Designed to accelerate AI adoption and increase predictive accuracy to drive business innovation and value
Prebuilt synthetic data sets for AI
IBM® Synthetic Data Sets are prebuilt, artificial datasets designed to train predictive AI models and large language models (LLMs) to benefit IBM Z® and LinuxONE enterprises in financial services.
Built with IBM’s financial services expertise, these data sets deliver rich, privacy-compliant data (downloadable in CSV or DDL) for quick, secure, and accurate AI development.
Agent-based model generation methodology is at a statistical population level so no real source data, which can take months accessing, is needed. Datasets are compliant with data privacy regulations because they do not contain any real or anonymized PII because they are artificially generated.
IBM Synthetic Data Sets are based on years of custom inputs and code worked into our agent-based model that other synthetic data generators don’t offer. These datasets retain and accurately reflect real-world complex relationships and constraints that often present challenges when generating data with other synthetic data generators.
Ground truth training data adds annotations regarding information that is known to be true, enhancing AI model accuracy. IBM Synthetic Data Sets has ground truth known, where each transaction is labeled for fraud and money laundering.
Referential integrity refers to the relationship between different tables, and that the connection makes sense, is accurate, consistent and up to date. Referential integrity is found across IBM Synthetic Data Sets but isn’t often found with data that uses standard synthetic data generators.
Agent-based model generation methodology is at a statistical population level so no real source data, which can take months accessing, is needed. Datasets are compliant with data privacy regulations because they do not contain any real or anonymized PII because they are artificially generated.
IBM Synthetic Data Sets are based on years of custom inputs and code worked into our agent-based model that other synthetic data generators don’t offer. These datasets retain and accurately reflect real-world complex relationships and constraints that often present challenges when generating data with other synthetic data generators.
Ground truth training data adds annotations regarding information that is known to be true, enhancing AI model accuracy. IBM Synthetic Data Sets has ground truth known, where each transaction is labeled for fraud and money laundering.
Referential integrity refers to the relationship between different tables, and that the connection makes sense, is accurate, consistent and up to date. Referential integrity is found across IBM Synthetic Data Sets but isn’t often found with data that uses standard synthetic data generators.
Accurate fraud detection keeps customers satisfied and loyal while minimizing financial losses. IBM Synthetic Data Sets for Payments Cards improves fraud protection AI models by providing labeled transaction data.
IBM Synthetic Data Sets for Core Banking and Money Laundering provides labeled data, including global and cash transactions unavailable in real banking data. This helps build stronger antimoney laundering models, reducing risks and false positives, saving investigation time and costs.
Insurers use real claims data but IBM Synthetic Data Sets for Homeowners Insurance adds synthetic “what-if” scenarios that cover diverse claim types and fraud cases. Each claim is labeled for fraud, detection status and reason, providing a rich dataset to train, validate and improve AI models for detecting fraudulent claims.
