A sales team can do everything right and still lose the deal after the customer says yes.
The proposal is accepted. The buyer is ready. Now finance must gather documents, check references, review financials and decide how much credit to extend. If the process takes too long, the customer may turn to a competitor. If it moves too quickly, the company may accept undue risk.
That tension has become harder to manage. Finance leaders are under pressure to protect cash as borrowing costs, insolvency risk and economic uncertainty weigh on customers. At the same time, sales teams and buyers expect faster decisions and fewer administrative hurdles. Credit approval sits directly between those priorities.
Benchmarking data collected by the American Productivity & Quality Center shows that organizations at the 25th percentile complete credit approval in four calendar days. The median is five days, while organizations at the 75th percentile take six. The measure tracks the full cycle, including weekends, from the start of the customer credit process through approval.
A two-day difference may not sound consequential, but slow approvals can point to unclear requirements, fragmented information, limited staffing or too many handoffs. They can also cost revenue when customers have competing options. Still, speed for its own sake is a poor goal. Instead, an adaptable process is best. A routine request for a modest credit line should not require the same review as a large, international or higher-risk exposure.
The clearest path to process improvement involves separating necessary scrutiny from avoidable delay. That requires risk-based review tracks, clear decision rights and targeted use of people and technology to move routine requests faster while preserving expert judgment where the exposure warrants it.
Create faster paths for routine credit decisions
A single approval path forces finance to choose between two bad options: review every request as though it was unusually risky or move every request quickly and accept more exposure. A tiered process avoids that tradeoff.
Start by defining the information and approvals required for a standard credit request. Then create clear escalation triggers based on the size of the credit line, the customer’s financial position, industry conditions, geography and the company’s existing exposure. A modest request from an established domestic customer should move through a different path than a large request from a new international buyer.
Those distinctions should be designed before applications arrive. Credit analysts should not have to negotiate the process case by case, and sales teams should know when a request is likely to need more time. Clear thresholds also allow senior finance leaders to delegate routine approvals while retaining control over important exceptions.
Find the sources of delay before adding resources
When approval times remain high, more staff or new technology may appear to be the obvious answer. But neither will solve an unclear process.
Finance leaders should first separate the time spent reviewing an application from the time it spends waiting. Common sources of delay include incomplete submissions, repeated requests for information, unclear ownership, approval queues and handoffs between sales, finance, treasury and legal. An application may take six calendar days even though analysts spend only a few hours working on it.
A cleaner intake process can remove much of that waiting. Customers should receive a concise list of requirements at the outset, and sales teams should understand what constitutes a complete application. Documents should flow into one repository rather than arriving through separate email chains. Each request should also have a named owner responsible for moving it through the process.
Once those basics are in place, leaders can determine whether the remaining constraint is capacity, capability or systems and take steps toward improvement with open eyes.
Automate the work that does not require judgment
Credit approval contains plenty of work that technology can perform faster and more consistently than people. Systems can collect documents, extract financial data, calculate ratios, retrieve credit reports, route requests and flag missing information. Rules-based tools can also approve straightforward requests within established limits or send them directly to the appropriate reviewer.
Aritifical intelligence could expand that role by reading financial statements, identifying inconsistencies and highlighting changes in a customer’s risk profile. Used well, these tools can reduce manual work and help experienced analysts focus on the decisions that carry the most exposure.
But these tools should not eliminate judgment from the process. Trade references, disputed credit information, unfamiliar markets and unusual business models often require context that does not fit neatly into a scoring model. Large exposures also warrant the involvement of an experienced person who can challenge the data and consider whether the company could absorb a default.
For CFOs, the key governance question is where automation should make a decision, where it should recommend one and where it should simply organize information for a human reviewer. Those boundaries should reflect the company’s risk appetite rather than the capabilities of a particular tool.
Judge performance by risk and revenue
An enterprise-wide average processing time provides a useful starting point, but it can also conceal more than it reveals. A five-day cycle may include routine approvals completed in two days and complex cases that take two weeks.
Instead, track cycle time by approval tier, customer type, geography and exposure size. Examine the longest-running applications and identify why they exceeded expectations. A small number of excessive delays may create more commercial damage than a modestly high average.
Speed should also be considered alongside bad-debt losses, delinquencies, rework, approval rates and lost or deferred sales. Faster approvals are not an improvement if defaults rise. A conservative process is not performing well if strong customers routinely take their business elsewhere.
