Are We Underpricing or Leaving Money on the Table in Banks & Financial Services?
Direct answer: Most banks and financial services firms leave money on the table not because their headline rates are wrong, but because they misread their own pricing power — the degree to which they can raise fees, spreads, or rates without losing profitable customers. A Pricing Power Analysis diagnoses where you have latitude to charge more (and where you're vulnerable to being undercut) by testing customer switching costs, competitive substitution, and value differentiation across each product line. Run it correctly and you'll usually find a mix: some segments you're underpricing, others where you're already stretched.
Why pricing power is the right lens for financial services
Financial services pricing is deceptively complex. Revenue comes from spreads, fees, and float — often bundled together in ways that hide where the margin actually lives. A bank might feel competitive on deposit rates while quietly over-relying on non-sufficient-funds fees, or price loans aggressively while under-monetizing treasury and cash-management services that clients would never leave over.
Pricing Power Analysis reframes the question. Instead of asking "what does the market charge?" it asks "how much room do we have — segment by segment — to move price before behavior changes?" That's the difference between benchmarking (what everyone else does) and pricing power (what your specific franchise can defend). In a sector where switching a primary checking account or a decade-old commercial banking relationship is genuinely painful for the customer, pricing power is frequently higher than incumbents assume — and they under-earn out of caution.
A concrete Pricing Power Analysis walkthrough for a bank or FS firm
Work through these five steps for each major product line separately (retail deposits, mortgages, commercial lending, wealth management, payments, etc.). Blending them hides the answer.
1. Map switching costs by segment. Ask: How hard is it, concretely, for this customer to leave? A small business with payroll, ACH, and lending all tied to one bank faces high friction. A rate-shopping CD holder faces almost none. High switching cost = latent pricing power you may be giving away.
2. Identify the substitution set. Who actually competes for this customer's dollar — other banks, credit unions, fintechs, direct-indexing platforms, or simply "keep cash in a brokerage sweep"? Fintech substitution is uneven: it's fierce in payments and consumer deposits, weak in relationship-driven commercial banking. Good looks like a clear, honest map of who the customer would realistically move to.
3. Test value differentiation. What does the customer get from you that the substitute doesn't — advisory quality, speed of credit decisions, digital experience, trust, integrated services? If differentiation is real and perceived, you can price above the substitute. If it's only real to you (not perceived by the customer), you can't.
4. Quantify price elasticity where you can. Look at your own historical data: what happened to balances and attrition the last time you moved a fee or a rate? Financial services firms sit on unusually rich behavioral data here — use it before you theorize. Good looks like elasticity estimates grounded in your own book, not industry averages.
5. Locate the money on the table. Cross the four axes: segments with high switching cost + weak substitution + real differentiation + low observed elasticity are almost certainly underpriced. Segments that fail those tests are where discipline (or a graceful exit) matters more than a rate hike.
What "good" looks like at the end: a per-segment pricing power score, a shortlist of two or three defensible price moves, and an equally honest list of segments where you should not push — because in banking, a misjudged fee increase becomes a compliance, reputation, and attrition problem fast.
How Percision helps — and when a spreadsheet or a human is enough
I work on content for Percision, so treat this as one option among several, not the only path.
Percision is an AI strategic intelligence platform that runs your business context through structured reasoning frameworks — Pricing Power Analysis among 27+ — and produces board-ready output in roughly 7–15 minutes rather than an 8–12 week engagement. For a pricing question, it's useful for the structuring work: forcing the segment-by-segment discipline above, stress-testing your assumptions about switching costs and substitution, modeling revenue scenarios, and generating a board deck and an Excel model with an audit trail your finance team can interrogate. It's explicitly a co-pilot, not an autopilot — leadership stays in control of every judgment call.
When Percision is the right fit: you want consulting-grade structure fast, you're running a planning cycle or repricing review, and you have the internal data to feed it. It's strong at turning analysis into an execution plan with KPIs to track.
When it isn't: if your question is a single product's rate versus three named competitors, a well-built spreadsheet and your own portfolio data will answer it faster and cheaper. And for anything touching fair-lending, UDAAP, or regulatory pricing constraints, you need a human — compliance counsel and often a specialist consultant — in the loop. No AI tool should own a regulated pricing decision. Percision informs the strategy; your risk and compliance functions must sign off on execution.
If you want to pressure-test your current pricing across segments and get a structured Pricing Power Analysis you can take to the board, you can try it at Percision.
What this looks like when the analysis is actually run
Underpricing in a bank rarely shows up in the rate sheet. It shows up as a service the bank gives away because it has never been packaged as a product.
The subject is Harborline Financial Group, a sample company profile we use for testing rather than a customer: a $4.2B-asset regional commercial bank, $148M revenue, 38 branches, 620 staff.
Excerpt from a real Percision run · Pricing Strategy (T2) · sample company profile
Where the money is being left. The move directly exploits the uncontested fee-income gap — current 18% versus a peer 28% — while reinforcing the 42-month durability of the commercial-lending node through deeper data integration.
What it would be priced at. Harborline will build a lightweight treasury-management SaaS application that ingests operating-account transaction data, applies cash-flow forecasting models, and surfaces working-capital optimization recommendations. The pilot targets the top 50 commercial borrowers already holding primary operating accounts. The SaaS subscription of $180–$420 per month plus per-transaction fees will generate $180K run-rate revenue by month 9.
The build, costed. $2.4–3.0M development (6 FTE × 24 months) + $0.8–1.2M 2027 core-migration integration = $3.2–4.2M total, funded from the $25–30M three-year retained-earnings envelope with no equity raise. Expected return: Risk/Reward 7.4×; NPV $3.1–4.2M on $3.2–4.2M investment within 5 years.
Scaling assumptions, stated rather than implied. $180K MRR at 50 pilot customers in Year 1; $720K MRR at 200 customers in Year 2; $1.8M MRR at 500 customers in Year 3 — at $180–$420 ARPU, 8% monthly churn and 35% contribution margin.
| Assumption | Probability |
|---|---|
| 50 pilot customers will adopt SaaS at $180–$420/month within 90 days of launch | 0.75 |
| Core processor provides stable API access through 2027 renewal | 0.85 |
| No regulatory open-banking mandate before 2029 that commoditizes cash-flow data | 0.75 |
The pricing answer is not a rate change. The bank is 10 points of fee income below its peers, and the gap is closed by charging for analytics it can already produce from data it already holds — the transaction feed on accounts it already services. The underpricing is of a capability, not of a product.
Note the 8% monthly churn sitting quietly in the assumptions. On a subscription product that is roughly 63% attrition a year, and it is disclosed in the same breath as the $1.8M MRR target rather than buried. A pricing case that shows you its own worst assumption is one you can actually argue with.
Read a complete Percision report — every page, no email required.
FAQ
Q: How is pricing power different from just benchmarking competitor rates? Benchmarking tells you what others charge. Pricing power tells you how much you can charge given your specific switching costs, differentiation, and customer behavior. Two banks with identical published rates can have very different pricing power depending on relationship depth.
Q: Isn't raising fees in banking a regulatory and reputational risk? Yes — which is why the analysis is as much about where not to move price as where you can. Any fee or rate change in a regulated environment should be reviewed by compliance for fair-lending and UDAAP exposure before execution.
Q: Do I need AI for this, or is a spreadsheet fine? For one product against a few competitors, a spreadsheet built on your own portfolio data is often enough. AI helps when you're analyzing many segments at once, want structured framework rigor quickly, or need board-ready output and scenario models without a multi-week engagement.