bank fee analyzer

The problem

Manually auditing bank statements for hidden fees and grouped costs is slow and prone to human error. Developers and finance teams need a predictable way to parse transaction data and isolate service charges without building custom regex patterns for every bank.

What it does

• Identifies and extracts bank fees from raw account movement objects.
• Groups fees by concept to provide a structured overview of banking costs.
• Validates tax details, VAT numbers, and IBANs within the financial context.
• Returns standardized JSON responses for easy integration into accounting or auditing pipelines.

Frameworks & tools

Requires an MCP server hosting the bank_fee_analyzer tool. Works with any LLM environment supporting Model Context Protocol (MCP) like Claude Desktop or Cursor.

Why this beats prompting it yourself

General-purpose LLMs often hallucinate or miscalculate financial figures when processing raw statements. This skill enforces a strict data schema, handles offline validation without network latency, and ensures consistent error codes instead of unpredictable conversational failures.

Use cases

• Automate monthly bank fee audits for corporate accounts.
• Verify client IBAN and tax details before generating invoices.
• Vet new vendors by validating VAT and tax registration data.
• Extract hidden service charges from exported bank CSV or JSON data.

Known limitations

The tool is stateless and does not remember context between calls. It requires properly formatted account statement objects to function correctly.