Why your collections numbers lie.
Every collections director I have spoken with in the last two years has been reporting numbers to the board that flatter reality. Not fraudulently — structurally. This note is the technical dissection of why.

Board packs from lender collections functions read the same everywhere. Roll rate stable. Cure rate stable. Right-party contact rate above 60%. Vulnerable customer flag rate roughly 5%. Every number is inside its historical range and the board approves the deck.
None of these numbers mean what the board thinks they mean. Under Consumer Duty they are actively dangerous. This note is why.
The three structural biases
First, roll rate is measured on the accounts you can see, not the accounts you have already lost. Second, right-party contact is measured on calls made, not calls made at times that actually helped the customer. Third, vulnerable customer flag rate is measured against the agent-detected pool, not the true incidence in your book (which is invariably higher by a factor of 2-3x).
Each of these biases makes the number look better than reality. Under a Consumer Duty conversation with the FCA, that gap is what gets a firm into remediation.
The numbers you should actually be looking at
- Pre-arrears roll rate (rising-risk to 1+, not 1+ to 2+).
- Outcome-quality index per treatment path (understanding, action, harm).
- Vulnerable-customer detection recall against a labelled sample (not agent-flag rate).
- Time-to-first-meaningful-intervention (not time-to-first-call).
- Post-treatment persistence rate (did the arrangement hold, not was it agreed).
What this looks like in practice
A lender that shifted from the classic KPI set to the five above discovered that their true roll-to-2+ rate was 40% higher than the reported number, that half of their 'right-party contacts' were happening at times the customer had explicitly said were unhelpful, and that their vulnerable-customer detection recall against a labelled sample was 22% — not the 82% the agent-flag rate suggested. The remediation programme that followed was significant, painful and ultimately produced a materially stronger collections function and a materially better regulatory relationship.
FAQ
Is this a hypothetical example?
It is composited from multiple real engagements. The pattern is very consistent.
How do we get to the true numbers?
Instrument the outcome capture, run a labelled sample against your vulnerable-customer detection, and re-cut roll rate to include the accounts you already wrote off.
Will the board like this?
Short-term no. Long-term yes — the alternative is finding out from the FCA.
How does AI fit in?
Pre-arrears scoring, treatment routing, vulnerability detection, outcome-quality classification. All designed for Consumer Duty from day one.
Where does the £15k Diagnostic fit?
The Diagnostic includes a collections-KPI audit as a standard component for lender engagements.
Want this rigour applied inside your firm?
Start with the free 5-minute AI Readiness Score, or go straight to the £15k Financial AI Diagnostic — a two-week engagement that produces a costed build plan mapped to your regulator, your stack and your P&L.