A Business Case for Dynamic Pricing in Banking

Dynamic pricing is evident in almost every industry. Would this pricing strategy work in financial services? What are the benefits and challenges of a dynamic pricing model?

By Ghela Boskovich, Director, Global Strategic Business Development at Zafin and Pascal Bouvier,Venture Partner at Santander InnoVentures

We are all familiar with dynamic pricing in the hospitality, travel and entertainment industries. We may have even encountered dynamic pricing in e-commerce, probably without knowing it. At its core it is flexible pricing based on current market demands, fine tuned by data analytics where sophisticated algorithms account for time of day, supply, demand, competitive offerings and target profit margins. We have either benefited from – or taken a hit by – what we perceived were bargains or price gouging. Booking a flight or hotel room during peak travel periods, purchasing a ticket to a weekend sporting event or even Uber surge pricing are all examples.

But what about dynamic pricing applied to the financial services industry. Can the banks or credit unions apply dynamic pricing in the same manner other industries have applied it – as an intermediating algorithm, matching supply and demand? Prices of financial services take into consideration operational costs, profit margins, and demand, but at its core, financial service or product pricing is predominantly constructed around risk. A loan, an insurance product, a payment service is usually priced based on risk assumptions.

Let’s focus on a borrower who wants a loan from a bank. The bank will assess the risk of that borrower, and will come up with a rate that is directly linked to that risk and to the bank’s cost of capital, which in turn is based on market based rates. The borrower will, in turn, be very price/rate sensitive, shopping for the best available rate. The only variable in the equation is the bank’s margin embedded in the loan’s rate. The floor on that margin has to align with profitability standards and liquidity coverage requirements.

Dynamic Pricing in Lending

So the real question is … can a bank or credit union truly provide dynamic pricing on a product, like a loan, given the constraints of price elasticity? The same can be asked of an alternative lender, although an alternative lender may have more flexibility given the lower amount of regulatory oversight.

We believe the answer is more likely to be no, although a traditional bank may deliver more granular rates based on better underwriting and data analytics.  True dynamic pricing is less likely.

Does this mean that dynamic pricing is an elusive holy grail for financial services incumbents? Not quite, especially if we introduce the added dynamic of client relationships. If our borrower from the above example also has a checking account, a savings account or an insurance policy with the bank, then dynamic pricing can be viewed along two vectors: a) relationship pricing and b) pricing execution.

Note that we are not using the term “product pricing” here since the emphasis is on relationship pricing where the customer’s portfolio of products used becomes the basis for pricing. The overall relationship is taken into consideration, including the operational costs, margins and risk of the consumer’s portfolio with the institution, not just the products in isolation. This requires a shift from being product-focused to being relationship and service-focused.

As for pricing execution, a shift also has to happen – one linked to how technology is architected, deployed and used. One where siloed systems and siloed data analysis are phased out for more holistic approaches. Imagine a system architecture that would allow for dynamically changing pricing individually or in the aggregate based on the entire product usage profile of a consumer. This pricing could be formulated based on internal goals, customer asks and/or competitive offerings, where the lifetime value (LTV) of the customer and the lifetime value to the customer are constantly weighed and optimized without one losing to the other materially. The results of this type of model can be quite powerful.

The benefits of such an approach are obvious and strategic:

• Increased customer loyalty and stickiness
• Enhanced customer experience
• Increased ability to rebundle and cross sell
• Increased contextual awareness of customer needs

The challenges are no less obvious. First, current core systems for traditional financial institutions are silo-based. Secondly, organizational structures are also silo-based. Thirdly, management incentives are also product and silo-based. Finally, and not unsurprisingly, most relationships are also viewed from a silo perspective both on a product and household structure basis (husband, wife, couple, family).

 Customer Lifetime Value

Looking at the concept of consumer lifetime value (CLV) in a little more detail, the tenant that underpins the notion is that “some customers are more equal than others”, and it measures long-term value over a current quarter’s profit. Boiled down to the basics, it takes the present value of the future cash flow (profitability) attributed to a customer during the entire potential relationship with the bank. In other words, the profitability of loyalty.

This equation can be a little misleading, since profitability is measured by past consumption of services (revenue minus operational costs), and value is a forecasting exercise that involves factoring in the declining value of money over time. This makes this calculationsignificantly more vague to measure than past profitability. What it boils down to, though, is how much the bank is willing to pay to acquire the customer against how much the bank is willing to pay to avoid losing the customer. Customer lifetime value (CLV) is really a dollar value of the asset, the customer.

To read the rest of the article on dynamic pricing in banking, go to the complete article here ...

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