count the CFAM must explain?
table“Oversee the delivery of monthly financial outputs, including revenue recognition, gross margin, reconciliations, variance analysis, and controls reporting.”
What is the refund candidate queue — accounts holding a credit balance owed back to the customer, by segment and region?
table“Review and challenge analytical work produced by the Customer Financial Assurance team, ensuring accuracy, completeness, and clarity before client submission.”
What is the settlement gap — traditional (non-smart) meter accounts by region and fuel type that carry the highest estimated-read settlement risk?
table“Provide commercial insight into consumption trends, billing performance, and financial risks, translating technical data into clear, actionable recommendations.”
How effective are the arrears controls — arrears rate by tariff type and PSR overlap, to assess whether controls are working proportionately?
table“Collaborate with operations, billing, data, and product teams to resolve issues, improve data quality, and strengthen financial controls.”
What is the revenue concentration risk — top segments by estimated annual revenue, with the HH/NHH settlement-class split?
tableThe honest other half. AI does the analysis; a person owns the decision — especially where regulation, fairness and accountability bite.
A plain-English question — the same one the job ad describes — is translated to SQL by the agentic backend.
Curated cards run server-side against MotherDuck when eligible. The workspace separately labels any local inspection path.
Runs through the workspace live path when a curated server query is available; otherwise the local inspection path is labelled.. No synthetic numbers.
Each figure carries a falsifier — recomputed from the result set, not a stored number, so it can't quietly drift.
It's the role getting done: curated questions run live server-side against the warehouse; local inspection is labelled inside the workspace.
Open the live workspace →Provenance. 5,000-row local verification dataset of the national energy estate — same accounts dataset as the credit-risk workspace. Distributions match ENSEK's real account book. No real customer data.
It's Sorted — I took ENSEK's job ads and didn't write a report on what AI could do. I built it. Get the rest sorted →
I'm trained on this proof and the real ENSEK: the Ignition meter-to-cash platform (seven modules), the move under Centrica in 2024, 7M+ energy accounts migrated for suppliers like British Gas and Utility Warehouse, and the Ofgem framing. Ask me how the Data Analyst function changes shape, or which open roles map to which Ignition module.