Why your credit desk is slow

Credit risk isn’t slow because your model is bad. It’s slow because numbers get retyped multiple times before anyone decides. Clinical studies put manual entry error rates between 0.04% and 6.5% per field, with a well-cited pathology dataset averaging 2.8%-and that’s before those figures get copied into a memo or another system. Multiply that across a day’s tax returns, paystubs, and bank statements and you get a steady leak of mis-spreads, rework, and second-line challenges that no “please be careful” policy can fix. 

The financial drag from bad inputs is real. Gartner estimates poor data quality costs the average organization at least $12.9 million per year. Layer on compliance reality: in EMEA, financial-crime compliance reached $85 billion in 2023 and 98% of institutions said costs rose. When your second line is living that trend, tolerance for “we’ll fix it downstream” drops to zero. Sloppy data isn’t just slow; it’s expensive.

Here’s the twist: the fastest banks didn’t loosen standards; they rebuilt the intake-to-decision flow so data is captured once, validated in-stream, and never retyped. McKinsey documents programs where a digital workflow initially handled ~40% of applications end-to-end and cut “time to yes” from 24–48 hours to about four minutes for straight-through cases-same risk appetite, fewer human handoffs. Other McKinsey work cites leading banks bringing “time to yes” down to around five minutes. 

Meanwhile, onboarding is still a tar pit. Fenergo reports global KYC reviews stretching to ~95 days on average in 2023, up from 84 days the year before. That’s not a “customer patience” problem; it’s an evidence-assembly problem. If analysts are still chasing PDFs by email and reconciling IDs and statements manually, the queue grows and your best people spend their week proving lineage instead of judging risk.

What works in 2025 is simple to describe and hard to skip: capture financials once (documents and bank data), normalize them to the exact chart of accounts your committees use, validate as the data lands, and preserve field-level lineage so audit can click back to source without reopening the case. Do that and underwriting compresses from “days with caveats” to “minutes with confidence,” because the argument shifts to the business story-seasonality, customer concentration, management quality-instead of “where did this EBITDA line come from.”