Use cases: Machine Learning Solutions

Assessing insurance risk depends on a high number of factors which makes insurance data very complex. The complexity is also increased by the numerous different types of insurances. In order to analyze the clients’ profiles, it is crucial for insurance companies to quickly and accurately process and analyze this data, which is a task perfectly suited for Machine Learning models. These models can predict risk and gain, as well as identify any fraudulent activities.

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Decreasing your time to process claims by automation while ensuring fraud detection

Processing claims is a repetitive task and often includes personal or sensitive data. An Information Extraction model can easily find the information relevant in them while based on typical patterns, while a model trained on Anomaly Detection can simultaneously monitor fraudulent activities.

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Assessing risks accurately in underwriting, e.g. by automated satellite imagery for building insurances

Assessing risk is crucial for profitability. Advanced Computer Vision methods can help with accuracy in the assessment. Satellite data is readily available and despite its complexity, Computer Vision models can quickly analyze it. Learning the typical patterns from historical data, the model can assess the risk of natural catastrophes.

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Estimating property damages after natural catastrophes from satellite data

The above mentioned satellite data can also be used to estimate damages once natural catastrophes have occurred. Comparing the current state to patterns learned from historical data, as well as any other relevant factors, a Machine Learning model can do a quick and accurate analysis

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