Fast forward to 2022 and banking and finance is a very different beast. The rise of online banking and neobanks has led to customers expecting great service at their fingertips, 24 hours a day.
Their experiences with other online services, such as Facebook, Apple, and Google, have set the bar high. Indeed personalisation, through which banks anticipate the needs of their consumers, is now expected.
The ways in which this can be delivered are many and varied. For example, an offer of a first home buyer’s loan could be made to a newly married couple, or an offer for home insurance following a house purchase. In some cases, these offers can be embedded into other apps and delivered directly into the user’s context.
The rise of embedded finance
According to Forrester, embedded finance is the ultimate goal for meeting customer needs when and where they arise, and it is tipped to transform the industry during the next decade. So, for good reason, a focus on the customer is now top of mind (or should be) as open banking takes hold across the industry.
To achieve successful embedded finance, banks need to know more about their customers. Growing customer expectations require expanded data access and collaboration across teams and partners.
As a result, customer 360 initiatives are creating a complete view of customers, with internal data enriched by third-party sources. That means access to external data, either from data marketplaces or via data collaboration with a broader ecosystem of partners. It’s a tall order and requires that financial services firms lift their data game.
The need for data optimisation
Another requirement for a successful embedded-finance project is effective data optimisation. Most companies have recognised the vast potential of their data, however are yet to extract all the value it contains.
To effectively use data, companies need to get their data house in order with an enterprise-wide strategy focused on delivering pan-business insights and driving value creation. That requires investment across the data value chain starting with streamlining, sourcing, and onboarding.
A bank’s data scientists don’t care about where data sits, but rather about the relationship that exists between various data sets. If you cannot bring data together in a meaningful way, you cannot strengthen customer relationships.
At the same time, as more financial institutions focus on protecting digital identities, they need to consider carefully how this added responsibility is going to be undertaken. They need to be aware of the risks involved and the steps that can be taken to ensure their mitigation. Those that have their identity and access mechanisms under control will be well placed to take commercial advantage when their competitors stumble. The time to do this is now. Any delay will risk loss of data, reputation, and customers to a rival.
For these reasons, growing numbers of banks and financial firms are thinking differently and embarking on a technology transformation that delivers a modern data infrastructure. This infrastructure is about enabling freedom to explore and learn from the data within guidelines that prevent catastrophic outcomes, providing a safe environment to explore the art of the possible.
Putting data to work
It is interesting to examine what leaders in the finance sector are already achieving in this area, and there are four types of initiatives worth considering.
- Personalisation: This means responding directly to customer expectations and the need to improve their experience. Banks and insurance providers are creating a complete 360-degree view of customer data across channels, products, and other previously siloed data. With greater visibility into the customer, they can better anticipate needs and deliver highly personalised, timely, and relevant services.
- Regulation and compliance: This area is also top of mind for banks. Since the financial crisis in 2008, regulators have been implementing robust frameworks to monitor market risk and behaviour. Regulators require firms to report key financial metrics in a frequent and timely manner, with each report comprising both internal data and market data in very specific formats. The physical movement of data from bank to regulatory bodies continues to be a source of particular frustration. Data sharing between banks and regulatory bodies will improve the process.
- Quantitative research: On the buy side of the industry, this is another hot area. Buy-side firms are becoming increasingly complex and taking quantitative and research workflows in-house, requiring a greater adoption of enterprise data management solutions. Systematic, rules-based, and automated approaches to data analysis are now the norm rather than the exception.
- The rise of ESG: Fourthly, there are ESG (environmental, social and corporate governance) issues. Climate change, diversity, and human rights as well as business ethics and corporate governance are at the forefront of public and political attention. ESG issues are integral to identifying and properly evaluating investment risks. ESG scores have become as important as traditional financial metrics when evaluating company performance.
It’s easy to see why, with personalisation, regulation, quantitative research, and new workflows, the financial services industry is ripe for cloud transformation. By taking full advantage of the emerging data cloud, banks and other firms will be well placed to meet customer demands and thrive in the years ahead.
Peter O'Connor, vice-president sales APAC, Snowflake
