financial-analytics

The Skill converts natural language questions into SQL queries to analyze student financial data. It reads pre-aggregated tuition and payment records, enforces active-student filtering by default, and returns aggregated revenue and balance metrics.

Yes, you can evaluate scholarship distribution and financial aid effectiveness by asking natural language questions. The Skill translates these into SQL to aggregate and analyze financial aid data across active students.

Use the Skill's built-in last_payment_method categorization to group tuition revenue and outstanding balances. It processes pre-aggregated financial data to return payment-method patterns without exposing raw credentials.

No, the tool does not require or expose raw credentials. It reads pre-aggregated financial data and returns aggregate metrics, ensuring sensitive credential information is never exposed during the NLQ-to-SQL analysis.

A key limitation is that the Skill only reads pre-aggregated financial data, so raw transaction-level details may be unavailable. Additionally, it enforces active-student filtering by default, potentially excluding historical or inactive student records.