IBM Synthetic Data Sets is a family of artificially generated, enterprise-grade datasets that enhance predictive artificial intelligence (AI) model training and large language models (LLMs) to benefit IBM Z® and IBM LinuxONE clients, ecosystems, and independent software vendors. These pre-built datasets are downloadable and packaged as comma-separated values (CSVs) and data definition language (DDL) files, making them familiar to use, and compatible with everything from databases to spreadsheets to hardware platforms to standard AI tools. These datasets also leverage the IBM® industry expertise and domain knowledge of the financial services sector without using any real client seed data, which alleviates security concerns with Personally Identifiable Information (PII). Real data at client sites is often limited in scope to only their own organization's transactions, and clients do not always know which transactions are fraudulent or not. To address this scenario, IBM Synthetic Data Sets were modified for fraud detection use cases so that clients can download and enable development of predictive AI models and LLMs for financial services or optimize existing models for improved accuracy and risk mitigation.

The IBM Synthetic Data Sets family contains the following features:

*IBM Synthetic Data Sets for Payment Cards

*IBM Synthetic Data Sets for Core Banking and Money Laundering

*IBM Synthetic Data Sets for Homeowners Insurance

This IBM Redbooks® publication introduces IBM Synthetic Data Sets and provides information about how IBM Synthetic Data Sets can enhance and optimize your predictive AI model training and LLMs.

Executive Overview

Introducing IBM Synthetic Data Sets

Dataset deep dive

Available editions

Previewing data schemas

Using real data versus synthetic data

Data generation methodology

Artificial intelligence ethics

Legal usage terms

Getting started

Frequently asked questions

Additional resources

Appendix: Data schemas for IBM Synthetic Data Sets