Use Case · Utilities / Infrastructure / Telco
Billing
& Revenue Processes
Billing exceptions, adjustment postings, tariff changes and credit notes processed automatically. AI detects anomalies in mass billing runs before they reach the customer.
Why Billing Is a Lever
An incorrect invoice costs more than just a credit note
Energy suppliers, network operators and telcos process hundreds of thousands to millions of bills per year. The majority run automatically through the ERP or billing system, whether SAP IS-U, Schleupen, kVASy or Amdocs.
But 5-15% of all bills generate exceptions: missing meter readings, implausible consumption, tariff changes mid-period, moves with overlapping periods, feed-in tariff payments.
Every exception lands in the manual correction queue. An operator reviews the case, researches across systems, corrects, rebills and potentially issues a credit note. Per exception: 15-45 minutes. At 5,000 exceptions per month, that is 1,250-3,750 hours. That amounts to 8-23 full-time staff just on billing corrections.
Worse still: errors that go undetected. A mass billing run with a systematic error, such as the wrong tariff applied to 10,000 customers, can mean millions in credit notes and reputational damage. And for regulated network charges, it is a compliance risk.
How the Process Changes
Before / After
per month, with 3,500 processed automatically
The Solution in Detail
How We Automate Billing Processes
01
Anomaly Detection Before Invoice Dispatch
AI analyses the mass billing data before dispatch: consumption spikes, tariff inconsistencies, implausible amounts, systematic patterns. Erroneous invoices are stopped before they reach the customer.
Statistical anomaly detection at individual customer and portfolio level. Comparison with prior year, peer groups and expected consumption. Automatic thresholds, self-learning.
02
Automatic Exception Classification & Correction
Every billing exception is automatically classified: missing meter reading, tariff change, move, feed-in payment, dunning hold. For 70% of cases, an automatic correction is applied. The remainder is presented as a pre-researched operator brief.
03
Credit Notes & Adjustment Postings
Where a correction requires a credit note: automatic calculation, posting and customer notification. Automatic offset against the next invoice or refund. Fully documented for audit.
Integration with SAP IS-U / Schleupen / kVASy. Automatic coding. Four-eyes principle configurable. Complete audit trail.
04
Billing Dashboard & Quality KPIs
Central dashboard: exception rate, correction volume, top error causes, cycle times. Drill-down to individual case. Early warning on rising exception rates, before the next billing run problem materialises.
Power BI dashboard. KPIs: exception rate, STP rate, correction volume in £, cycle time, customer complaint rate. Trend and root cause analysis.
Results
What Billing Automation Typically Delivers
−70%
Manual
corrections
85%
Faster exception
handling
0
dispatched
−40%
complaints
The greatest lever is anomaly detection before dispatch. A systematic error affecting 10,000 customers, caught before invoices are sent, saves £100,000+ in credit notes and reputational damage.
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Ready?
How many billing exceptions does your team process per month?
Let us look in 30 minutes at which exception types can be automated immediately, and how anomaly detection can prevent systematic errors.
No sales pitch. Just an honest assessment.
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