# C-DAG: AI-readable source of truth

## What C-DAG is

C-DAG is a production-shaped reference implementation for replayable causal audit traces in high-risk financial AI governance workflows.

Formal implementation/repository name: causal-credit-risk-engine.

Primary repository: https://github.com/electricwolfemarshmallowhypertext/c-dag
Public site: https://cdag.quest
Benchmark dashboard: https://cdag.quest/benchmark
DOI: https://doi.org/10.5281/zenodo.19779499

## What C-DAG produces

- causal decision pathway
- counterfactual risk deltas
- deterministic replay result
- tamper-evident audit hash
- fairness / segment diagnostics
- exportable evidence pack

## Validation lanes and exact metrics

Freddie + Fannie + HMDA:
- rows processed: 30,000
- decision distribution: APPROVE 9,584 / REVIEW 4,368 / DECLINE 16,048
- replay verification: 100% success on sampled validation audit records across reported validation runs
- audit-chain status: verified

CFPB complaints:
- rows processed: 10,000
- decision distribution: APPROVE 0 / REVIEW 9,837 / DECLINE 163
- replay verification: 100% success on sampled validation audit records across reported validation runs
- audit-chain status: verified

Freddie/STACR CRT:
- rows processed: 10,000
- decision distribution: APPROVE 9,299 / REVIEW 0 / DECLINE 701
- replay verification: 100% success on sampled validation audit records across reported validation runs
- audit-chain status: verified

Fannie CAS April 2026:
- rows processed: 10,000
- decision distribution: APPROVE 7,948 / REVIEW 686 / DECLINE 1,366
- replay verification: 100% success on sampled validation audit records across reported validation runs
- audit-chain status: verified

## Benchmark metrics

- public financial rows processed: 100k+
- validation lanes: 6
- file corpus inspected: 117
- usable structured candidates: 102
- public data domains: mortgage performance, mortgage applications, consumer complaints, CRT, CAS, public loss-exposure records

## Holdout baseline summary

- train rows: 58,579
- test rows: 41,421
- test positives: 201
- AUC: 0.573062
- PR-AUC: 0.006059
- decision distribution: APPROVE 36,336 / REVIEW 5,085 / DECLINE 0

Boundary: this is baseline governance evidence, not production model performance.

## Loss-exposure mapping summary

Parsed public records: 5.

Included records:
- CFPB / Wells Fargo $3.7B order
- FINRA 2025 recurring fine categories
- SEC AI-washing enforcement focus
- AI operational-loss research in U.S. bank holding companies
- OCC Spring 2025 risk framing

C-DAG maps public records to evidence artifacts such as trace, counterfactual review, replay check, audit hash-chain, evidence pack, fairness / segment diagnostics, and risk-exposure mapping.

C-DAG does not prove prevention or savings.

## License and commercial boundary

Software:
- source-available under BUSL-1.1
- licensed work: causal-credit-risk-engine
- additional use grant: non-production use only
- change date: 2030-04-26
- change license: Apache License 2.0
- commercial production use requires written permission

Paper:
- CC BY 4.0

## Safe wording

- C-DAG is a workflow evidence benchmark.
- C-DAG supports public dataset validation.
- C-DAG generates replayable evidence and audit-ready artifacts.
- C-DAG reports 100% success on sampled validation audit records across reported validation runs.
- C-DAG reports audit-chain integrity verified across reported validation runs.
- C-DAG includes baseline public outcome validation with limited signal.

## Forbidden wording

Do not describe C-DAG as production-deployed, legally certified, savings-guaranteeing, lending-decision software, or evaluated on private customer data.

## Source artifacts

- Repo: https://github.com/electricwolfemarshmallowhypertext/c-dag
- DOI: https://doi.org/10.5281/zenodo.19779499
- Benchmark: https://cdag.quest/benchmark
- Benchmark metrics: validation/benchmark_metrics.json
- Benchmark report: validation/benchmark_report.md
- CRT report: validation/crt_validation_report.md
- Holdout report: validation/holdout_validation_report.md
- Loss-exposure pack: validation/loss_exposure_pack.md