Why the aggregate is the wrong unit
In an indirect book, loss attribution naturally starts with the channel: which sources sent the paper that went bad. That is its own method (Note 03). A direct-to-borrower book has no channel axis at all. The underwriting decision belongs to the lender, so attribution has to come from the credit box itself, which FICO band, vehicle type, vehicle age, mileage bucket, advance rate, term, payment burden, and geography are running hot relative to the rest of the book.
A book-level cumulative net loss of, say, 9% is not actionable on its own. It could be a uniformly mediocre book, or a mostly sound book subsidizing three segments running at twice the average. The credit-box response to those two situations is completely different, and the aggregate cannot tell them apart. Segment attribution exists to tell them apart.
Four numbers per segment
The method computes the same four quantities for every segment, on originated balance rather than loan count wherever dollars matter.
Cumulative net loss rate is gross charge-offs minus recoveries, divided by the segment's originated balance. Share of total loss is the segment's net-loss dollars over the whole book's net-loss dollars, the materiality axis. Default frequency, the PD proxy, is charged-off plus repossessed units over all units in the segment. Loss severity, the LGD proxy, is net loss on the defaulted loans divided by those same loans' originated balance. A fifth number, 60+ days past due among still-active accounts, serves as the forward indicator.
The frequency-severity split is the point of the exercise. A segment can carry outsized loss because too many of its loans default, deep credit tiers, stretched payment burdens, or because each default recovers badly, aged luxury collateral, very high mileage. The first is an approval and structure problem; the second is a collateral and advance-rate problem. Same loss dollars, different fix.
The segment grid
Bins are fixed, not data-driven. A segment definition that drifts with the book cannot be tracked across snapshots, so the cut points below stay constant and the book moves through them. These are the exact edges the method uses.
| Dimension | Bin edges, as used |
|---|---|
| Credit (FICO) | <520 · 520-539 · 540-559 · 560-579 · 580-599 · 600-619 · 620+ |
| Vehicle type | as labeled on the tape (economy sedan … luxury SUV) |
| Vehicle age | 0-3 yr · 4-6 yr · 7-9 yr · 10-12 yr · 13 yr+ |
| Mileage | <60k · 60-90k · 90-120k · 120-150k · 150k+ |
| Term | ≤48 mo · 49-60 · 61-66 · 67-72 · 73 mo+ |
| LTV | <100% · 100-114% · 115-129% · 130-144% · 145%+ |
| Affordability (PTI) | <10% · 10-15% · 15-20% · 20%+ |
| Down payment | 0% · 0-5% · 5-10% · 10%+ |
| Income type | as labeled (W-2 · 1099 · cash · fixed) |
| Geography | ZIP subprime tier where present, else state |
The flag line: elevated and material
A segment is flagged only when two conditions hold at once: its loss rate runs at 1.2× the book average or worse, and it carries at least 3.5% of the book's total net loss, with a minimum of 20 loans in the bin. Both conditions matter. Without the materiality bar, a tiny 40-loan bucket at 3× book dominates the list while moving nothing; without the multiple, big segments get flagged just for being big.
Flagged segments are then ranked by an impact score: share of loss × (multiple − 1). That is, roughly, the loss the book would not have taken had the segment performed at book average, expressed in share points. A segment at 1.6× book carrying 21% of loss outranks one at 2.0× carrying 12%, which is the correct ordering for anyone deciding what to tighten first. Above 1.4× book, the flag is treated as severe. At book level, the context bands are: cumulative net loss above 8% reads elevated and above 13% severe; severity above 70% severe; 60+ DPD above 7% severe. In the per-dimension view, a bin above 1.35× book reads hot and below 0.7× reads cool.
| Rule | Threshold, as used |
|---|---|
| Segment flag, elevated | loss rate ≥ 1.2× book |
| Segment flag, material | ≥ 3.5% of total net loss |
| Minimum sample | 20 loans (25 for cross-cuts) |
| Ranking score | share of loss × (multiple − 1) |
| Severe segment | ≥ 1.4× book |
| Cross-cut flag | ≥ 1.5× book and ≥ 3% of loss |
| Bin heat, dimension view | hot > 1.35× · cool < 0.7× |
| Book bands: cum net loss / LGD / 60+ DPD | 8% and 13% / 70% / 7% |
The cross-cuts the aggregate hides
Single-axis attribution has a blind spot: risk factors arrive on the same loan. The method therefore crosses six fixed pairs, credit × term, vehicle age × mileage, vehicle type × vehicle age, geography × credit, LTV × term, and affordability × credit, and flags any intersection at 1.5× book or worse that carries at least 3% of total loss on 25+ loans. This is where the classic concentrations surface: deep-subprime credit written at 73 months and beyond, and old luxury collateral, where frequency and severity go bad together.
Here is a worked read on a fully synthetic direct book, roughly 2,400 loans, cumulative net loss 9.4%, default frequency 14%, severity 58%. The top flagged segments, ranked by the impact score above:
| # | Segment | Cum net loss | × book | Share of loss | Default freq | LGD |
|---|---|---|---|---|---|---|
| 1 | Term · 73 mo+ | 15.0% | 1.6× | 21% | 24% | 64% |
| 2 | Credit · <520 | 18.8% | 2.0× | 12% | 31% | 66% |
| 3 | LTV · 145%+ | 14.1% | 1.5× | 14% | 22% | 63% |
| 4 | Down payment · 0% | 12.2% | 1.3× | 16% | 19% | 61% |
| 5 | Mileage · 150k+ | 13.2% | 1.4× | 9% | 20% | 67% |
The cross-cuts sharpen the same picture. In the worked book, credit <520 × term 73 mo+ runs at 2.6× book, well past either parent bin alone, and vehicle age 10-12 yr × mileage 150k+ at 2.3×, with LGD in the low 70s. Neither intersection is visible in the single-axis table: the loans are spread across four separate rows there, diluted in each.
Where the bin edges come from
The worked numbers on this page are synthetic, but the structure, where the lines are drawn and which direction the flags point, is not guesswork. It is set to match published, public data, none of it loan-level, borrower-level, or vehicle-history data:
- The credit axis uses the 620 subprime line the Federal Reserve Bank of New York Consumer Credit Panel/Equifax uses, and the direction of the credit flags follows its finding that auto delinquency concentrates in below-620 borrowers, auto 90+ day delinquency reached 5.02% in 2025:Q3, the highest since 2020.
- The term axis singles out 73 months and beyond because Experian reports roughly a third of loans now sit in the 73-84-month band, and its data shows subprime delinquency rising fastest there.
- The sector loss level the multiples are measured against tracks the Fitch subprime auto ABS net-loss index and the Federal Reserve's May 2026 FEDS Note on subprime and BHPH lending; the negative-equity pressure behind the LTV axis follows published Edmunds negative-equity data.
In other words: a reader who wants to know whether the 73-month flag or the sub-620 flag is a real phenomenon can check it against the public record, not take this note's word for it. The full list is under Data & sources.
Limits
What this method cannot see is as important as what it can. It does not separate segment from seasoning. Cumulative loss favors older vintages; a segment concentrated in early originations will read hot partly because it is old. A vintage-controlled cut is the check. It does not establish causation. Segments correlate, deep credit tiers also carry longer terms and higher LTVs, so a flagged bin may be carrying a neighbor's risk. The materiality bars hide small, emerging problems by design; a new 30-loan segment at 3× book will not surface until it grows. Recovery lag flatters recent defaults, understating current-period severity. Input quality binds everything: LTV depends on which book value was used at origination, and PTI on stated income. And the method says nothing about what the segment earns, a 1.5× loss segment written at a sufficiently higher yield may still be sound economics. That is a different analysis.