Our customized solution will assist and guide you in evaluating your property risks more accurately.

Rawcubes’ risk engineering solution leverages catastrophe modelling which is created by machine learning techniques to aid insurers, reinsurers, financial institutions, corporations, and public agencies in accurately evaluating and managing property risk.

Automate and integrate your supply chain and manufacturing processes for enhanced productivity

  • Refined Feature Selection

    The process involves shortlisting a list of consumable variables from a vast number falling under Construction, Occupancy, Protection, and Exposure (COPE). This enables you to better understand another company’s risk profile and exposure.
  • Fourfold Benefits of an Accurate COPE Data

    A highly accurate COPE data helps you to: Prevent speculation in offering accurate insurance coverage and offering realistic premiums. Building a comprehensive picture of the building and its exposure to risk. A highly scalable framework. Staying competitive through better pricing.
  • Multi-Model Approach for Enhanced Scoring

    After running pre-processing techniques on the consumable variables, a customized multi-model approach is used to enhance the accuracy of the scores generated. Since it is executed via Spark and HDFS, one can scale whenever needed.

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