Products
Celent recognises SunTec: Luminary in Corporate Banking | Strong Functionality in Retail Banking → Read the Report
What Should the Bank Charge and What Will It Actually Make?
9:15 a.m. Monday. Maya has a pricing request on her desk. A prestigious multinational customer wants to consolidate its payments, collections, and liquidity business with the bank across six markets. The relationship is strategically important. The Relationship Manager (RM) wants to win it. The client wants competitive pricing, flexibility, and a proposition tailored to its needs.
Maya has four questions:
As a Transaction Banking Commercial Leader at a global bank, this is Maya’s job. Her mandate sits at the intersection of customer competitiveness and commercial performance. She works with Cash Management teams on pricing strategy, complex deals, discounts and concessions, profitability, and ultimately, whether negotiated pricing translates into realized revenue.
Her KPIs include pricing turnaround, win rates, price realization, relationship profitability, discount leakage, and revenue growth.
For a straightforward product, pricing may seem simple. But corporate cash management rarely stays simple for long.
A large client may use multiple services across markets, with different transaction volumes, currencies, account and liquidity structures, and charging models. The relationship may come with negotiated rates, volume-based discounts, waivers, or other commercial terms. An RM may also want to offer concessions based on the broader relationship with the client.
Maya needs to understand all of this before recommending a price.
She may need to look at:
The information exists. The problem is getting it together quickly enough to make a commercially sound decision.
In many banks, pricing teams still spend significant time pulling information from different systems, spreadsheets, and teams before they can even begin modelling the proposal. And by the time the analysis is complete, the customer may already be waiting for an answer.
For Maya, speed matters. But speed without commercial intelligence can be expensive.
This is where the role of pricing is changing. Maya doesn’t simply need a system that calculates a rate. She needs an intelligent commercial layer that helps her understand what price makes sense for this particular relationship.
Imagine opening the pricing request and immediately seeing the relevant customer economics, historical pricing, comparable deals, and potential profitability of different scenarios.
An AI-assisted recommendation could highlight how similar customers have been priced, identify relevant price benchmarks, and suggest pricing options based on the available data.
Instead of spending days building and comparing scenarios, Maya can get to a commercially informed proposal in hours.
She can ask:
Maya shares her pricing recommendation with the RM. Before it reaches the client, the deal may need to clear several internal checkpoints, including approvals from relevant product teams such as FX, as well as pricing and commercial approvers. Once internally approved, the RM takes the proposal to the client. After several rounds of negotiation, the client accepts. The price is agreed. The deal is won.
But for Maya, two questions matter: Will the bank deliver what it promised? And will the client deliver on its commitments?
The agreement may include preferential rates, volume thresholds, waivers, effective dates, and specific service conditions.
If those commitments sit separately from the pricing decision, they must be translated manually into downstream processes. That is where commercial intent can start getting lost.
A discount agreed during negotiation may not be reflected correctly in billing. A volume-based condition may not be tracked. A new service added to the relationship may not be incorporated into the commercial arrangement. The customer sees one deal. The bank may see several disconnected processes.
For Maya, this is why pricing cannot be viewed in isolation.
The commercial journey extends from:
Every stage affects the value ultimately realized from the relationship.
If pricing is intelligent but billing cannot interpret the agreed terms, the bank may fail to capture the value it negotiated.
If billing is accurate but the customer cannot understand the charges, the RM inherits the problem.
If a customer disputes an invoice because a negotiated condition was missed, the pricing team may once again have to revisit the original agreement.
The price may have been right. The revenue journey wasn’t.
Maya’s challenge isn’t unique to cash management. The same commercial complexity exists across corporate banking businesses. What changes is the product being sold. The underlying revenue questions remain remarkably similar:
An Intelligent Revenue Layer connects these moments.
For Maya, that means being able to move from customer and market intelligence to pricing scenarios, from negotiated terms to execution, and from execution to accurate billing and profitability. It creates continuity across the commercial lifecycle rather than treating pricing, deal management, and billing as separate activities.
Six months later, Maya faces another complex pricing request. The complexity has not changed. The bank’s ability to master it has. Customer and market intelligence is available when she needs it. Pricing scenarios can be simulated quickly. Recommendations help her identify commercially viable options. Negotiated terms are connected to the deal. The team has access to the right context and insights, flattening the learning curve for team members. Downstream teams have greater visibility into what was promised. Billing can apply complex commercial arrangements more consistently.
And when the customer asks, “Why was I charged this?”, the bank has a clear answer.
Maya still makes the decisions. She simply spends less time finding, reconciling, and reconstructing information, and more time applying her commercial judgment.
That changes the economics of pricing itself.
Back to Maya’s Monday morning request. The multinational client wasn’t simply asking for a price. It was asking the bank to design a commercial proposition that reflected its needs, the value of the relationship, and the bank’s own economics.
That requires more than a pricing engine. It requires the bank to connect the decisions made before the deal with what happens after it.
For Maya, the goal isn’t simply to price services better. It is to ensure that every commercial decision—from proposal and negotiation to execution, billing, and post-billing is connected to the value the bank intends to create and capture. That is the promise of an Intelligent Revenue Layer.
Because in corporate banking, winning the deal is only one part of the equation. The real advantage comes from knowing what to offer, pricing it intelligently, delivering what was promised, and realizing its full value.