Case study

Global automotive company uses Hazy to boost data provisioning


Exactly the same results as real data
20 ×

Faster data provisioning

Requirement for long masking process


With the automotive industry facing significant challenges, the client wanted to improve performance and be able to make faster, more accurate decisions on how credit-worthy potential customers were.

Improving the systems that drive the credit decisioning process as well as improving the algorithms that make those decisions all relies on developers and data scientists using highly sensitive data. This data is often aggregated with third party data that has contractual usage limitations as well.

These factors make it hard to drive rapid improvements and increase revenue.


Hazy’s synthetic credit risk data set preserved the same risk profiles and outcomes as the real data. This allowed thorough, realistic testing of new data pipelines, workflows and processes. By improving the whole credit risk rating system to make it more efficient, synthetic data allows quicker credit risk checks making the customer experience more seamless.

By maintaining the statistical fidelity in the data it also critically allows the team to now explore and build new credit decisioning models without accessing any third party data. These built and tested models can then be run against real data in production.

The whole team across the development and testing community varies in technical skill level. Some members of the team a comfortable with interacting with databases and command lines directly, others prefer to use excel and graphical interfaces to interact with the data.

To integrate synthetic data into production, the team built a Confluence front end interface on top of the synthetic data set in order to allow every type of user to easily access, subset and export the exact data they needed. This was a core part of the project that allowed very quick data provisioning which was unlocked by having pre-certified synthetic data.


Hazy created a synthetic data set for the client that surpassed the useability of its raw data, enabling the business to access real results, safely and quickly.

This new approach has allowed the client to take a giant step forward in how it conducts credit assessments, overcoming the huge restrictions of using real data, while retaining statistical reliability within the synthetic data set.

The success of synthetic data has not only shown how the client’s credit business could be revolutionised, but it has generated intense excitement around how this transformative technology could make a real difference elsewhere in the business.

With their eyes now wide open to the real possibilities of synthetic data, the client is exploring how they can unlock new cases and drive innovative collaboratives with external partners.

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