Concrete, reproducible evidence for quantitative analysis, AI evaluation, workflow automation, SQL/BI and economics.
PD modelling and dynamic-programming FICO segmentation.
Deterministic semantic similarity, keyword coverage and instruction adherence.
Classification, extraction and validation pipeline.
SQLite data model, KPI queries and decision outputs.
Forecasting, holdout validation and scenario analysis.
| Evidence | Result | Status |
|---|---|---|
| Credit-risk portfolio | 50,000 borrowers; 13.344% synthetic default rate | Reproducible |
| PD model | Test AUC 0.700717 | Reproducible |
| FICO segmentation | 8 optimized buckets; monotonic default rates | Reproducible |
| AI evaluation benchmark | 0.500 accuracy against synthetic labels | Benchmark |
| Automation benchmark | 1.000 type accuracy; 1.000 validation pass rate | Benchmark |
Synthetic experiments are explicitly labeled as synthetic. They do not establish production performance, client impact, regulatory validation or commercial outcomes.
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