The goal is developing a database analytic model to identify customers with potential credit rating risk. Identifying them and aware early point of time. Even earlier than they have the credit rating risk. Their value from the BigPrivacy solution is, we are able to develop the model with external partners using real data but completely Pseudonymised real data. So, the data is 100% consistent, it's 100% real but it's not linkable with the customers behind.
An expected benefit from our point of view is identifying the risky customers earlier and with the early identifying we are able to handle the risks much better, and maybe help them to get out of the problem situation. And, finally not to lose money for the bank. The second use case is the data enrichment within the group entities. This means combining insights of group entities to create customer benefits. Their added value from the BigPrivacy solution is we can share insights with intergroup entities, but only sharing the insights and not the identity behind the insights. This can help us to identify the customer needs more accurate, and it's a real large cross and upselling potential in all the group entities.
Last but not least, we want to open the data for innovative cooperation which means providing data with full color consistency and full quality for verifying ideas and ideas of some startup. They are able to use real data but also in a full, Pseudonymised way. So, there is no problem with GDPR. The expected benefit for us and the customers is having some real cool services, developing some cool services they are willing to pay for.
Finally, it's a benefit for the customers and also for us as a company. So, this was in a quite short way, the outline of our BigPrivacy project at Raiffeisenlandesbank.