Finance & Banking

LLM-Ready Financial Data Extraction for AI & Risk Modeling

Normalized transaction data, entity resolution, and alternative data pipelines for fraud detection, credit risk, and financial sentiment AI. Compliant with SEC, MiFID II, and GDPR standards — delivered in the schema your models expect.

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Built for Financial AI Pipelines

Financial AI models are only as good as the data they train on. Inconsistent schemas, duplicate entities, and unmasked PII degrade model accuracy and create compliance exposure.

Transaction Data Normalization

Raw financial transactions standardized to consistent schemas — ISO 4217 currency codes, ISO 8601 timestamps, and normalized merchant categories — ready for fraud detection models.

Financial Entity Resolution

Company names, LEIs, ticker symbols, and counterparty identifiers resolved and deduplicated across data sources to build clean transaction graphs for risk modeling.

Market Sentiment Data

Financial news, earnings call transcripts, and regulatory filings extracted and structured for sentiment analysis and alternative data LLM pipelines.

Regulatory Compliance

Pipelines designed to respect SEC EDGAR, MiFID II, and GDPR data handling requirements. PII masking applied to all customer-level financial records.

Finance AI Use Cases We Power

From fraud detection model training to ESG data feeds and earnings transcript RAG, ScrapeZen delivers structured financial data at the quality your quant and AI teams demand.

Fraud detection model training data
Credit risk scoring data pipelines
ESG (Environmental, Social, Governance) data feeds
Earnings call transcript RAG systems
Regulatory filing analysis (SEC EDGAR, Companies House)
Alternative data for algorithmic trading strategies

// Sample normalized transaction record

{
  "record_type": "transaction",
  "timestamp": "2026-03-15T14:22:31Z",
  "amount": {
    "value": 4850.00,
    "currency": "USD"
  },
  "counterparty": {
    "lei": "5493000IBP32UQZ0KL24",
    "name": "Acme Financial Corp",
    "sector": "GICS:40101015"
  },
  "risk_flags": [],
  "pii_masked": true,
  "compliance": ["MiFID-II", "GDPR"]
}

Financial Data Questions

What financial data sources can ScrapeZen extract from?

ScrapeZen extracts from publicly available financial data sources including SEC EDGAR filings, Companies House, central bank publications, financial news outlets, earnings transcripts, and regulatory databases. We do not extract from closed or subscription-gated financial terminals without an explicit data licensing arrangement.

How does ScrapeZen handle real-time financial data requirements?

For near-real-time financial data feeds, ScrapeZen operates on a Monthly Retainer (DaaS) model with defined delivery cadences — hourly, daily, or weekly. True tick-level real-time market data falls outside our scope, but we excel at structured alternative data and fundamental data pipelines with fresh, regular delivery.

Can the output integrate directly with our existing ML stack?

Yes. Delivered datasets are available as LLM-ready JSON, structured CSV, or via an API endpoint that integrates with standard Python data pipelines (Pandas, Polars), vector databases (Pinecone, Weaviate, pgvector), and LLM orchestration frameworks (LangChain, LlamaIndex).

Ready to Validate Your Finance Data Pipeline?

Request a free Proof of Concept — we'll extract, normalize, and deliver a representative financial dataset sample within 3 to 7 business days.

Request a Free Finance PoC