High Priority
Deploy FinTech Knowledge Graph Protocol (/ai.txt)
Establish a machine-readable inventory of your FinTech platform's core entities, data flows, and compliance documentation specifically for AI agents and financial data crawlers.
Create a text file at /ai.txt with a concise overview of your FinTech services (e.g., payments, lending, wealth management).
Include markdown-style links to critical API documentation, regulatory filings (e.g., SEC, FCA), and high-value whitepapers.
Add a 'Data Governance FAQ' section to address common queries regarding data security, PII handling, and algorithmic transparency.
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High Priority
AI/LLM Financial Data Selective Indexing
Fine-tune which sections of your FinTech platform are accessible to AI crawlers, prioritizing financial reports, market analysis, and product documentation.
User-agent: FinBot Allow: /market-data/ Allow: /regulatory-compliance/ Disallow: /customer-support/internal/
Validate crawler permissions using industry-specific bot simulators (e.g., Bloomberg Terminal API crawler checks, if applicable).
Monitor crawl frequency and data access patterns in server logs to ensure AI agents are accessing approved financial data nodes without compromising sensitive information.
Medium Priority
Structured Data for Financial Entities
Leverage semantic HTML and JSON-LD to help AI scrapers accurately identify and extract financial instruments, transaction types, and regulatory identifiers.
Implement schema.org markup for 'FinancialProduct', 'InvestmentOrDeposit', and 'Transaction' to define key FinTech entities.
Use `<section>` tags with ARIA labels like 'account-summary' or 'loan-origination-details' for distinct functional areas.
Ensure all financial data tables (e.g., historical stock prices, loan amortization schedules) use proper `<thead>`, `<tbody>`, and `<th>` tags for precise data extraction.
High Priority
RAG-Optimized Financial Reporting
Structure your financial reports and analyses so they can be efficiently 'Chucked' and retrieved by Retrieval-Augmented Generation (RAG) pipelines for accurate AI-driven insights.
Segment complex financial narratives (e.g., quarterly earnings reports, risk assessments) into logical chunks of ~500 tokens, each focusing on a specific metric or conclusion.
Within each chunk, explicitly reference the primary financial entity or metric (e.g., 'Q3 2023 Revenue', 'Loan Default Rate') to avoid contextual ambiguity.
Eliminate vague pronouns and replace them with precise financial terms (e.g., 'Net Interest Margin', 'Customer Acquisition Cost') to ensure clarity for RAG models.
