Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Let’s explore the following example to help explain its meaning. where \(x\) is the original data and \(\hat{x}\) is the synthetic data. Armando Vieira Data Scientist, Hazy. A further validation of the quality of synthetic data can be obtained by training a specific machine learning model on the synthetic data and test its performance on the original data. As a side note, if X and Y are normal distributions with a correlation of \(\rho\) then the mutual information will be \( –\frac{1}{2}log(1–\rho^2) \) - it grows logarithmically as \(\rho\) approaches 1. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. This dataset contains records of EEG signals from 120 patients over a series of trials. Accenture were aiming to provide an advanced analytics capability. How do you know that the synthetic data preserves the same richness, correlations and properties of the original data? Hazy is the market-leading synthetic data generator. We generate synthetic data for training fraud detection and financial risk models. 2 talking about this. Read about how we reduced time, cost and risk for Nationwide Building Society. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Evaluate algorithms, projects and vendors without data governance headaches. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. We specialise in the financial services data domain. For temporal data, Hazy has a set of other metrics to capture the temporal dependencies on the data that we will discuss in detail in a subsequent post. Hazy is an AI based fintech company that generates smart synthetic data that’s safe to use, and works as a drop in replacement for real data science and analytics workloads. Author of the book "Business Applications of Deep Learning". Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Through the testing presented above, we proved that GANs present as an effective way to address this problem. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. It originally span out of UCL just two years ago, but has come a long way since then. Synthetic sequential data generation is a challenging problem that has not yet been fully solved. \[ H(X) – H(X | Y) = 2 – 11/8 = 0.375bits \]. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. The Hazy team has built a sophisticated synthetic data generator and enterprise platform that helps customers unlock their data’s full potential, increasing the speed at which they are able to innovate, while minimising risk exposure. Information can be counterintuitive. This can carry over to machine learning engineers who can better model for this sort of future-demand scenarios. An enterprise class software platform with a track record of successfully enabling real world enterprise data analytics in production. As can be seen in Figure 4 the data has a complex temporal structure but with strong temporal and spatial correlations that have to be preserved in the synthetic version. That's drop-in compatible with your existing analytics code and workflows. This metric compares the order of feature importance of variables in the same model as trained on the original data and on trained synthetic data. We generate synthetic data for training fraud detection and financial risk models. Hazy generated a synthetic version of their customer’s data that preserved the core signal required for the analytics project. The Mutual Information score is calculated for all possible pairs of variables in the data as the relative change in Mutual Information between the original to the synthetic data: \[ MI_{score} = \sum_{i=1}^{N} \sum_{j=1}^{N} \left[ \frac{ MI(x_{i},x_{j}) } { MI(\hat{x_{i}},\hat{x_{j}}) } \right] We are pleased to be cited as having helped improve on their exceptional work. Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. This unblocked Accenture’s ability to analyse the data and deliver key business insight to their financial services customer. Where \( \bar{y} \) is the mean of \( y \). Most machine learning algorithms are able to rank the variables in that data that are more informative for a specific task. Synthetic data innovation. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. The same for Y = 2 bits, so Y (blood pressure) is more informative about skin cancer than X (blood type). Once you onboard us, you can then spin up as many synthetic data sets as you want which you can then release to your prospects. Zero risk, sample based synthetic data generation to safely share your data. Follow their code on GitHub. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. Follow their code on GitHub. Synthetic data comes with proven data compliance and risk mitigation. 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