Surface risk signals from public filings automatically
Train models to extract and predict risk signals from SEC filings, earnings calls, and financial news. By treating future risk events as labels, models learn which filing language predicts actual adverse outcomes.
The kinds of questions a model trained on your data can answer.
Benchmark comparisons against frontier models
Fine-tuned Qwen3-32B achieves Brier Skill Score +11.6% with ECE of 0.029 across 6,109 SEC risk queries — 64.7% lower calibration error than GPT-5 (ECE 0.081). The model learns to distinguish boilerplate legal language from meaningful signals preceding adverse outcomes.
Primary write-ups and artifacts for this solution.
Leverage your own raw data or use public sources. No labeling required.