Wells Fargo, the broad-based financial services company enjoys a customer base of around 70 million due to its sheer services. Analytics is obviously a big piece for any financial company, but at Wells Fargo, the importance of analytics is at a different level as it is heavily invested in and aims to achieve, not just vanilla but cutting-edge analytics. “To be able to do analytics at any place, you need to have the right data. The data must be organised in a shape and form that can be meaningfully used and explored to be able to execute analytics,” says Prahalad Thota, Head of Enterprise Analytics & Data Science , Wells Fargo & Company.
There is tons of data pouring in from different financial services and products and there are various channels through which it comes. Hence volumes of data remain unorganised in different places. “The challenge that arose was that all the data was residing at different places,” says Thota. That is when he felt that centralisation of data is
For the past three years, the banking giant has embarked on a journey which they have termed it as ‘Data Management and Insights’, to compile the data into a central enterprise data environment by organising it in 17 domains. For example, the customer data is clubbed in one domain, the risk data in another domain, and so on. “This journey, over the last three years has been ongoing using some of the latest technologies from big data environment to compile the data and then essentially asking, the question of what are the kinds of analytics that we should be using,” points out Thota.
Wells Fargo has different lines of businesses and each of them has its own analytics team. For example, PVSI- payments and virtual channels is a line of business with its own analytics team. Marketing is another, etc. “We have different analytics teams who want to use the available data. Initially, we were trying to organise the data and make sure that it is available for all analytical teams. But beyond each of these teams doing their own analytics, we realised there was a need for creating enterprise analytics automation and that journey started around four years ago,” he informs.
To be able to do analytics at any place, you need to have the right data. The data must be organised in a shape and form that can be meaningfully used and explored to be able to execute analytics
Enterprise analytics is brought in the company firstly to offer better experience and secondly, as there have been lots of advancements in AI and machine learning, Wells Fargo wanted to create a centre of excellence to make sure that it is bringing the “latest and greatest” into the bank. In order to do that, it is looking into the machine learning use cases.
“The first step was to create what we call an Artificial Intelligence Program Bank. It comprised of three different teams that were put together to do this. The first team is the business team, which is part of our innovation team and their mandate was to identify the big use cases that we want to go after and what are the big focus areas, and to figure out the areas that they want to understand and see where they can apply AI and machine learning. The second team was my team, which is all about data and data science. We ensure that we bring the right data, identify the problems, and then make sure that we have the right team members to be able to do the model development. The third team in the group was related to technology. We decided to bring these three groups together, and drive forward the application of AI in the bank,” informs Thota.
The big idea
The company has started investing end-to-end — from the technology stack to people. “We have identified eight ‘big rock areas’ that we want to invest in. From an AI perspective, risk, fraud, collections, deposits, voice of customer, market intelligence, business performance and personalisation are significant areas. We want to make sure that within these, we identify very specific opportunities to go after. For instance, in the area of personalisation, we want to personalise every individual customers’ experience with the bank. Whether you use your mobile app or you visit the bank, we want to make sure that it’s a personalised experience for the customer,” stresses Thota.
This has been a multi-year journey. “This year, we did some proof of concepts and we showed that there is real value in this. I think when we can measure the customer’s satisfaction and deliver more, they are much more engaged with the bank. By mid- 2020, we are going to have much more in-depth set of conversations with our customers. We will have more tenets of conversations that we are going to start with and that will require about 50 to 100 models to be built. Over time, it’s going to be several hundreds of models that will be fed into the system,” he adds.
Revenue generation with data
When asked how data helps in revenue generation, Thota points out that if a customer is happy with the bank’s services, he will stand by it and with time, generate more requirements. So it is more about relation building.
“In terms of revenue generation, personalisation is key. When a new customer of Wells Fargo bank opens an account with a savings product; if they get the experience right, then we will accomplish multiple objectives. Customers will really look at us as a bank of choice; and as their financial needs evolve and emerge, they are more likely to look at the bank for requirements like a credit card or maybe over take a home loan. If we give the right experience from the inception, customers are more likely to choose us for their next set of needs. That will add to the overall lifetime value of the relationship,” explains Thota.
Banks take lessons from other sectors
Banks are generating massive amounts of data and are using top-notch analytics, but do they take learnings from other sectors as well?
Thota answers, “Definitely. Generally, financial services companies are reasonably advanced when you talk about data, as well as analytics. I wouldn’t say we are on the leading edge, but we are pretty advanced. However, in academia, there are several areas where a lot of interesting work is happening, wherein new kinds of algorithms are built to solve certain problems that we may not have seen before. Our focus is on applied research and applied machine learning. We typically look at some of these sources and ask what is going on out there, and we want to bring that into the company. Also, startups are doing a fabulous job.”
Undoubtedly, Wells Fargo is investing heavily in the latest technologies, in all the right processes. But more importantly, it is investing heavily in people.
“Wells Fargo is looking for great talent because talent triumphs over everything,” he adds.
“We want the best talent, both in the US and internationally. On the data science front, we have made a very firm commitment to massively grow our team in India. The journey for us is to make sure that we go to all the top colleges, hire the right talent and give them access to all the tools and also give them the opportunity to work on interesting problems, which are significant. We are hiring great talent in data science, AI and machine learning. They should really take a hard look at Wells Fargo and point out where a lot of interesting things are happening. We want them to be a part of that journey,” he informs.
“For us, it’s not simply about going to campuses and trying to get the talent, but also to try to help these students understand what it is to be part of an organisation like this, with a lot of data and insights, and what it really means,” Thota concludes.