Menu
For every electronic payment in Belgium made by a consumer on one of the 5500 payment solutions of our client, multiple data points are collected: card type (consumer/business), payment scheme (Visa, Mastercard, AMEX, …), transaction amount, geographical area, VAT number.
This data is gathered for every acquirer our client is working with. Although rich, none of this valuable data was being exploited for improving the quality of their services. Our client reached out to us for providing them with adequate reporting tools and analysis (without confidential data, under the strict respect of the GDPR regulations), allowing them to understand their clients, the attractiveness of different sectors, and detected fraudulent clients.
Our solution includes a series of Power BI dashboards that provide advanced business analytics and reporting tools. These dashboards allow the company to gain insights into their clients, identify attractive sectors.
The Power BI dashboards provide real-time data visualizations that enable the company to make data-driven decisions quickly and efficiently. They allow the company to monitor key performance indicators, such as transaction volumes, revenue streams, and payment trends. The dashboards also provide drill-down capabilities to help the company analyze their data at a more granular level. The solution also allows adding new acquirers, and the dashboards are updated on a monthly basis, after the data is received from the acquirers every first Monday of the month.
What is next? We have iterations planned to continue refining the Power BI dashboards to meet the evolving needs of the company and its merchants. We will work closely with the client to ensure that our solution remains up-to-date and relevant as the payment landscape continues to evolve. Furthermore, we will also provide training to an admin at the client to ensure that they are equipped with the skills and knowledge necessary to maintain and update the solution independently. In addition, we plan to leverage machine learning algorithms to provide more advanced analytics capabilities. By implementing clustering and predictive models, we can help the company identify patterns and trends in their data that may not be immediately apparent through traditional data analysis methods.
We started by collecting the datasets coming from the different acquirers in Belgium, use the client CRM data as well as data from the Crossroads Bank for Enterprises Open Data (BCE). We then had to build a robust data model, allowing the integration of the monthly reports received by the different acquirers. Once enough data was collected, we could start building the dashboards in an agile way of working, iterating on the visualizations (i.e., the analysis the client wants to perform) and the different reports. The reporting tools setup, together with the dashboards and filters, help the management of the company answer their most critical questions.
Excel
Python
Power BI
Newsletter
