How is Synthetic Data Generated?

How is Synthetic Data Generated?

Synthetic data becomes artificially annotated information generated by computer algorithms or simulations. Most of the time synthetic data acts as a substitute when suitable real-world data is not available. For example, to augment a limited machine learning dataset with additional data examples.

What is Synthetic Data?

Synthetic data is artificially generated data, not data from the real world.  This is essential for teams that need to cover specific use cases in their AI projects but cannot find or pay for real data.  While new data generates every year, not all of it is available for various reasons, including privacy issues.  Fortunately, companies can solve this problem by creating synthetic data.  For example, using manually created data ensures adequate training data for underrepresented populations in the data set, while autonomous vehicles can use synthetic data to create unique edge use cases for training autonomous vehicles.  

Synthetic data has many advantages, such as privacy, cost, accuracy, and flexibility, and tools to create synthetic data provide opportunities to expand data access while maintaining data security, ensuring proper representation, and helping to create AI solutions that work for everyone. 

History of Synthetic Data and its initial usage

Synthetic data was initially used in scientific modeling, in physical systems, where running simulations can estimate/calculate/generate data points that are not observed in actual reality, for example, research into synthesis of audio and voice can be traced back to the 1930s and before. Moreover, driven forward by the developments of e.g., the telephone and audio recording. Digitization gave rise to software synthesizers from the 1970s onwards.

In the context of privacy-preserving statistical analysis, in 1993, the idea of original fully synthetic data became created by Rubin. Rubin originally designed this to synthesize the Decennial Census long form responses for the short form households. He then released samples that did not include any actual long form records – in this he preserved anonymity of the household. Later that year, the idea of original partially synthetic data became created by Little. Little used this idea to synthesize the sensitive values on the public use file.

In 1994, Fienberg came up with the idea of critical refinement, in which he used a parametric posterior predictive distribution (instead of a Bayes bootstrap) to do the sampling. Later, other important contributors to the development of synthetic data generation were Trivellore Raghunathan, Jerry Reiter, Donald Rubin, John M. Abowd, and Jim Woodcock. Collectively they came up with a solution for how to treat partially synthetic data with missing data. Similarly, they came up with the technique of Sequential Regression Multivariate Imputation.

Synthetic Data’s Application in Finance


Synthetic data plays an important role in the future of banking. Access to meaningful customer and transaction data is getting more and more restricted. Growing cybersecurity concerns and increasing legislative pressure are just some of the reasons. Business lines work in siloed ways, where data owners and data consumers are separate entities. Legacy systems represent a mounting challenge to data architectures. Customers demand digital personalization and privacy simultaneously. Cybersecurity concerns and full digital transformation have grown critical over the pandemic years. Synthetic data can solve all these issues and more. 

High quality synthetic data is representative and flexible. Generate as much or as little as you need, fix embedded biases and train models with high accuracy. Mostly, AI’s state of the art synthetic data generator handles complex data structures well. Behavioral data, time-series data, transactions data and synthetic text are the highlights. Highly realistic synthetic test data originates directly from databases.


Insurance companies have always been among the most data-savvy innovators, since the ability to calculate risk accurately makes or breaks an insurance provider. Looking in the long term, companies need to adopt sophisticated AI and analytics across their operations while staying compliant with regulations and protecting their customers’ data.

In conclusion, synthetic data is a game-changer in all things data-driven. Synthetic data can improve the performance of your pricing and fraud detection models, improve accuracy and fairness in AI models and unlock data assets hidden by privacy regulations. Realistic synthetic test data can help you serve customers, brokers, and advisors with great applications, tested to perfection with synthetic user stories identical to those in production.

Written by Anyu Mei

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How is Synthetic Data Generated?

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