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Risk Engineering

Intelligently evaluate and manage property risk

Rawcubes offers customized catastrophe modelling using latest machine learning techniques allowing insurers and reinsurers, financial institutions, corporations, and public agencies to evaluate and manage catastrophe property risk with great level of accuracy.

Let Rawcubes help you in evaluating and managing property risk intelligently

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Construction, Occupancy, Protection and Exposure

The Collection of COPE (Construction, Occupancy, Protection and Exposure) data is essential for organizations to understand an organization’s risk profile and exposure.  Without COPE data, underwriters may need to assume the worst, which could lead to higher insurance premiums for organizations.

Considering huge number of variables involved in COPE, we have developed our own feature selection process using statistical techniques to refine list of consumable variables.

After running complex pre-processing techniques over consumable variables, we execute our own custom weighted multi-model approach, which can enhance our model scoring accuracy. This framework is executed on Spark and HDFS to scale as needed. We provide pluggable interface through REST API for easy client on-boarding.

Benefits of accurate and detailed COPE data

Without COPE data,underwriters may need to assume the worst, which could lead to higher insurance premiums for organizations. Accurate and detailed COPE data owns following critical benefits:
  • Comprehensive picture of building and it’s risk exposure.
  • A more accurate risk assessment through modern machine learning techniques like Gradient Boosting Regressor, Random Forest Regressor etc.
  • Great Leverage in Property Insurance Market.
  •  Accurate Insurance Coverage and Realistic Premiums.
  • Identify conditions that, if corrected, could reduce hazards and improve protection deficiencies.
  • Identify loss exposures.
  • Stay ahead of the competition by offering better pricing for a better risk.