The insurance industries from the history perspective have strong roots in advanced analytics. The essence of insurance industry is the in-depth experience of applying statistics to assess and mitigate the risk. With the market development an advanced analytics are transforming the insurance industry by pairing traditional actuarial methods with new data driven insights to propel companies forward and disrupt the way they do business. Insurance companies are embedding analytics into every aspect of their organization and our company making this task more rational and intuitive on the all level of the business organization.

Simula has deep and rich experience helping insurance companies with assessing and identifying areas for growth, providing training and support, by delivering the actionable insights and deploying effective predictive models with the data visualization tools. Some insurance sectors where Simula has provided analytics solutions include:

  • Long-term Disability Insurance
  • Property and Casualty Insurance
  • Life Insurance
  • Health Insurance

Simula has extensive analytics consulting experience leveraging information about policy holders and claims to help our insurance clients. Examples of our insurance analytics consulting services include:

From insurance claims prioritization and forecasting claims volume, to improving claims approval speed and accuracy, Simula can streamline your insurance claims process while reducing cost, improving resource utilization, and increasing customer satisfaction.

Whether your company is interested in generating actionable insights about customer experience, customer segmentation, customer churn, or new customer acquisition, Simula can support your insurance analytics initiatives to provide your marketing team with the actionable data insight they need to succeed. Usage of advanced analytics in combination with geo-fencing data provided from Simula mobile platform can significantly improve marketing approach and extend the reach of digital aware clients.

Simula enhances the utilization of existing insurance data, as well as incorporating third party data sources to identify new opportunities for underwriting, improving risk management, and creating more precise predictions.

With a long history of detecting and aiding in the recovery of funds lost to fraud, waste and abuse, Simula can help insurance companies identify unusual claims activity and prioritize your fraud prevention and recovery caseload. Usage of advanced cluster algorithms enables to Simula to detect with high level of precision and predict fraud activities within the broad range of categories of clients.


There are numerous opportunities to apply analytics in insurance. Simula has the experience to strengthen and grow your company’s insurance analytics initiatives, whether you are new to analytics or looking to augment existing capabilities. Examples of our insurance solutions include:

Optimize the Management of Long-term Care Claims

Simula provided analytics consulting services to improve the management of long-term care insurance claims by anticipating the implications of changes in patient conditions and expanded care. The client wanted to be more proactive in helping the patients and their caregivers manage these changes. The model predicted escalation in claim invoice amounts months in advance, enabling the client to accurately identify cases likely to benefit from proactive intervention. This led to greater efficiency in claim management and improved customer experience.

Improving Claims Approval Speed and Accuracy

Research combined text mining with traditional statistical techniques to create an analytics solution for ranking disability claims for approval. For the Social Security Administration identifying claims for disability that met the requirements for approval was a time-consuming and error prone process. Some claims were taking over two years to be processed, much too long for very ill or elderly claimants. The challenge was to effectively integrate the unstructured text describing each patient’s symptoms with traditional structured data.