Nomis’ Prashant Balepur chats with Tearsheet’s Zach Miller about why and how banks are applying data science and analytics to their pricing strategies.

This year’s exceptionally volatile market has resulted in an even more competitive environment for bank deposits. Even before the coronavirus hit, it seemed that every week some incumbent or new challenger bank was making high rate offers in order to attract new customers. The dynamic may be different now, but financial institutions are getting increasingly savvy about their pricing strategies. Borrowing from retail, banks are moving away from one-price-fits-all approach to a more data-driven and competitive strategy.

With that in mind, Zach Miller of Tearsheet podcast interviewed Nomis SVP of product and marketing to discuss the firm’s new product, a lightweight tool that provides banks and credit unions real-time pricing intelligence around mortgages and deposits. Prashant describes how many FIs make pricing decisions today, and what type of data science it takes to optimize products across sectors, geographies, and customers.

We wish to thank Zach for his kind permission to reprint these highlights of the interview.

Zach: Hi Prashant. Would you like to give our listeners the elevator pitch?

Prashant: Sure! I lead product and marketing at Nomis. It’s the Greek word for coin. Nomis was founded on a simple premise: that banking and financial services viewed pricing as a cost-plus activity. For lenders, cost is the risk the banks take on. Then you add something to it — that’s your profit and you price to that. 

However, banking is now also a consumer-facing product. When you have this in a competitive dynamic, it’s a consumer good. So, you need to think of this from the consumer’s perspective. Then, it’s no longer a cost-plus problem — it’s a value-based pricing problem.

Zach: Do you think the way consumer goods are priced has influenced how banks price their products?

Prashant: From a consumer’s standpoint, yes. Shopping for comparable products, pricing them, comparing them and making a choice — this has translated to the financial services sector, where it’s now commonplace for consumers to shop for banking in the digital domain. Consumers have been trained to expect more in this area from their financial services providers, and so banks must now plan to reflect that in the way they put their offerings and pricing forward.

Zach: How has this market matured? What have you noticed?

datascience

Prashant: Different markets and segments are at different points along the evolutionary curve. If you think of the deposit side of the house, using data and analytics have been used to think strategically and tactically around pricing. Now, we’re seeing lending adopt similar approaches. Strategically, then, the concept is widely adopted—but some firms are further along the adoption curve than others.

Zach: What do you think explains that?

Prashant: Market dynamics matter. If you take small, concentrated markets like Canada, we see rapid adoption once a couple of the banks and lenders adopt data-driven pricing. The effort a bank would need to put in — given the maturity of the technology and systems — is also a factor.

Zach: Do you find there’s still a lot of market education to be done when you initially talk to a bank?

Prashant: There used to be, but less so today. Nobody asks us anymore whether or why they should implement data-driven pricing. Now, we go straight into the how — what is the best way to approach it and get the most value. The education and evangelism we did for a number of years when we started has shifted as the market has started to acclimatize and adopt the approach.

Zach: Take us through a deployment. How do you address the ‘how’?

Prashant: Pricing is owned in some cases by a function, but in most cases it’s rolled into the head of a product line like mortgages and savings and deposits. In smaller institutions, it might be the CFO or someone in the C suite. It also depends on the end of the problem you’re tackling. For some banks, it’s critical to get the pricing and analytical decision right — that’s the back-office part of the puzzle. In other institutions, it’s important to get the front-line problem right and address that right off the bat.

Zach: Are you seeing a shift in whether this is driven by the front office vs. the back end?

Prashant: Our pedigree continues to be rooted in data science and deep understanding of consumer behavior. That’s where most of our deployments start and they then migrate to execution and, eventually, presentment. More recently, we’ve been starting in the front line and then talking about the science and optimization that’s under the hood.

Zach: How has your product approach evolved to address that shift?

Prashant: We’ve typically been a SaaS model with 3- to 5-year subscriptions. However, we just launched a new product, nSight. The core idea here was to arm our customers with intelligence about markets and customers without taking on the heavy lifting around data, analytics or systems integration. We just launched in beta in the U.S. for two pillar product lines for banks: deposits and mortgages.

On the mortgage side, information for pricing was typically on a 36-hour cycle. That’s too long. We have shrunk that down to real-time updates every 10 minutes. We have a cohort of early adopter customers loving this. It’s a huge shift in the pace and level of granularity of the information available.

In the US, banks price deposits by region. We change state-level pricing and look at it at a geo-level by starting with what customers care about. We boost granularity by a minimum of 20X. This means that banks can get far more surgical with their pricing.

Zach: Can you give me some use cases on the deposits side?

Prashant: Central banks have been cut to almost zero, so rate won’t really move the needle right now. As banks lower rates, they’re thinking about whether they should lower the entire book or be more strategic. If they understand where they can lower it more methodically, they can preserve some of their margin and redeploy it. That’s a core use case. 

Banks are also using our product to get a better handle on where they’re going to end the year given the volatility of the macroeconomic environment.

Zach:Looking into the future, where are you taking the product?

Prashant: Three areas are key for us. First, this level of data-driven decision making has typically been the purview of the larger banks. We want to make it easier for everyone to adopt our approach without investment and total cost of ownership being a major hurdle. Here, we are focused on simplify the entire approach to make it easier to get started.

Secondly, we believe that pricing ultimately is that value exchange rate between the bank or the lender and the customer, so making pricing customer-centric and holistic is critical and crucial. There a lot of things from a product perspective focused on this area. For example, how do I present and execute offers that may not just be about a rate or price in the traditional sense —there may be other incentives that need to be factored in. Banks need a lot of help here in defining a platform that can drive this presentment.

Lastly, the natural shift to digital means pricing needs to be made available in new ways. As that happens, we have customers are asking us to support them by enabling the consumption of their pricing strategy in any channel or format.

To listen to the entire interview, visit the Tearsheet podcast on SoundCloud.