NVIDIA 发布 Nemotron-Personas-Finance-Japan 合成数据集

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Nemotron-Personas-Finance-Japan: A Synthetic Dataset Capturing Japan's Demographics and Financial Context

Enterprise + Article Published November 9, 2026

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:last-child]:mb-0">How We Built It</strong>","children":[{"id":"data-generation-pipeline","label":"<strong>Data Generation Pipeline</strong>","children":[],"isValid":true,"title":"Data Generation Pipeline"},{"id":"enhanced-cultural-context","label":"<strong>Enhanced Cultural Context</strong>","children":[],"isValid":true,"title":"Enhanced Cultural Context"}],"isValid":true,"title":"How We Built It"},{"id":"private-by-design","label":"<strong>Private By Design</strong>","children":[],"isValid":true,"title":"Private By Design"},{"id":"who-this-data-is-for","label":"<strong>Who This Data Is For</strong>","children":[],"isValid":true,"title":"Who This Data Is For"},{"id":"practical-ai-applications","label":"<strong>Practical AI Applications</strong>","children":[],"isValid":true,"title":"Practical AI Applications"},{"id":"why-it-matters","label":"<strong>Why It Matters</strong>","children":[],"isValid":true,"title":"Why It Matters"},{"id":"pairs-with-nemotron-pii-japan","label":"<strong>Pairs with Nemotron-PII-Japan</strong>","children":[],"isValid":true,"title":"Pairs with Nemotron-PII-Japan"},{"id":"start-building-with-nemotron-personas-finance-japan","label":"<strong>Start Building with Nemotron-Personas-Finance-Japan</strong>","children":[],"isValid":true,"title":"Start Building with Nemotron-Personas-Finance-Japan"}],"classNames":"not-prose hidden lg:block"}"> Open Data Breaking Down Privacy Barriers to Pave the Way for AI in Finance

What’s in the Dataset?

How We Built It Data Generation Pipeline

Enhanced Cultural Context

Private By Design

Who This Data Is For

Practical AI Applications

Why It Matters

Pairs with Nemotron-PII-Japan

Start Building with Nemotron-Personas-Finance-Japan

A compound AI approach to Japanese personas grounded in real-world distributions

Open Data Breaking Down Privacy Barriers to Pave the Way for AI in Finance

In the financial sector, the utilization of real-world data faces significant constraints due to strict privacy regulations, compliance requirements, and the need for advanced domain expertise. In this context, the development of agentic AI in the financial domain requires synthetic data capable of avoiding the risks of using real data while reflecting demographics, household structures, and asset statuses to reproduce diverse, realistic financial scenarios.

To address these challenges, Elith and NVIDIA have released Nemotron-Personas-Finance-Japan, a financial domain extension of the open-source persona dataset "Nemotron-Personas-Japan." To our knowledge, it is the first open persona dataset at million-record scale that pairs Japan’s official statistics with household finance and financial literacy data. Built upon official government statistics, this dataset was developed using NeMo SDG-PGMs, a probabilistic graphical model framework for synthetic data generation (SDG), and NeMo Data Designer, an enterprise synthetic data generation system. Furthermore, to enhance the practical utility of the dataset in financial use cases, it was designed by incorporating insights and feedback provided by leading domestic financial institutions and industry associations, including Sompo Japan, Tokio Marine Holdings, Bank of Fukuoka, Mizuho Financial Group, Meiji Yasuda Life Insurance, and the Financial Data Utilization Association (FDUA).

This dataset expands the environment where Japanese developers and enterprises can develop and evaluate AI tailored to local and industry requirements, without using real customer data. Released under the CC BY 4.0 license, it is available for commercial use. From building conversational agents to customer simulation and model evaluation, it supports practical financial AI development. The Nemotron-Personas collection, including Nemotron-Personas-Finance-Japan, is designed to seamlessly integrate with the user simulation framework NeMo User Sim, enabling instant execution of simulations drawn from diverse distributions consistent with the real world.

How We Built It

Data Generation Pipeline

It was built using NVIDIA’s framework for synthetic data generation, NeMo Data Designer. This compound AI system enables complex Jinja templating, Pydantic validation, structured outputs, automated retries, and support for multiple generation backends. This is the essential toolset required for generating such large-scale synthetic datasets. In addition, the following models were utilized:

Enhanced Cultural Context

Nemotron-Personas-Finance-Japan was designed to align with Japan's official demographic and labor statistics while taking into account key considerations essential for AI training:

Private By Design

Every persona is fully synthetic. No real individuals are represented, and the dataset is designed to reduce privacy risk. Demographics and household finances follow official statistics and public survey data, and names follow real Japanese naming patterns. Personas are built from these distributions, not from records about real people.

Who This Data Is For

Nemotron-Personas-Finance-Japan is built first for teams creating financial AI in Japan, at banks and insurers and at the startups and integrators that serve them. Each persona carries household finances and financial literacy alongside demographics. Teams can use them to build customer-service agents, evaluate sales conversations/marketing initiatives, run user simulations in NeMo User Sim, and red-team customer-facing AI.

The release also serves anyone building for Japanese users. It holds 9M personas, 50% more than Nemotron-Personas-Japan, across 50 fields instead of 22. That gives any Japanese-language project finer slices of the population to train and test on.

Practical AI Applications

The synthetic personas in this dataset can be leveraged for:

Why It Matters

Access to diverse, high-quality training data that reflects real-world populations has long been a challenge in AI development. Enterprise AI development has predominantly relied on private data, creating barriers for researchers, startups, and particularly AI developers in regions with limited available data.

By releasing Nemotron-Personas-Finance-Japan under CC BY 4.0, we provide access to enterprise-grade, high-quality synthetic data, empowering anyone to build culturally grounded AI systems with lower cost and less privacy risk.

Pairs with Nemotron-PII-Japan

Nemotron-PII-Japan launches the same day. It holds 100,000 synthetic Japanese business documents with labeled personal information, for training and testing detection and redaction models. Its documents are built on Nemotron-Personas-Japan. Use Nemotron-Personas-Finance-Japan to simulate customers, and Nemotron-PII-Japan to test how your pipeline protects their data.

Start Building with Nemotron-Personas-Finance-Japan

You can download this dataset using the command below to help develop AI that genuinely understands Japanese culture and language.

from datasets import load_dataset ds = load_dataset("nvidia/Nemotron-Personas-Finance-Japan")

Use cases for building production-ready applications:

From teams building AI in Japan to global developers aiming for broader regional deployment, Nemotron-Personas-Finance-Japan dataset provides the authentic, privacy-conscious foundation required for your applications.

Datasets mentioned in this article 3

nvidia/Nemotron-PII-Japan

Viewer • Updated about 8 hours ago • 100k • 2 • 1

nvidia/Nemotron-Personas-Finance-Japan

Viewer • Updated about 8 hours ago • 1M • 25 • 1

nvidia/Nemotron-Personas-Japan

Viewer • Updated Dec 16, 2025 • 1M • 3.72k • 134

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