ABS-EE
Asset-Level Data

Regulation AB-II requires ABS issuers to file loan-by-loan data with the SEC on every asset in the pool. That means individual records for each auto loan: the borrower's credit score at origination, the loan terms, the current payment status, and whether the loan has defaulted. We parse every XML filing, flatten it into a relational schema, and make it queryable across issuers and time.

Scale and Depth

The most granular public dataset in auto lending, parsed and normalized for analysis

9.5M+

Loans Tracked

Individual auto loan records with monthly status updates. Each loan is tracked from securitization through payoff, chargeoff, or pool termination.

20+

Issuers

Every major auto ABS shelf that files ABS-EE with the SEC. Prime, near-prime, and subprime issuers across captive lenders, banks, and specialty finance.

XML

Parsed from Source

Raw SEC XML filings are parsed, validated, and loaded into a normalized relational schema. No manual data entry, no third-party intermediaries.

Monthly Refresh

New asset-level snapshots arrive with each distribution date. Our pipeline detects new filings on EDGAR and processes them within hours.

Sample Fields

Loan-level attributes available for each record in the ABS-EE dataset

Field Description Type
Loan ID Unique identifier assigned to each loan within the trust string
Original Balance Principal balance at the time the loan was securitized currency
Current Balance Outstanding principal as of the reporting period currency
FICO at Origination Borrower's credit score at the time the loan was originated integer
DTI Debt-to-income ratio at origination, measuring borrower leverage percent
LTV Loan-to-value ratio at origination, reflecting collateral coverage percent
Interest Rate Annual coupon rate on the loan percent
Loan Term Original term of the loan in months (typically 36-84) integer
Payment Status Current loan status: Current, 30-day, 60-day, 90+-day delinquent, Default, or Chargeoff enum
Origination Date Date the loan was funded, used for vintage analysis date
Geography (State) U.S. state where the borrower is located, enabling regional risk analysis string

What You Can Build

Loan-level data unlocks analytics that pool-level reports cannot support

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Vintage Loss Curves

Track cumulative net losses by origination quarter. Compare vintages within an issuer or across the market to spot underwriting shifts early.

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Transition Matrices

Build month-over-month state transition probabilities from individual loan histories. See exactly what share of 60-day loans cure versus roll to chargeoff.

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FICO Stratification

Segment performance by credit score band. Measure how deep-subprime borrowers (below 580) perform relative to near-prime (620-680) across issuers.

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Geographic Concentration

Map loan counts and default rates by state. Identify regional pockets of stress before they show up in aggregate pool numbers.

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Term & LTV Analysis

Measure how 72- and 84-month loans perform against shorter terms. Quantify the loss difference between high-LTV and low-LTV originations.

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Issuer Benchmarking

Compare origination quality across issuers on a like-for-like basis: same FICO band, same term, same vintage. Apples-to-apples credit comparison.

Ready to Work with Loan-Level Data?

9.5 million auto loans, parsed from SEC filings, normalized across issuers, updated monthly.

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