Platform Engineering
Governance
Nexi_digital payments_background
Scopri
Business Summary

Nexi is the European PayTech, present in high-growth European markets and technologically advanced countries. Listed on Euronext Milan, Nexi has the scale, geographical reach, and capabilities to lead the transition to a cashless Europe. With an innovative product portfolio, e-commerce expertise, and industry-specific solutions, Nexi provides flexible support for the digital economy and the global payment ecosystem through various channels and payment methods. Nexi’s technological platform and best-in-class professional skills enable the company to excel in three market segments: Merchant Solutions, Issuing Solutions, and Digital Solutions. Nexi consistently invests in technology and innovation, focusing on two fundamental principles: meeting the needs of its customers in collaboration with partner banks and creating new business opportunities for them. Committed to supporting people and businesses of all sizes, Nexi transforms the way people pay and businesses accept payments, offering customers the most innovative and reliable solutions to better serve their clients and grow. This is how Nexi promotes progress for everyone: simplifying payments and enabling individuals and businesses to build closer relationships and grow together.

Challenges and Goals

To provide business users with increasingly effective tools to monitor Nexi’s credit status and enable informed decisions, it is necessary to structure credit data in a single, easily accessible, reliable, and documented point. In synergy with Nexi’s Risk Management and Data areas, our goal is to integrate and collect credit data currently managed by various applications, management programs, and paper documents into a unified repository. We aim to model them to be easily adaptable to Nexi’s business processes.

The main challenge involves integrating heterogeneous data: different collection, update, persistence, and semantic logics. Given the need to integrate information from various domains, it is crucial to normalize, standardize, and model data extracted from sources to ensure consistent semantics. This facilitates end-users’ efficient access to data and enhances its value.

Among the main project objectives are:

 

Defining the data domain within the project scope is essential for proceeding knowingly during the analysis, development, and testing phases. Specifically, the project integrated information related to the following domains:

  • Economic transactions executed with both credit cards and point-of-sale (POS) transactions;
  • Unpaid amounts, i.e., sums of money not repaid within the due date;
  • Recoveries, i.e., sums of money recovered following an unpaid amount;
  • Losses, i.e., sums of money considered unrecoverable in the face of an unpaid amount;
  • Disputes, i.e., disputes over transactions by both Nexi cardholders and affiliated merchants.

Additionally, we integrated credit score information provided by various Credit Bureaus such as Cerved, Crif, and Experian. By determining this information with the smallest granularity for each customer, it was possible to calculate various economic Key Performance Indicators (KPIs) to make more informed decisions both during merchant onboarding and customer portfolio monitoring.

The first objective, to define a precise scope, is to identify all data sources with which business units interact in their daily operations. These sources may include both digital and paper-based information, clear and anonymized data, aggregated and specific data, structured and unstructured data. This information has been integrated to support both the onboarding process for new cardholders and merchants and the customer portfolio monitoring process. The onboarding process involves conducting personal, economic, anti-money laundering, and credit score checks for new Nexi cardholders and merchants using Nexi’s electronic payment services. The monitoring phase, on the other hand, involves periodic verification of the economic performance of Nexi cardholders and affiliated merchants to assess potential risks and intervene if necessary.

Once all data sources are defined and delimited, it is necessary to converge credit information into a single point to achieve a comprehensive view for each data entity. This ensures a single regulated access point to the data, avoiding inconsistencies and unnecessary data duplications. The data in the target repository has been structured to ensure consistency with the source data. These two requirements will enable end-users to be confident that the exposed data will be complete and always up-to-date.

The credit data within the scope of this project primarily refers to the millions of daily payment experiences managed by Nexi. These involve substantial amounts of data that must be processed, integrated, and made available to a wide audience every day. Developing optimized technical solutions to minimize the load on source systems, for example, through incremental integration logic, is therefore an essential requirement to have timely access to data for business analysis and to avoid compromising the performance of source systems, which must continue to fulfill their tasks.

As the data involved in this project pertains to economic entities, it is crucial that the information in the repository aligns—both in terms of amounts and volumes—with the data in the company’s accounting systems. This is essential because the repository will be accessible to stakeholders from administrative functions. To achieve this, it is necessary to implement automatic mechanisms enabling data quality monitoring and data modeling that allows reconciliation between information from management systems and accounting entries.

Solution

Quantyca has supported Nexi in achieving the following objectives: 

During the functional and technical analysis phase, meetings were organized with various business units to document both internal processes and management systems used by operational teams. The output of this phase provided a detailed overview of the operational activities of each team, the management systems supporting business activities, and the identification of credit data in source systems. This information was compiled into functional documentation, which was then delivered to the business. To effectively manage the complexity of the domain, a task schedule was also defined across different project streams, each linked to a specific functional area and its credit-related processes.

Using Informatica PowerCenter, we implemented data integration flows that extract credit data from various data sources—such as SAP, Salesforce, Credit Bureau, and External Databases. If necessary, the flows normalize the data, load it into the shared repository, and define data access interfaces for various stakeholders and applications. Employing the ELT paradigm to maximize the computational power of the Nexi DWH engine allowed us to implement lightweight integration flows, enabling faster data alignment in the DWH. Common orchestration, logging, and monitoring logic for flows facilitated both development and potential troubleshooting. Leveraging the data quality framework extensively used in all Nexi data projects ensured additional ease of flow monitoring and data observability.  

In order to ensure the reliability of processed data, reconciliation logics have been implemented between the management data loaded into the shared repository and the information present in the accounting systems. This step was crucial to align accounting entries with data from various management systems, enabling analyses and uses of data previously unattainable within the accounting systems.

Results

Nexi has achieved the following objectives:

  • Detailed and shared documentation of operational processes across various business units and a comprehensive inventory of credit data in source systems.
  • Standardization of all credit domain-related information into a repository, building a complete, up-to-date, and easily accessible view.
  • Alignment between credit information from management systems and accounting systems, enabling independent and comparative analyses by administrative and operational offices.
  • Preparation for new reporting and data analysis opportunities tailored to the needs of different business units.

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