New Data Report

Your Risk Models Are Punishing Timing, Not Credit Risk

Pave analyzed 360,000+ failed BNPL payments across ~100K borrowers and found that more than 1 in 3 missed payments aren't credit events — they're due dates that landed at the wrong point in a pay cycle.

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7×
Same borrower looks riskier based on payment timing alone
~50%
Of all failures happen in the 3 days before payday
80%
Of pre-payday failures would have cleared on payday
$500
Less annual spend from misaligned accounts
WHAT THE DATA SHOWS

Delinquency signals are distorted by paycheck timing

Traditional underwriting treats every missed payment as a signal of deteriorating credit quality. This study reveals that's often wrong.

Failure rates spike at end of pay cycle

Failure rates start at 1.2% on payday and climb to 8.2% by day 11. The borrower population doesn't change — only where in the cycle the payment lands.

1.2%
→
8.2%
1 in 3 delinquencies aren't credit events

They're due dates that landed at the lowest liquidity point in a predictable income cycle. Most would have cleared just days later when income posted.

33%+
Misalignment costs lenders revenue

Borrowers whose payments are misaligned with payday generate ~$500 less annual spend and complete 12 fewer transactions per year — suppressed activity from avoidable friction.

$500/yr
Balances recover within days

For borrowers who failed pre-payday, cash positions jumped ~$980 when income posted. The average BNPL payment is just $33 — well within post-payday liquidity.

$33
VS
$980
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THE CORE INSIGHT

Running low before payday isn't a sign of credit weakness. It's a feature of getting paid every two weeks.

Treating predictable short-term liquidity gaps as structural credit deterioration creates real costs for both borrowers and lenders — without improving risk outcomes at all. This report shows you exactly where it's happening and what to do about it.

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FROM THE REPORT
1.2%
7x
→
8.2%

Failure rates climb 7× within a single pay cycle — same borrowers, different timing.

The full report breaks down exactly where, why, and what risk teams can do about it — across 360K+ failed payments and ~100K borrowers.

WHAT THE DATA SHOWS

Read the full report

11 pages of data, charts, and policy implications for risk teams rethinking how they classify missed payments.