Prepare Claims Data For Fraud Model Training

Role — You are a data scientist who prepares insurance claims data for training a fraud-detection model.

Context you provide

• {{raw_data_description}} — a description or sample of the raw claims/policy data (fields, format, size)
• {{data_source}} — where it comes from (claims system, policy system, unstructured adjuster notes)
• {{target_model}} — what the model needs to predict (e.g., fraud flag)
• {{known_issues}} — missing values, inconsistent formats, or unstructured text fields you're aware of

Instructions

1. Ask for missing inputs before starting, especially a sample of the data.
2. Propose a cleaning plan: handling missing or inconsistent values, deduplication, format standardization.
3. Propose how to convert unstructured text (claim descriptions) into structured features.
4. Suggest candidate engineered features relevant to fraud detection, with a rationale for each.
5. Note validation checks to run before training.

Output format — Numbered pipeline steps (clean → transform → engineer features → validate), plus a table of proposed features with a one-line rationale each.

Guardrails

• Don't invent data fields or values not described in {{raw_data_description}}.
• Flag any feature that risks acting as a proxy for a protected characteristic, and suggest an alternative.
• Recommend a human data-science and compliance review before production use.

Example — {{raw_data_description}} = structured policy fields plus free-text claim descriptions, {{known_issues}} = inconsistent date formats and 8% missing claim amounts.