The explosion of AI and ML applications in recent times has created high demand for synthetic test data. GenRocket is uniquely poised to provide the volume, variety, and format required by developers creating the next generation of AI and ML platforms for healthcare, insurance, and financial markets. Here are some of the many ways in which the GenRocket synthetic test data platform supports AI and ML testing.

Synthetic Test Data for Anomaly Detection

One area in which AI and ML excels is anomaly detection. AI and ML systems can examine data for patterns and detect anomalies by comparing them to rule-based structures. Such systems are often used in healthcare, health insurance, and financial companies where rule-based systems abound.

GenRocket - Use Cases For Anomaly Detection

Once the system is programmed, testing it requires copious amounts of data. Masked production data, or a synthetic replica of production data,is unsuitable for such testing for several reasons.

GenRocket - Insufficient Data Quality

Insufficient Data Quality

Production data files often contains data that is biased and/or under presents the outliers required to sufficiently train anomaly detection programs to identify those outliers.

GenRocket - Insufficient Data Quantity

Insufficient Data Quantity

There may be insufficient data in the production data file to fully train the model to reach the desired level of accuracy. New records may need to be generated to obtain sufficient volume.

GenRocket - Time Consuming

Time Consuming

Preparing production data, or a synthetic replica of production data to ensure quality, consistency, accuracy, and validity takes time. It’s considered the most laborious part of training data provisioning.

GenRocket - Personally Identifiable Information

Personally Identifiable Information (PII)

Production data files may contain PII, which even when masked, may pose a security risk for the original file information. A rules-based system like the GenRocket platform only requires the data model and never accesses or examines production data.

GenRocket Synthetic Test Data: A Perfect Choice for AI and ML Testing

The GenRocket synthetic test data platform addresses these challenges with ease. Its unique synthetic test data ensures that the appropriate volume, variety, and format can be created for training AI and ML systems.

GenRocket can:

  • Utilize a production data schema to to model, design and deploy synthetic data on-demand with any desired volume.
  • Produce data in record time by using the Partition Engine, a unique method of ensuring the data generation load is spread across multiple servers to rapidly provision millions, even billions of rows of quality training data.
  • Compensate for data lacking from production files, such as outliers, boundary values and negative data, by producing synthetic data according to scenarios, or Test Data Cases, created in the Center of Excellence. Test Data Cases can be created to simulate any desired scenario and ensure that the system’s anomaly detection is working accurately.
  • Maintain data table relationships (referential integrity), including highly complex interdependencies, all within the generated synthetic data.

GenRocket in Action: Fraud Detection Case Study

Recently, GenRocket’s synthetic data platform was put to the test with a fast-paced and complex project. The requesting company, a major software firm, had developed a system of fraud detection to spot tax evasion. They needed billions of rows of data to test the system’s ability to detect anomalies such as missing or invalid tax payments – all within a six-week period!

And, because tax laws are complex, there were plenty of areas in which a clever tax evader could try to fool the system – and GenRocket had to compensate for them all providing synthetic data that would ensure the accuracy and performance of the AI assisted tax fraud detection system built by the software company. at scale

GenRocket not only produced the required volume of synthetic test data by the requested deadline but was able to assist the software company in finding several coding errors that could have resulted in big problems.

Read the full case study, here.

Bring Unlimited, High Quality Synthetic Data to Your AI and ML Project

Are you ready to take your AI and ML project to the next level of accuracy and performance? GenRocket is up to the challenge. Discover how GenRocket synthetic data can bring comprehensive and accelerated training and testing to your AI and ML applications.

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See how GenRocket can solve your toughest test data challenge with quality synthetic data by-design and on-demand