The financial world is facing profound change. The introduction of wCBDC is giving rise to new technological Banks, and savings banks also address countless parties around the world every day in their payment transactions. This is because for payments outside the European Economic Area (EEA), the payer's address must be provided, and the recipient's address should also be provided to ensure smooth processing. This is necessary for the required checks, such as money laundering and fraud prevention.
The enormous variety of address formats worldwide has not been a major issue until now. This is because address data is provided in an unstructured format. Within the payment file, simple fields are available for this purpose, the address lines (AdrLine), in which the address is provided as free text. Only the name must be shown separately in each case.
SEPA 2.0 will put an end to this. In future, address data must be delivered in a structured format – for all SEPA payment formats. The changes will come into force gradually from November 2023. From November 2025 at the latest, address data for SEPA transfers may only be delivered in a structured format. And the challenges are not purely European: Swift and other market infrastructures have the same time frame. For payments within the EEA, the provision of address data remains voluntary. However, if banks decide to deliver it, it must also be done in a structured format.
This means that in future, every component of an address must be entered in the field provided for it. The Payment Markets Practice Group lists a total of 14 characteristics that can be assigned to a postal address.
The example shown in the graphic is simple. Everyone in this country knows that 9 is the house number and Wiesenweg is the street name. Converting this data into the new format takes only a few seconds – provided the application has this capability.
But even then, the transformation would be a mammoth task. Banks and savings banks have millions of address records that need to be converted. And such simple addresses are the exception. If you estimate the work involved, a simple calculation quickly shows that it would take up to 250,000 working hours for an average bank with 500,000 corporate customers. Added to this is the cost of training to equip employees with the necessary expert knowledge of global address formats.
Given the scope of the task, efficient solutions are required. Regular expressions are not an option in this case. As shown above in the example of the USA, address data can vary greatly even within a single country and does not follow a regular structure. In addition, countless test data would be necessary.
Another option is address data services, such as those offered by Google. However, these are not only expensive, but also questionable from a data protection perspective. In addition, such services are often limited to certain regions or even countries.
An application based on artificial intelligence (AI) can provide a remedy. This allows data to be automatically converted into the required structure. AI can recognise structures based on specified training data and transfer them to other cases.
At PPI, we are happy to help banks prepare for and implement the transformation of address data. This includes selecting and adapting the appropriate AI application, as well as choosing the necessary training and test data.
Ultimately, institutions receive a powerful and reliable solution that benefits more than just the banks themselves. This is because corporate customers will also have to provide address data in a structured format in the future. Banks that take on the necessary transformation for companies can gain a significant competitive advantage.
