--- title: "Sample Walkthrough \u00b7 Direct Lender" url: https://lendriskanalytics.com/sample-report-direct.html publisher: LendRisk Analytics published: 2026-04-30 kind: Page description: "A blinded direct-lender portfolio walkthrough. No dealers to blame, so loss attribution comes from the credit box itself: which FICO band, term, LTV, vehicle, affordability, and geography are eating the book. We find the slope, rank the segments, project the covenant breach, and tighten the box that stops it." html: https://lendriskanalytics.com/sample-report-direct.html --- # Sample Walkthrough · Direct Lender Worked example · direct lender A model data report for a lender that funds borrowers directly. Numbers drawn from a synthetic direct-lender dataset. Names and identifying details are invented. How they lend: [**Indirect lender** Dealer attribution](https://lendriskanalytics.com/sample-report.html) [**Direct lender** Borrower segments](https://lendriskanalytics.com/sample-report-direct.html) LendRisk Analytics · Worked Example · Synthetic Data # No dealer to blame, so we read the credit box. A worked example on a synthetic direct-to-borrower book · Every figure is illustrative **Prepared**  LendRisk Analytics **Date**  May 2026 **As-of**  April 30, 2026 **Loans**  2,400 **Line size**  $40M senior warehouse Worked example · Synthetic 2,400 Active loans $38.7M Originated balance 9.6% Cum net loss 62% Loss severity · LGD 561 WA FICO How to read this This is the direct-lender version of the dealer report, for a book that funds borrowers directly. There is no dealer to attribute the loss to, so the attribution comes from the credit box itself: which FICO band, which term, which advance rate, which vehicle, which affordability tier, which geography is running hot. It is here to show what the analysis looks like. The book gets read for the slope the aggregate hides, then every segment is ranked by how much of total loss it actually carries, not how big it is. Same tools, borrower axis instead of dealer axis: the dashboard, the segment loss-driver intelligence, the vintage static pool, the vehicle-to-term matcher, and the covenant projector. 01 · We open the tape On paper, you look completely fine. Tool · Portfolio Dashboard Walking the book Start with the headline numbers. Cumulative net loss running **9.6%**, weighted FICO **561**, and a loss severity (LGD) of **62%**, meaning when a loan goes down the book recovers about 38 cents on the dollar of balance. The NCO covenant cap is 13.5%, so today it sits comfortably under it. On a direct book the loss splits into two pieces worth separating right away: how often loans default (frequency) and how badly they hurt when they do (severity). The aggregate hides which one is moving. That distinction is the whole game on a book with no dealers to point at. *A book is a movie, not a photograph.* The number that matters isn't where you are. It's how fast you're moving, and which segments are pushing. 02 · The slope, not the snapshot Watch the column nobody puts on the dashboard. Tool · Covenant Projector · trend read What I'm pointing at Look at the "Monthly Δ" column. Net loss is not parked at 9.6%, it is climbing **0.65 points every month.** 90+ DPD is climbing 0.30. The level is comfortable. The trajectory is not. That slope is a straight line fit through your last six monthly readings. | Metric | Current | Monthly Δ | Covenant cap | Headroom | Status | |---|---|---|---|---|---| | NCO (annualised) | 9.60% | +0.65 pp | 13.50% | 3.90 pp | ⚠ 6 months | | 90+ DPD | 4.20% | +0.30 pp | 6.00% | 1.80 pp | ⚠ 6 months | | Loss severity (LGD) | 62% | +0.5 pp | n/a | n/a | ⚠ Elevated | | WA FICO | 561 | −1.4 / mo | 545 floor | 16 pts | ✓ Watch | | Advance rate (blended) | 118% LTV | +0.4 pp | n/a | n/a | ⚠ Drifting | Benchmark: Fitch subprime auto ABS, Jan 2026, NCO 9.81% · 60+ DPD 6.65%. At market on the level, above market on the rate of change, and your advance rate is drifting up. At this slope, your book *breaches its NCO cap in six months.* That puts it in October. 03 · Which segments are eating the book No dealers. So we rank the credit box. Tool · Segment Loss-Driver Intelligence The direct-lender version of attribution On an indirect book the attribution would be a dealer scorecard. This book has no dealers, so every loan gets binned across the dimensions the lender actually controls, credit band, term, advance rate, down payment, geography, vehicle, affordability, and rank each segment by its **share of your total loss times how far above the book average it runs.** A small bucket that loses a lot does not get to cry wolf; this is loss you can feel. | Segment | Loans | % of loss | Net loss | Default freq | LGD | Flag | |---|---|---|---|---|---|---| | Credit FICO Book average net loss 9.6% · segment flagged when it both runs ≥1.2× the book and carries ≥3.5% of total loss · LGD = loss on defaulted balance. Reading it back to you Two things jump out. First, your **deep-subprime credit band under 520** is doing the heavy lifting, a quarter of your total loss at 3.1× the book, and it fails on both axes: it defaults more often *and* recovers worse. Second, look at the LGD column on **LTV 145%+ and 13-year-old vehicles**, 71% and 73%. Those segments do not default the most, but when they do you barely recover. That is an advance-rate and collateral problem, not a borrower problem. So your loss has two separate engines: a frequency engine in deep-subprime credit, and a severity engine in thin-equity, old-collateral loans. You fix them with two different levers. Three segments, a fifth of the book. *Fifty-nine percent of the damage.* 04 · The combinations the average hides It's never one box. It's two stacked. Tool · Segment Intelligence · cross-cut Why single dimensions undersell it A single segment understates the risk, because the real damage lives where two bad attributes stack. A 73-month term is elevated. Deep-subprime credit is elevated. Put a 73-month term *on* a sub-520 borrower and the loss is not additive, it compounds. Here are the intersections carrying outsized loss. FICO < 520 × Term 73mo+ 2.6× book 11% of loss 34% default 148 loans ** LTV 145%+ × Vehicle 13yr+ 2.3× book 8% of loss 77% LGD 96 loans ** ZIP Tier 4 × FICO < 540 1.9× book 7% of loss 27% default 171 loans ** Zero down × PTI 20%+ 1.8× book 6% of loss 25% default 134 loans ** The credit box looks fine one rule at a time. *The losses live where two rules overlap.* 05 · When it started The damage has a date stamp. Tool · Vintage Static Pool Is it still happening Knowing which segments is half of it. Knowing when tells you whether your underwriting has already drifted back or is still loose. We line up every origination quarter as its own static pool and age them at the same months on book. | Vintage | Loans | Orig $ | Avg MOB | 60+ DPD | Charged off | Cum net loss | |---|---|---|---|---|---|---| | 2023 Q1 | 438 | $7.0M | 39 | 6.3% | 9.1% | 9.6% | | 2023 Q2 | 441 | $7.1M | 36 | 6.6% | 9.0% | 9.4% | | 2023 Q3 ▲ | 452 | $7.3M | 33 | 9.0% | 11.5% | 12.7% | | 2023 Q4 ▲ | 447 | $7.2M | 30 | 9.4% | 11.0% | 12.1% | | 2024 Q1 | 451 | $7.3M | 27 | 6.1% | 5.4% | 6.3% | | 2024 Q2 | 171 | $2.8M | 24 | 4.9% | 3.2% | 3.5% | ▲ Flagged cohorts: 2023 Q3 and Q4 exceed 12% cumulative loss at month 30-33, about 1.4× the 2023 Q1-Q2 rate at the same seasoning. Common factor: a loosening of the term and advance-rate caps in the back half of 2023. Reading it back to you The break is the **back half of 2023**, when the share of 73-month-plus loans and 145%+ advances spiked in your originations. 2024 is already coming in cleaner, so the box partly self-corrected, but the loose 2023 vintage is still on your book bleeding, and it lines up exactly with the hot segments from the last two sections. 06 · Why those loans default You didn't underwrite a borrower. You underwrote a car. Tool · Vehicle-to-Term Matcher The mechanism behind the severity This explains the LGD column. Your highest-severity segments are **long terms and high advances on old vehicles.** Put the depreciation curve next to the amortization curve and the loss draws itself. Loan balance vs. vehicle value · 73mo+ term, 11-year-old vehicle, 145% advance (illustrative) Loan balance (73mo+ amortization) Vehicle value (depreciation) Where defaults cluster Reading it back to you At a 145% advance you are underwater the day the loan funds. On an 11-year-old vehicle the value falls off a cliff while a 73-month note barely moves early. The negative-equity gap is widest exactly where these loans default, **months 18 to 36.** A borrower who hits a bump there cannot sell or refinance out, the car is worth thousands less than they owe, so you get the keys back and recover 30 cents on the dollar. That is your 71-73% LGD, drawn as a picture. The term outran the metal. *The loan runs to 73; the car was gone by 36.* 07 · Where this ends Now we put it on the calendar. Tool · Covenant Breach Projector The part the bank cares about most Take the slopes measured above and run them to the line. Same math a warehouse bank will run, just earlier, while the inputs can still change. NCO Covenant · Breach projected 6 months Current 9.6% · cap 13.5% · slope +0.65 pp/mo Projected breach: **October 2026** 2 of 6 mo consumed 90+ DPD Trigger · Breach projected 6 months Current 4.2% · trigger 6.0% · slope +0.30 pp/mo Projected breach: **October 2026** 2 of 6 mo consumed FICO Floor · Compliant 16 pt headroom Current 561 · floor 545 · drift −1.4 pts/mo Projected floor: **11 months out** ~3 of 16 pts consumed Post-fix · NCO slope 18+ months After tightening the three credit-box rules: slope +0.23 pp/mo Breach pushed beyond 18-month horizon Line preserved Reading it back to you NCO and 90+ DPD both break in **month six. The same month. October.** A breach trips your cash sweep, the sweep halts new originations, and you lose the line right when you would need it. The fourth card is this same book after we tighten the box. Hold that thought. 08 · The fix Three rules, not a book-wide pullback. Tool · Segment Intelligence + Underwriting policy Why not just stop lending The panic move is to slam the whole credit box shut and watch your volume collapse. You do not need to. Because we attributed the loss to named segments and combinations, you can re-cut the box surgically: tighten the three rules carrying the loss, and keep funding everything else exactly as you do today. Credit-box fix · modelled Three changes, applied to **new originations only**: floor FICO at 540 when term exceeds 72 months, cap LTV at 130% on vehicles aged 10 years or older, and require a minimum down payment when PTI exceeds 20%. Existing paper stays on the book under enhanced monitoring. Everything outside these rules funds untouched, that is roughly 80% of your current volume. +0.65 → +0.23 Monthly NCO slope ~135 bps Loss avoided · new vintages ~$1.7M Covenant headroom restored Reading it back to you Those three rules sit right on top of the hot segments and the worst combinations. They flatten your slope from 0.65 to 0.23 a month, push the breach past the 18-month horizon, and cut roughly 135 basis points of expected loss out of your new vintages, all while leaving about 80% of your volume untouched. The green covenant card from the last section is this scenario. Three rules tightened. *The line preserved. Eighty percent of volume untouched.* 09 · What you do Monday Three actions. One is immediate. Output · the page you keep What the analysis points to Here is where the numbers lead. Short enough to push to an underwriting team in a week, documented enough to stand up on a bank's next surveillance call. 1 Immediate · This week Floor FICO at 540 on terms over 72 months The sub-520 × 73mo+ combination is your single worst pocket, 2.6× the book and 34% default. Stop writing that intersection on new applications today. This one rule does most of the work to flatten your slope and remove the October breach risk. Document the change and the data behind it for the bank. 2 30 days · Advance-rate policy Cap LTV at 130% on vehicles 10 years and older This is your severity lever, not your frequency lever. The 145%+ LTV and 13yr+ vehicle segments do not default the most, they hurt the most when they do, at 71-73% LGD. Capping the advance on old collateral keeps you from being underwater on day one, which is the whole reason recoveries are so thin. Pair it with the 60-month term cap on the same vehicle age. 3 Ongoing · Monthly Re-run the segment scan monthly; watch ZIP Tier 4 and zero-down ZIP Tier 4 and zero-down sit above the book but below the urgent threshold. Both bear watching the next two cycles. If either crosses 1.8× the book on rising volume, add a down-payment or geography overlay. A monthly segment refresh catches a drifting box before it reaches the aggregate, which is the whole point: you find it here, not on the bank's call. **Method & lineage.** Net loss = (charge-off − recovery) ÷ originated balance, computed per loan and binned across credit, term, LTV, vehicle type, vehicle age, mileage, affordability (PTI), down payment, income type, and geography (ZIP tier). A segment is flagged when it loses materially faster than the book average *and* carries a real share of total loss, so a tiny high-loss bucket does not cry wolf. Combinations cross two dimensions to surface concentrations the single-axis view misses. Default frequency = charge-off plus repo over the segment; LGD = net loss over defaulted balance. Covenant slope is a linear fit over the prior 6 months of monthly NCO readings. The balance-versus-value chart in section 06 is illustrative of the structure, not a per-loan plot. This was prepared on a synthetic direct-lender book for illustration; the method is the same one a risk desk would apply to a live book. LendRisk Analytics Independent market research the research desk LendRisk Analytics · lendriskanalytics.com Worked Example · May 2026 Synthetic data · Illustrative only The point The average hides the answer. On a direct book the loss is never spread evenly. It lives in a few corners of the credit box, where two bad attributes stack, and the book-level number smooths all of it away. Ranking the segments by what they actually cost is what turns a calm aggregate into a decision the underwriting team can act on.