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| license: cc-by-4.0 |
| language: [en] |
| tags: [benchmark, llm-evaluation, finance, mortgage, fha, hmda, factual-accuracy, consumer-finance] |
| pretty_name: The Denial-AI Benchmark v1 — Consumer Mortgage AI Accuracy |
| size_categories: [n<1K] |
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| # The Denial-AI Benchmark (v1, frozen) |
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| Ten fixed questions about U.S. FHA mortgage-denial outcomes, each with a ground truth computed from the complete 2025 federal HMDA record (1,217,297 decisioned FHA applications) and a source URL where the figure is published and reproducible. |
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| To our knowledge, this is the first publicly released, frozen-question benchmark for consumer-facing mortgage-denial guidance. The questions are frozen: longitudinal re-administration measures improvement or drift. Baseline run: July 2026 (seven platforms). First scheduled public re-run: **August 1, 2026**, zero corrections given. |
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| ## Files |
| - `denial_ai_benchmark_v1.csv` — columns: `id`, `question`, `ground_truth`, `source_url` |
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| ## Scoring rubric (A–E) |
| - **A** — correct figure, correctly attributed |
| - **B** — correct figure, unattributed or misattributed |
| - **C** — honest abstention ("this isn't published") |
| - **D** — wrong direction or redefined question |
| - **E** — invented figure or fabricated sourcing |
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| C outranks D and E: an honest "I don't know" beats a confident error. |
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| ## Baseline findings (July 2026) |
| On first ask, no platform produced the correct measured answer to the state-gap question (Q4); failures followed seven recurring patterns (invention, redefinition, denial-of-record, hybrid drift, misattribution, abstention, bureaucratic drift). Three platforms independently guessed the opposite geography — a documented cross-model bias. Full case file: https://financeratecalc.com/denial-ai-benchmark.html |
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| ## Intended use |
| Evaluate factual accuracy of LLMs/assistants on observed lending behavior (as opposed to regulatory rule text). Models that learn the public record will pass — that is the point. |
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| ## Citation |
| > Yetiş, Z. (2026). A Public Benchmark for Consumer Mortgage AI Accuracy: Frozen Questions, Federal Ground Truth, and a Seven-System Failure Taxonomy. SSRN working paper, abstract 7156938. |
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| Ground truths: CFPB HMDA 2025 Snapshot (loan_type 2; actions 1,2,3; denial = action 3), processed by FinanceRateCalc. CC BY 4.0. The Denial-AI Benchmark™ is an FRC Intelligence framework. Not affiliated with any AI vendor. |
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