Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Hazy synthetic data generation significantly reduced time to prepare, create and share safe data, which in turn increased the throughput of innovation projects per year. Founded in 2017 after spinning out of University College London’s AI department, Hazy won a $1 million innovation prize from Microsoft a year later and is now considered a leading player in synthetic data. Normally this involves splitting the data into a Training Set to train the model and a Test Set to validate the model, in order to avoid overfitting. Hazy uses generative models to understand and extract the signal in your data. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. For these cases, it is essential that queries made on synthetic data retrieve the same number of rows as on the original data. Armando Vieira Data Scientist, Hazy. Mutual Information is not an easy concept to grasp. Hazy for Cross-Silo Analyse data across silos Problem data stuck in different silos (legal, geography, department, data centre, database system) can’t merge and analyse to get cross-silo insight Solution train synthetic data generators at the edge, in each silo sync generators and aggregate synthetic data, with Histogram Similarity is the easiest metric to understand and visualise. \]. 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. Good synthetic data should have a Mutual Information score of no less than 0.5. Hazy. Read about how we reduced time, cost and risk for Nationwide Building Society. Where \( \bar{y} \) is the mean of \( y \). The next figure shows an example of mutual information (symmetric) matrix: When we developed this MI score alongside Nationwide Building Society, we were building on the work of Carnegie Mellon University’s DoppelGANger generator, which looks to make differentially private sequential synthetic data. “Hazy can help accelerate our work with synthetic datasets,” he … Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Even more challenging is the replication of seemingly unique events, like the Covid-19 pandemic, which proves itself a formidable challenge for any generative model. identifiable features are removed or … I recently cohosted a webinar on Smart Synthetic Data with synthetic data generator Hazy’s Harry Keen and Microsoft’s Tom Davis, where we dove into the topic. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. identifiable features are removed or masked) to create brand new hybrid data. Join Hazy, Logic20/20, and Microsoft for our upcoming webinar, Smart Synthetic Data, on October 13th from 10:00 am-11:00 am PST to learn more. The same for Y = 2 bits, so Y (blood pressure) is more informative about skin cancer than X (blood type). The result is more intelligent synthetic data that looks and behaves just like the input data. If, on the other hand, the variable is totally repetitive (always tails or head) each observation will contain zero information. Before then being used to generate statistically equivalent synthetic data. Hazy uses advanced generative models to distill the signal in your data before condensing it back into safe synthetic data. To illustrate Autocorrelation, we consider the following EEG dataset because brainwaves are entirely unique identifiers and thus exceptionally sensitive information. Hazy has 26 repositories available. is the entropy, or information, contained in each variable. Learn more about Hazy synthetic data generation and request a demo at Hazy.com. Synthetic data of good quality should be able to preserve the same order of importance of variables. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. However, their ability to do so was blocked by data access constraints. Hazy synthetic data quality metrics explained By Armando Vieira on 15 Jan 2021. The following table contains hypothetical probabilities of skin cancer for all combinations of X and Y: The question is: how much information does each variable contain and how much information can we get from X, given Y? Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. Hazy is the market-leading synthetic data generator. Synthetic data enables data scientists and developers to train models for projects in areas where big data capability is not available or if it is difficult to access due to its sensitivity. Unlock data for innovation Safe synthetic data can be shared internally with significantly reduced governance and compliance processes allowing you to innovate more rapidly. Hazy is the most advanced and experienced synthetic data company in the world with teammates on three continents. Let’s explore the following example to help explain its meaning. However, some caution is necessary as, in some cases, a few extreme cases may be overwhelmingly important and, if not captured by the generator, could render the synthetic data useless — like rare events for fraud detection or money laundering. Hazy’s synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Today we will explain those metrics that will bring rigour to the discussion on the quality of our synthetic data. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. Hazy helped the Accenture Dock team deliver a major data analytics project for a large financial services customer. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. | Hazy is a synthetic data company. 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. Hazy synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. Sell insights and leverage the value in your data without exposing sensitive information. This is a reimplementation in Python which allows synthetic data to be generated via the method .generate() after the algorithm had been fit to the original data via the method .fit(). Hazy is a synthetic data generation company. Evaluate algorithms, projects and vendors without data governance headaches. Because synthetic data is a relatively new field, many concerns are raised by stakeholders when dealing with it — mainly on quality and safety. It originally span out of UCL just two years ago, but has come a long way since then. 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. Another blogpost will tackle the essential privacy and security questions. In this session, we will introduce some metrics to quantify similarity, quality, and privacy. Read about how we reduced time, cost and risk for Nationwide Building Society by enabling them to generate highly representative synthetic data for transactions. The autocorrelation of a sequence \( y = (y_{1}, y_{2}, … y_{n}) \) is given by: \[ AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2 \]. This Query Quality score is obtained by running a battery of random queries and averaging the ratio of the number of rows retrieved in the original and in the synthetic data. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. How can we be sure the synthetic data is really safe and can’t be reverse engineered to disclose private information. We generate synthetic data for training fraud detection and financial risk models. “Hazy has the potential to transform the way everyone interacts with Microsoft’s cloud technology and unlock huge value for our customers.”, “By 2022, 40% of data used to train AI models will be synthetically generated.”, “At Nationwide, we’re using Hazy to unlock our data for testing and data science in a way that signicantly reduces data leakage risk.”. Redefining the way data is used with Hazy data — safer, faster and more balanced synthetic data for testing, simulation, machine learning & fintech innovation. Access, aggregate and integrate synthetic data from internal and external sources. 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. Using synthetic data, financial firms can increase the speed of innovation while maintaining control of information and avoiding the risk of a data security breach. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. Autocorrelation basically measures how events at time \( X(t) \) are related to events at time \( X(t - \delta) \) where \( \delta \) is a lag parameter. The DoppelGANger generator had hit a 43 percent match, while the Hazy synthetic data generator has so far resulted in an 88 percent match for privacy epsilon of 1. If you are dealing with sequential data, like data that has a time dependency, such as bank transactions, these temporal dependencies must be preserved in the synthetic data as well. Data science and analytics Synthetic data use cases. How do you know that the synthetic data preserves the same richness, correlations and properties of the original data? \[ H(X) – H(X | Y) = 2 – 11/8 = 0.375bits \]. If the synthetic data is of good quality, the performance of the model yp measured by accuracy or AUC, trained on synthetic data versus the one trained on original data, should be very similar. We use advanced AI/ML techniques to generate a new type of smart synthetic data that's both private and safe to work with and good enough to use as a drop in replacement for real world data science workloads. Using synthetic data, financial firms can increase the speed of innovation while maintaining control of information and avoiding the risk of a data security breach. Follow their code on GitHub. This is essential because no customer data is really used, while the curves or patterns of their collective profiles and behaviors are preserved. For instance, in healthcare the order of exams and treatments must be preserved: chemotherapy treatments must follow x-rays, CT scans and other medical analysis in a specific order and timing. Note that the test set should always consist of the original data: P C = Accuracy model trained on synthetic data / Accuracy model trained on original data. If both distributions overlap perfectly this metric is 1, and it’s 0 if no overlap is found. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. Generating Synthetic Sequential Data Using GANs August 4, 2020 by Armando Vieira Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. 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. Synthetic data enables data scientists and developers to train models for projects in areas where big data capability is not available or if it is difficult to access due to its sensitivity. Assuming data is really used, while the curves or patterns of their customer ’ s 0 if no is! And long-range correlations the metric of choice is Autocorrelation with a histogram Similarity is important it. 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