Electricity provider boosts fraud detection accuracy

A major European electricity provider, whose identity we are unable to disclose for confidentiality reasons, is at the forefront of modernizing, maintaining and optimizing its energy infrastructure for enhanced sustainability and efficiency. Hiflylabs collaborated with this forward-thinking effort by developing an anomaly detection solution that can pinpoint fraudulent behavior in residential utility meters.

6x

return on investment in 6 months.

2x

increase in fraud detection accuracy.

Challenge

In the energy industry, it is a major challenge for service providers to detect fraud. Electricity fraud, especially in the residential and SME sectors, can average up to 3.5% of total usage. Traditional selection processes are proved to be inefficient, and providers are unable to allocate sufficient inspector resources on each endpoint. However, data-driven solutions can make the identification of fraudulent behavior much easier.

Solution

Hiflylabs unified a wide variety of data sources: invoices, usage metrics, technical workflows, and customer communications. We developed predictive models that score usage endpoints according to associated risks, including the major risk of metering anomalies that suggest illegal activity.

The comprehensive assessment system Hiflylabs provided was able to list individual high-risk utility meters for the controlling department each month. This modeling engine had proven to be twice as accurate compared to the previously used methodology.

Service

AI

Industries

Energy

Technologies

Python

SPSS Modeler

PostgreSQL

R

Technologies

Python

SPSS Modeler

PostgreSQL

R

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