CEE22: Open banking 2.0 and using standards

The changing concept of open banking

At the time of the introduction of open banking concept in Europe, open banking regulation meant that banks must share transaction history and account balances of customers with 3rd party payment providers to enhance competition on the payment market. The PSD2 regulation made banks open up their systems via APIs to third party entities.

Data driven banks

With the opening of banking interfaces for third parties, banks will have the opportunity not be only providers of data, but they can turn the implemented interface infrastructure imposed by regulation into opportunities to upgrade their internal processes and generate new revenue streams.

The open banking interface infrastructure can be good starting points for banks not only providing data but also becoming consumers of data from other data sources. For an end-to-end servicing of customers banks must be able to manage data much more flexibly than ever before, both within the bank and when exchanging data with third parties.

The emergence of platform banking and data ecosystems pushes banks into this direction.

This means that banks must enable themselves to deal with multiple data sources and multiple data structures.

Data sources will be any other systems or open datasets that the bank wants to process. Multiple data connections can only be established if the integration efforts are minimised or even eliminated. Otherwise, the number of data connections that the bank can establish, will be limited, which will limit the bank’s opportunities for value creation and customer service.

Banks in the future must enrich the data that they can work with: various real time/continuously updated structured data sources can help a financing process, e.g.:

  • Access to real estate advertisement or sales databases can help the bank make a better evaluation of property for a housing loan
  • Digital invoice data can be used as an input for supply chain financing process, and invoice data assets of an enterprise help banks to have real time access to enriched data that can be used to build better scoring or cash flow forecasting models.
  • Travel booking data or online purchases of a customer provides opportunities to refine segmentation, cross-sell or upsell models for banks.

Using data within the bank

Banks can use structured data for enhancing their own internal processes: in this case the data can be used to digitalize or automate processes, or provide inputs for data modelling purposes, such as scoring models, product development or pricing models. In these use cases the bank can use other, external data sources, which are not originated by the customer within the bank, such as the examples above.

Exchanging data with third parties

For managing non-banking data internal systems must be upgraded to be able to receive enriched and changing data sets, in many cases with real time data exchange capabilities. When receiving data from third parties, the bank will need to define, what data structures it wants to work with, and the source data structured must be mapped with the internally used data structure. As the amount of externally generated data will increase within the bank, it will be important to manage data in an integrated way: it will be important that 3rd party data assets are not separated from each other but can be used from a central data storage/data lake to enable various uses cases throughout the organization.

For example, embedded finance will expose the financing product APIs to 3rd party systems, while running scoring models in the background. The bank will use transaction data generated by the merchant both regarding the individual transaction for processing the financing request and mass transaction data for the scoring model.

Data as product

For using and managing the data flexibly, banks must implement new approaches to deal with real time data from multiple data sources and multiple data structures. A recent article from the Harvard Business Review [1] and McKinsey analysis[2] both state that the most efficient approach to use data as a product. According to the Harvard Business Review article: “A data product delivers a high-quality, ready-to-use set of data that people across an organization can easily access and apply to different business challenges.”

This means that from the data sources available within the organisation, subsets of data can be selected for specific use cases and the data are easily accessible throughout the organisation for various use cases and teams.

This new approach to data management can only be enabled using standardized datasets. Standardized datasets allow having a universal dataset, from which subsets can be defined for various use cases.

The organisation can define their own proprietary standards, or it can also rely on already existing standards. Using already existing standards saves a lot of time and effort in each phase of working with data:

  1. Set-up: standards have been implemented by a group of industry practitioners, having faced many challenges with datasets widely used for that specific use case: it will save time for your data standardisation efforts.
  2. Integration management: the use of standards decreases the efforts for integration, when widely used.
  3. The use of data standard enables interoperability[3] for data exchange with third parties without 1-to-1 integrations if counterparties also implement the standards. Good example in banking is the implementation of ISO20022 or the UBL standard in e-invoicing.
  4. Maintenance of data assets: even if there are diversions from the official standard, it is easier to manage the individual diversions rather than managing and maintaining completely proprietary datasets continuously.

Standardisation initiatives in the financial industry

The PSD2 regulation made banks open up their systems via APIs to third party entities. However, the directive did not provide a standardised data structure for implementation, which caused banks implementing the regulation according to their own interpretation.

There were standardization initiatives such as the Open Banking Standard in the UK, and the Berlin Group Access to Account (XS2A) Open Banking Framework. These standardization initiatives follow the already ongoing upgrading of the financial services industry to the ISO20022 standards.

Other uses cases will be added continuously as the need arises, so new standards will be available.

The future is now

Banks are at different phases of their way to using standardized data. With the exponential growth of processable data the question will not only be for open banking how open or closed you want to stay for third parties, but whether you can build capabilities to work with structured data in an agile way.

How to move forward with implementing Open Banking 2.0 supported by data?

  • Be prepared to open up to exchange data with 3rd parties
  • Become a party in data ecosystems
  • Look for new use cases of your data assets
  • Use standards
  • Realize returns on your data assets

Come to the upcoming CEE22 SME Banking Conference to hear from the banks about their open banking strategy and how they think about data. Register here!


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[1]A Better Way to Put Your Data to Work (https://hbr.org/2022/07/a-better-way-to-put-your-data-to-work)

[2] The data-driven enterprise of 2025 (https://www.mckinsey.com/business-functions/quantumblack/our-insights/the-data-driven-enterprise-of-2025)

[3] Interoperability refers to the seamless execution of a business pro- cess by various counterparties with different levels of automation and time-to-market requirements or capacity. Source: ISO20022 for dummies