Marco Bressan: “BBVA’s data-driven transformation is already a reality”
The Group celebrated the second edition of BBVA Brainstorm, an in-company event created to provide all bank employees with a first-hand update on how of the bank’s most strategic projects are progressing and what kind of work is being conducted.
On this occasion, BBVA Chief Data Scientist Marco Bressan served as host of an event intended, mainly, to explain some examples and the advantages, increasingly turning the bank into a “data-driven” institution. “Businesses that work like this make their decisions, design their internal processes and, above all, gauge the experience of its customers based on data. In general, machines make these decisions and learn to adopt them through data,” explained Bressan by way of welcome.
Data will allow us create new opportunities for our customers, adjusted to their needs and circumstances.”
But, what do data-driven companies have in common? BBVA’s data and analytics expert explained their basic characteristics:
- They have a deep understanding of what customers expects from them, and tailor their experiences to these expectations; i.e. they use the data inside each product.
- They understand the trade-off between trust and data, as a key factor in the relationship with customers.
- They use data to inform all their decisions and most of which are adopted automatically.
- Thanks to data, these companies and their employees learn and adapt quickly.
As first example of how vast data sets are handled, Elena Alfaro, CEO of BBVA Data&Analytics, explained how her company, whose team is basically devoted to creating new products and services based on the data mainly obtained from transactional activity. Additionally, these data can be combined with other data mined from external sources (other webpages, social media, etc.) and boiled down to obtain information about the reality of the customers and create products that bring an actual value for them. And here’s where machine learning technologies come in. Offering a service tailored to meet each customer’s needs at any given time is only possible if a machine can infer how each person behaves based on the data available from the.
Alfaro also explained how they came to realize that the transactional data sets the bank already had could be brought to life again; Thus, they decided to offer the bank’s retail customers access to their businesses’ activity data repositories, providing them, also, with the knowledge required to help them make more informed business decisions. The result was Commerce360, a web tool that turns anonymized and aggregate data from purchases paid for by customers with a BBVA card in a physical POS terminal into useful data for business customers. The tool is already available in Spain and Mexico.
Marco Bressa, Chief Data Scientist of BBVA during his speech.
Alejandro Valladares, head of Business Intelligence at BBVA Spain, explained how the department is applying different methodologies across the bank, with very positive results. As times change, so do our customers’ needs and what they expect from us:
- Faster, real-time responses to cover their eventualities or needs
- Flawless customer experience, based on a thorough knowledge of each one of their interactions and contacts, of their internal data
- New external data, whether public or transferred by the customer, complementing the internal vision
In order to deliver the level of service our customers expect and the analytical capabilities they demand, different Group units are undergoing a transformation process, bringing in new talent and skills, building new IT platforms and implementing cutting edge methodologies (agile, project-based organization) to harness the business potential of data.
Success stories
Juanjo Divassón, from Madiva Soluciones, a company that the bank bought two years ago, shared the story behind the development of one of BBVA’s most successful data-based products, in which Madiva collaborated: BBVA Valora, an app that allows anyone to determine whether the asking price a property they intend to buy or sell is fair or not. In this project, the bank put all its technology and data at the service of its customers in order to help them make better decisions.
Similarly, Madiva has collaborated in the renewal of the Home Insurance system deployed in branches. The challenge was to speed up the process, and they managed to do so managing external data. Customers only need to provide their full address to the office manager. Any additional info is provided by the National Cadaster Institute. The rest of the process is simple and quick. These examples confirm that the data-driven bank, the leveraging of data technologies for improving processes and experience, is something that is already here.
To cap off the event, Marco Bressan emphasized the meaning of some terms that are intimately related with the use of huge datasets by companies, such as artificial intelligence: To develop intelligence or, at least, be able to execute cognitive tasks, a machine has to be able to learn: The most efficient way we have developed to develop machines that can learn is by providing them with huge amounts of data. Machine learning is the subfield of computer science that focuses on giving machines the ability to learn. Some of the learning algorithms, such as neural networks or deep learning, are already quite famous. Although these learning techniques, in general, have been around for quite some time, only now has it been possible to feed huge amounts of data to these algorithms.
Bressan concluded the event encouraging BBVA's employees to continue contributing to the construction of a data-driven bank, with the aim of delivering on the promise set out by the bank’s purpose: to bring the age of opportunity to everyone.