The Gen AI Frontier

GANs: From basic to advanced Generative Adversarial Networks (GANs) are a machine learning framework designed and introduced in 2014. GANs consist of two neural networks, a generator, and a discriminator, which compete against each other in a zero-sum game. The end goal is to train the generator to produce data so accurately that the discriminator can no longer identify it as fake. Once sufficiently trained, the generator can synthesize accurate data at scale.

Initial developments in GANs focused on generating images, but the past few years have seen substantial literature around their ability to rate tabular enterprise data. This has opened the door to diverse applications across many industries.

Fractal: Taking GANs one step further

Of course, not all data is static, but it can also include time-series features. So, the second phase included creating synthetic data from real data features such as daily/ weekly/ monthly number of transactions on a credit card or activity on a website. The temporal element we built in the utility can now simultaneously account for both static customer data and variable monthly data.

Working with enterprise data across multiple domains such as consumer banking, retail, insurance, and telecom, Fractal has a rich experience in customer-centric data. Since most data enterprises collect is tabular, we realized that the need to generate synthetic tabular data outweighs other unstructured data types like images or audio. We divided our approach into four phases and completed the first two. In the first phase, we created a synthetic data generator utility that generates a single data set of static features with no temporal element. This includes anything that does not change over time, such as demographics, gender, or occupation. The output is a synthetic version of static data inputted.

When we started our research, the idea was to address the cost, privacy, and scarcity challenges organizations face with tabular data.

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