Reconciling data privacy and added value for customers: conclusions from the SBA blog series on handling data
Ultimately, it’s all about trust
Banks already have suitable arrangements in place to ensure data confidentiality in future. As the first blog in the SBA series explains, banks can build on that advantage by offering customers greater transparency regarding what happens to their data and the concrete benefits they can expect to derive from data sharing. Customers should be allowed to choose not just whether data are shared, but also which data, how much, and most importantly with whom. Once it becomes clear that both parties stand to gain from data sharing, trust will be enhanced, and customers will be more willing to disclose information. Banks can better understand their customers’ specific needs and concerns by engaging in proactive dialogue, thus setting themselves apart from the “data miners” of the new economy. At the same time, the banks will benefit from comprehensive, high-quality customer data that can be used to tap into new areas of business. The key phrase for achieving this positioning is data analytics, according to the blog by Michael Burkhalter.
Artificial intelligence: a critical technology
The blog series also looked at artificial intelligence (AI) – a key technology for the future of banking. The gains made in terms of speed and efficiency by implementing initial-use cases, however, have quickly raised the question of how scalable AI solutions are. Christoph Schrills describes how agile methods such as DevOps and focused AI labs offer a means of developing solution strategies and addressing uncertainties arising from internal factors, regulatory requirements, and ethical considerations. Christian Diethelm-Spiss writes in his blog that lack of AI skills among staff, insufficient knowledge of the benefits and limits of AI, and challenges concerning data (e.g., data silos) are still the main obstacles. Many banks have already set up analytics teams and departments that bring together a range of different skill sets. The success of AI projects often hinges not just on the capabilities of data scientists, but also on agile, interdisciplinary teams that cover the broad spectrum of required profiles. One key support role in such teams is that of the analytics translator.
Organisation – a critical factor
According to Werner W. Wyss, the strategic aim of every bank must be to capture the value of data analysis in everyday business and enhance it in a targeted manner. To achieve this, the tasks, skills, and responsibilities for handling data must be expediently and functionally delegated across the whole bank. Existing "typical" functions that take a structured approach to data-related matters are ideally supplemented by a data management competence centre. The data itself must consistently be of sufficient quality for necessary processing, whilst also being available in a form that meets the additional requirements of the bank and its staff. Processes for handling data must therefore be expediently restructured along the whole life cycle. Such increased efficiency in data usage makes it possible to provide customers with up-to-date, personalised offers and services at all times, which are ideally tailored to their requirements.
Customer focus at the heart of everything
A general realisation resulting from the SBA blog series is that digitalisation, and consequently also the correct handling of data, is not an end in itself. The goal of all banking activities must be to generate added value both for customers and for the bank itself. In his blog, Roger Jäggi describes how the relationship between customers and banks has changed fundamentally in recent years. It is therefore important to understand changing customer needs and to take these into account when digitalising products, services, and processes. If digital experiences are created as part of customer journeys, and processes are simplified and digitalised, thus improving the overall provision of services, then customer focus can go hand in hand with cost optimisation and can also be clearly beneficial in terms of increasing revenues.
PET as a possible solution for ongoing conflicts of interest
One problematic area covered by the SBA Guideline but not specifically dealt with in the blog series, is the trade-off between broader data sharing and data privacy. Privacy-enhancing technologies (PET) may help to resolve this conflict. A study published by Swisscom and Lucerne University of Applied Sciences and Arts (HSLU) describes in detail how these technologies can address and resolve the consequences of data sharing for customers’ data privacy by controlling sensitive information and data protection. Although many of the technological approaches with exotic names such as “Trusted Execution Environment”, “Differential Privacy”, “Homomorphic Encryption”, “Zero-Knowledge Proofs”, “Federated Analysis” and “Secure Multiparty Computation” are still in the development phase, the potential for the financial sector is considerable.
At best, these technologies make it possible to break into new business areas, increase revenues through efficiency gains, and trim costs, while at the same time minimising the risks of data sharing. Hopefully these technologies will be able to resolve potential friction between data privacy and added value for customers. This will allow the economic potential of data usage in the financial industry to be fully exploited to the benefit of the customer. Ultimately many of these developments are also linked to social and economic questions that can only be answered through broad public debate and continuous dialogue between customer, financial market participants and the authorities. The SBA and its members will therefore continue to actively engage in this dialogue as it works to promote an innovative and strong Swiss financial centre.