Banks are often described as possessing a huge pile of customer data but being unable or unwilling to leverage it. We confronted five industry experts with this statement asking what is hindering banks to monetize their rich data reservoirs? Here are their answers and recommendations on how banks could overcome them.
IT legacy – banks’ IT systems are not in shape to allow state-of-the-art data analytics
An often described hurdle to leverage data is the IT legacy system of incumbent banks. While the mere size of the data banks own could be a rich resource, the IT systems are not (yet) consolidated data pools that can provide information. Even in collaboration with fintech companies that developed efficient algorithms to perform smart data inquiry, implementation often fails after a successful proof-of-concept stage. The data is not structured and stored in ways that allow for relevant and timely data consultation. So, where to start?
The unique data of banks are spending data. An expert recommendation is to stratify spending data according to customer demographics for a time horizon of the past five years. Some experts recommended that effective and efficient usage of data would only be possible if banks were building new systems from scratch and migrate carefully selected data (e.g. the last five years) subsequently.
Talent turnover – culture and demands are not attractive for young high potential IT workforce
Banks’ IT systems display opportunities for young and ambitious IT workers: they are embedded in huge and well-paying organizations and require plenty of work. While banks communicate externally that they are particularly looking for IT employees with a disruptive mindset the reality is often very different: a highly regulated and risk-aversive culture is skeptical of incrementally built and improved IT solutions. No IT system is released flawlessly today. Systems are optimized, catered towards customer needs, or improved in terms of security standards while they are already in the market. Banks expect a bullet-proof solution from the get-go. In addition, banks are not particularly interested in functionality that does not yet have a clear use case. Industry expertise is needed in combination with data analytics skills to develop promising use cases that appeal to strategy-setting executives. This represents a key to stretch banks’ risk-averse culture and provide young IT employees with interesting challenges.
Value chain positioning – highly-regulated back-end vaults vs. life-fulfillment platforms
Big tech companies are entering the financial services market. While companies like Apple and Google are partially interested in gathering access to spending data via financial products, their main interest is to extend their portfolio by yet another revenue stream. However, because of their data analytics skills and their business model, tech companies can offer a level of convenience and pricing (e.g. freemium) banks are unable to provide. The question is whether banks are willing to play the role of highly regulated institutions that manage the back-end of financial services while tech companies will own the customer relationships?
Tech companies are increasingly becoming targets of supervising and regulatory bodies (especially in Europe) and it is at least unclear whether they are motivated to become as regulated as banks. This represents a competitive advantage for banks that are very familiar to strive in the regulated environment.
Moreover, if banks do want to act proactively defending their customer relationships, data analytics are necessary to design platforms that offer financial services that go beyond today’s banking products. A banking platform should provide internal, external, and integrated financial services that facilitate everyday life (e.g. buying public transportation tickets) or rare life-changing financial decisions (e.g. buying own property). The challenge is that not every bank can turn into a platform, given that platform economics usually represent natural oligopolies.
Data monetization can be direct or indirect – which path to chose?
Direct data monetization refers to trade data in exchange for value, whereas indirect data monetization refers to using data to enable, improve, or maintain revenue streams (without trading data itself). While trading data could be lucrative for banks on a short- to midterm scale, it could also jeopardize their reputation as highly entrusted institutions. Hence, pursuing indirect data monetization by using customer data to design tailor-made solutions seems to be the golden route. However, for services and solutions to be highly relevant in content and timing, banks still have a long way to go.
by Jonas Röttger, FINDER ESR
FINDER 