Fraud detection and confidentiality systems Testing and training
fraud detection and confidentiality systems are devised using synthetic data. Specific algorithms and generators are designed to create realistic data, which then assists in teaching a system how to react to certain situations or criteria. For example, intrusion detection software is tested using synthetic data. This data is a representation of the authentic data and may include intrusion instances that are not found in the authentic data. The synthetic data allows the software to recognize these situations and react accordingly. If synthetic data was not used, the software would only be trained to react to the situations provided by the authentic data and it may not recognize another type of intrusion. so synthetic data is sometimes used to protect the
privacy and
confidentiality of a dataset. Using synthetic data reduces confidentiality and privacy issues since it holds no personal information and cannot be traced back to any individual. Beyond privacy protection, synthetic data is also being explored for methodological innovation in drug development. For instance, synthetic data may be used to construct synthetic control arms as an alternative to conventional external control arms based on real-world data (RWD) or randomized controlled trials (RCTs). Collectively, regulatory agencies such as the FDA and EMA appear to be at various stages of recognizing and integrating AI-generated synthetic data into their methodologies. While there is growing consensus on the potential of such data to support model development and the broader lifecycle of medicinal products, to date no drug or medical device has been approved using solely or predominantly synthetic data—particularly not as a comparator arm generated entirely via data-driven algorithms. The quality and statistical handling of synthetic data are expected to become more prominent in future regulatory discussions, particularly in contexts such as predictive modeling (e.g., digital twins), where innovative approaches have already been referenced.
Machine learning Synthetic data is increasingly being used for
machine learning applications: a model is trained on a synthetically generated dataset with the intention of
transfer learning to real data. Efforts have been made to enable more
data science experiments via the construction of general-purpose synthetic data generators, such as the Synthetic Data Vault. In general, synthetic data has several natural advantages: • once the synthetic environment is ready, it is fast and cheap to produce as much data as needed; • synthetic data can have perfectly accurate labels, including labeling that may be very expensive or impossible to obtain by hand; • the synthetic environment can be modified to improve the model and training; • synthetic data can be used as a substitute for certain real data segments that contain, e.g., sensitive information. This usage of synthetic data has been proposed for computer vision applications, in particular
object detection, where the synthetic environment is a 3D model of the object, and learning to navigate environments by visual information. In the context of
large language model training, synthetic data generation has become a core component of the post-training pipeline. Techniques such as Self-Instruct, which uses a small seed set of 175 human-written instructions to generate 52,000 synthetic instruction-following examples, and Persona Hub, which generates over one billion synthetic personas for diverse instruction generation, have enabled the creation of large-scale training datasets at a fraction of the cost of human annotation. At the same time, transfer learning remains a nontrivial problem, and synthetic data has not become ubiquitous yet. Research results indicate that adding a small amount of real data significantly improves transfer learning with synthetic data. Advances in
generative adversarial networks (GAN), lead to the natural idea that one can produce data and then use it for training. Since at least 2016, such adversarial training has been successfully used to produce synthetic data of sufficient quality to produce state-of-the-art results in some domains, without even needing to re-mix real data in with the generated synthetic data. ==Examples==