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Nomis’ Prashant Balepur on bringing data science to bank pricing strategies

  • Banks are getting more strategic about their pricing strategies by using data science and analytics.
  • Prashant Balepur, svp product and marketing at Nomis Solutions, describes the change.
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Nomis’ Prashant Balepur on bringing data science to bank pricing strategies

Before COVID-19 hit, we spent a lot of time talking about the competitive environment for bank deposits. Every week, an 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, we are moving away from one-price-fits-all into a more data-driven and competitive market.

Joining us on the podcast today is Nomis’ Prashant Balepur. We talk about the firm’s new product, a lightweight tool which 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.

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The following excerpts were edited for clarity.

The core pricing problem

I lead product and marketing at Nomis. It's the Greek word for coin. It was founded on a simple premise: that banking and financial services still saw 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.

Banking is 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 a consumer perspective. Then, it's no longer a cost-plus problem -- it's a value-based pricing problem.

Shopping for comparable products, pricing them, comparing them and making a choice -- this has translated to financial services. Now, I can't walk into a branch. Consumers have been trained to expect that from the financial services providers and they need to reflect that in the way they put their offerings and pricing forward.

Pricing strategy adoption

Different markets and segments are at different points. If you think of the deposit side of the house, using data and analytics are being used to think strategically and tactically around pricing. Strategically, the concept is widely adopted, but some firms are very far along the adoption curve.

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 -- also influences.

Nobody asks us anymore whether 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.

Driving pricing strategy

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. It 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 off the bat.

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.

New lightweight product

We just launched a new product, nSight. The core idea 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 on a 36 hour cycle. That's too long. We have shrunk that down to 10 minutes. We have a cohort of early adopter customers loving this. It's a huge shift in the pace and granularity to the information available.

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

Use cases on deposit side

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.

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