Synthetic Data

AI teams, data partners, and institutions building finance-domain models

Synthetic Data

Synthetic Data gives teams a finance-specific corpus filtered by language, theme, and optional country so they can evaluate or fine-tune models without relying on raw publisher text as the starting point.

Request Sample Access View Data Layer

Sample-generation workflow

Supporting product proof from the EMAlpha data layer

Model-development data product

Sample-generation workflow

Try EMAlpha's Synthetic Finance Data

Choose the language and theme, generate a structured sample record, and evaluate a finance-specific multilingual corpus designed for model teams.

Training-ready

Language

English (EN)

Theme

Monetary Policy

Country

IN (optional)

Language coverage

EnglishSpanishPortuguese (BR)HindiChineseArabicKoreanRussian

Commercial use

Positioned for model evaluation, fine-tuning, and finance-domain adaptation with licensing options.

Refresh model

Recurring daily or weekly delivery can be paired with event-driven pushes for high-impact releases.

Sample output

JSON record preview

Synthetic

{
  "language": "English (EN)",
  "theme": "Monetary Policy",
  "country": "IN",
  "headline": "Synthetic policy brief captures the inflation, liquidity, and rate-cut debate in India.",
  "summary": "The generated record reframes a finance event into training-friendly structured text for model evaluation and fine-tuning workflows.",
  "tags": [
    "central-bank",
    "rates",
    "inflation",
    "emerging-markets"
  ],
  "training_safe": true
}

The public website should show why the format matters: structured, multilingual, finance-specific output that teams can test before moving into a broader data conversation.

Synthetic Data is best explained through the generate-sample workflow: language and theme filters, structured JSON output, and a clear bridge into licensing for model teams.

Workflow

Choose the language and theme, generate a sample record, inspect the JSON output, and move into sample-pack or licensing conversations if the data fits the model-development workflow.

Step 1

Select the language, theme, and optional country you want to evaluate.

Step 2

Generate a sample finance record and inspect the JSON structure used for model development.

Step 3

Request a larger evaluation pack or delivery plan once the coverage and format are aligned.

Language, theme, and optional country filtering for sample generation

Sample JSON output for quick evaluation before a deeper commercial discussion

Training-friendly multilingual finance dataset design

Commercial licensing and recurring delivery options for model teams