Reduced bad debt losses thanks to machine learning processes 

Challenge:

With increasing digitization, customer expectations are changing – also when it comes to concluding energy contracts. Quick, digital order lines are now standard and are no longer enough to differentiate one’s company from the competition. More and more energy suppliers are therefore offering their customers attractive bundle products in addition to their classic energy contract. As a result, the energy suppliers are struggling more frequently with the risks and challenges from the classic eCommerce business: attractive products not only attract customers who are willing to pay, and the loopholes that online sales offer are readily exploited. Fraudsters often target the expensive devices, causing additional expenses and financial damages for the energy supplier.

Solution:
We have developed an analysis, which aims to improve selectivity as early as the application management stage. By enriching the existing customer data from the order process with our context data, a model was trained for a pilot customer of E-MAKS on the basis of machine learning. With the model, each contract application can be additionally evaluated in the future. Based on the trained model, a decision is made as to whether the customer will be rejected by the system or whether the contract will be concluded. This significantly reduces the number of bad debt losses for the energy supplier. Moreover: the number of customers wrongly rejected by the system but potentially willing to pay is reduced.

 

Customer testimonials

»With the analysis by Geospin, we aim to reduce bad debt losses of our clients and increase sales.«

Glenn Boeckxstaens, Head of Receivables Management E-MAKS GmbH & Co. KG

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