AI-powered insurance underwriting portal built natively on Snowflake. Automates end-to-end risk assessment — from raw PDF applications to actuarial profitability analysis — in under 30 seconds.
Underwriters upload PDF applications directly from the UI. The system:
- Parses the document and extracts 44+ structured fields across 7 sections
- Scores the applicant using a trained Snowflake ML regressor (0–20 risk score → LOW / MEDIUM / HIGH)
- Generates a professional underwriter narrative summary
- Runs an actuarial profitability simulation per policy
- Saves the complete record to Snowflake
| Layer | Technology |
|---|---|
| PDF ingestion | Snowflake Internal Stage + CORTEX.AI_PARSE_DOCUMENT (layout-aware OCR) |
| Field extraction | CORTEX.COMPLETE with Mistral Large 2 — structured JSON across 7 sections |
| Risk scoring | UNDERWRITING_SCORE_MODEL — Snowflake ML regressor, 16 clinical & financial features |
| Underwriter summary | Second CORTEX.COMPLETE call — 2–3 sentence professional narrative |
| Actuarial analysis | Python mortality model with US life table multipliers, premium pricing, profitability prediction |
| Conversational analytics | Cortex Analyst + semantic YAML model for natural language portfolio queries |
| Frontend | Streamlit in Snowflake with IBM Plex typography and Plotly dashboards |
Snowflake Cortex · Snowflake ML · Streamlit in Snowflake · Plotly · Python · Mistral Large 2
- Analyse Application — upload a PDF, get a full risk assessment with triggered clinical factors and a visual score gauge
- Application History — portfolio dashboard with BMI vs glucose scatter, risk tier breakdown, and per-applicant detail
- Profitability — actuarial simulation per applicant: estimated life expectancy, annual premium, profit margin, and ACCEPT / REFER / DECLINE recommendation
- Ask Your Data — natural language queries over the portfolio via Cortex Analyst
Requires a Snowflake account with Cortex AI and Snowflake ML enabled.
- Create the database and schema:
UNDERWRITING_DB.UNDERWRITING_SCHEMA - Create an internal stage:
UNDERWRITING_STAGE - Upload the semantic model YAML to a dedicated stage:
YAML_STAGE/underwriting_model.yaml - Train and register
UNDERWRITING_SCORE_MODELusing the Snowflake ML Classification/Regression API - Deploy
app.pyas a Streamlit in Snowflake app