Data Reconciliation

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Telecommunication

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Charges for end customers were not calculated correctly.

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Dashboard, ML model

INITIAL SITUATION

Our customer experienced issues in which users were occasionally overcharged or undercharged for their mobile plan.

This was due to inconsistencies between CRM and billing data.

SOLUTION

A dashboard was built in which inconsistencies could be easily visualized.

An underlying ML model was used to detect and prevent such inconsistencies.

BUSINESS VALUE

The revenue assurance department could detect and prevent over / undercharging.

In this way the customers’ satisfaction and the financial transparency increased.

FRAMEWORK & TOOLS

PysparkSklearn, Oracle, Pandas, Tableau</span

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