Synthetic Data for Insurance

Finally there is a way to reconcile Big Data innovation with data protection!

As insurance companies continually progress with digital transformation, they are faced with a fundamental conflict. On the one hand, they need to be trusted by their clients to handle some of their most sensitive personal data. On the other hand, existing data protection regulations and other safeguards that guarantee this trustworthiness impose severe limitations on the use of this data. The inability to freely operate with and share data with a wider group of people hampers the adoption of new technologies and poses a significant obstacle for developing next-generation insurance services.

Classic Anonymization Fails for Big Data

Truly anonymous data is exempt from data protection regulations and thus free to use. But in the era of Big Data, classic anonymization does not protect against de-anonymization anymore. Only recently, European researchers demonstrated that 99,98% of individuals could be re-identified in datasets with only 15 demographic attributes. This risk  risk is fully understood within the privacy community but is commonly underestimated by decision makers, who thereby put their companies at financial, regulatory and reputational risk. As even the most sophisticated anonymization methods on the market fall short in the presence of Big Data, a fundamentally new approach is needed!

Synthetic Data for Big Data Anonymization

AI-generated Synthetic Data is a game changer for Big Data anonymization, which allows to retain nearly all of the valuable information in a dataset, while at the same time protecting the privacy of every individual. This is made possible by leveraging state-of-undefinedthe-art generative deep neural networks that can automatically capture the structure and variation of an existing customer dataset. Then, after they were trained, they can be used to generate an unlimited number of highly realistic & representative synthetic customers that match the patterns and behaviors of your actual customers at an unprecedented level.
Synthetic Data is as-good-as-real but yet completely anonymous, allowing you to transform your privacy-sensitive big data assets into data that is free to use, free to share and free to monetize.

How The Synthetic Data Engine Works

Our Synthetic Data Engine is flexible, easy to use, scales to millions of protected customers and is certified with the European ePrivacy Seal. Since we know that your customers' data is one of your most sensitive assets and that keeping it safe & secure is of utmost importance to you, our software is deployed within your secure environment - either on-premise or in your private cloud. Thereby, we make sure that your privacy-sensitive data never has to leave your organizational boundaries.

The synthetization process consists of two basic steps:
1. Training phase: The engine analyzes the existing data and automatically learns all the structures, correlations and time-dependencies within your customers' behavior.
2. Generation Phase: After the training is completed, you are able to generate an unlimited number of synthetic customers, which almost exactly match the patterns and behaviors of your original customers. An additional benefit is, that this step does not require the availability of the original data anymore, and thus can be executed in any environment at any later stage.
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Top 4 Use Cases for Insurance

Mostly AI's Synthetic Data achieves unparalleled accuracy, retains an unprecedented level of detail and is yet fully anonymous. Thereby, it opens up a whole range of opportunities for your otherwise locked up customer data.

AI Training & Analytics

Advanced analytics, machine learning and artificial intelligence require broad data access, and new emerging tools & infra. Provide data at scale and with peace of mind in non-prod environments to your data scientists and AI engineers alike.

Open Big Data & Innovation

Data is the new oil. So, fuel your innovation by broadly sharing granular level data with researchers, startups and innovators alike, increasing the chances for disrupting breakthroughs.

Predictive Analytics

Up to this point, every predictive target (e.g. customer churn) needed its own model. With Mostly SIMULATE universal consumer behavior predictions are possible, where a multitude of predictive scenarios can be explored easily, which allows you to quickly identify and act upon new business opportunities.

Product Development & UX Design

Digital products only come to life with data. Thus, it is pivotal to provision highly realistic data, in all its diversity, to product owners, developers and designers in order to create innovative digital products and to deliver a relevant user experience to your customers.

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