The Financial Institution of the future

The Financial Institution of the future

We all know the importance of data-driven personalization and recommendations in the digital economy. In the post-pandemic world, these are rooted in our daily activities from watching a movie to listening to music or making an online purchase. Netflix recommends the next show you are likely to enjoy, Amazon recommends items you will likely need (or not), and Spotify recommends new songs based on what you listen to the most. Data is widely available not only for streaming services and social media companies but for other companies as well, data is evolving and transforming entire verticals. And why should banking not use knowledge about their customers to make suggestions and create personalized journeys.

Historically, financial institutions have been an early adopter of data usage for such things as underwriting of loans and fraud prevention, however, for personalized services leveraging customer data to improve their experience and financial well-being is still in its infancy. Looking to the future, we believe that innovative financial institutions will consistently utilize a variety of data to make recommendations, predictions and provide incentives based on people’s contextual needs. Given the challenges banks across the world are facing today such as high default rates, inflation and increasing competition, it is essential to deeply understand and act upon customer behaviors and habits in order to make the best and timely  recommendations of products or services.

One of the structural changes in regulation enabling this deeper personalization is Open Banking. With Open Banking and embedded finance, institutions can leverage multiple data sources to understand consumers better. In terms of risk and credit worthiness, for example, banks can use this additional data to offer lower interest rates or higher limits on credit card products, but more importantly to create new types of experiences combining alternative data sources and digital footprint. Financial institutions are well positioned to be a hub of consumer insights, but they must act and make the necessary investments before they miss this chance. 

Companies from different industries are investing in these opportunities created by the structural changes that are under way. A few clear examples come from energy companies, retailers and even gas station brands that are now offering financial products and services to their clients. Qista, one of our customers in Brazil, was born as a spin-off from Focus Energia. These companies want to take advantage of 1) financial data being widely available and 2) Banking as a Service providers making it easy to offer banking products, to diversify their revenue streams and compete with traditional banks, keeping the consumer capital “inside their four walls.”

As more players enter the market, it becomes harder for financial institutions to distinguish themselves from the competition. The banking institutions who will flourish are those that offer an incredible and meaningful customer journey that goes beyond being transactional. For us, the answer for this journey is personalization, and it is a lot more than pushing financial products on the consumers, but rather helping individuals thrive and achieve their goals given their unique context, like buying their first car, paying off their loans, investing in themselves or getting married. 

That’s the opportunity we have at Flourish FI, to build the infrastructure and intelligence to provide financial institutions with insights that can be transformed into business results. We started by incentivizing people to build savings habits and nudging them to pay bills on time and adopt digital products; and our vision is to expand the data sources we leverage to build intelligence for institutions while helping people achieve their financial goals. We know Flourish will play a key role in designing the next generation banking products through our customer engagement and personalization engine leveraging both transaction and behavioral data to predict actions, understand people’s context and preferences to make recommendations and predictions.