How to Use Data Analytics to Reduce Your Total Cost of Risk

How to Use Data Analytics to Reduce Your Total Cost of Risk

Know Your Total Cost of Risk

Often, people tend to have a limited view of what surrounds their costs as it relates to insurance and risk management. An organization’s Total Cost of Risk (TCoR) captures and quantifies a variety of factors that impact how much they pay with regard to risk on a holistic basis, including the below direct and indirect costs.

Direct Costs:

  • Premiums
  • Deductibles
  • Medical Costs
  • Legal
  • Uninsured Losses
  • Safety and Claims Management
  • Administrative Expenses

Indirect Costs:

  • Reputational Damage
  • Loss of Use
  • Loss of Productivity
  • Culture and Morale
  • Salary Continuation
  • Overtime
  • Turnover

UNDERSTAND the importance of data

Data can be used to negotiate more favorable terms with insurance companies during the underwriting process and identify areas of loss control and claims management where an organization’s largest return on investment exists.

During the underwriting process, we can use your organization’s data to demonstrate an understanding of how the insurance should be priced by:

  • Developing a story/narrative that advocates for your organization
  • Highlighting historical performance and positive trends
  • Demonstrating a commitment to reducing TCoR by focusing mitigation initiatives on key loss drivers
  • Showing the effects of large “shock” losses that are usually rare
  • Calculating loss development factors
  • Determining appropriate deductible levels that optimize TCoR
  • Determining optimal levels of collateral for deductible, retro factors, and other loss-sensitive programs

Using historical loss data, we can identify:

  • What types of injuries are occurring in your organization
  • What types of occupations are experiencing those injuries
  • The specific locations where those injuries occur
  • Seasonal trends that impact your work environment
  • The types of vehicles and equipment used when an injury occurs
  • The lines of coverage you have in place to transfer the risk of those losses

From a claims management perspective, we can identify similar points of data that give us a good idea of what the behaviors are inside your organization, including:

  • Lag Reporting
  • Litigation Rates
  • Jurisdiction
  • Claim Reporting and Investigation
  • Comorbidities
  • Frequent Claimants
  • Medical vs Indemnity Split
  • Return to Work Programs

These are all highly critical data points that we need to know in order to focus our efforts and limited resources in the right areas. Data is also important because it provides us with benchmarks for historical performance, validates the effectiveness of loss control strategies, projects future performance, and helps us develop and implement loss allocation strategies.

Transform Data to Analytics

By collecting and tracking the right data, we can control our analytics and make the data useful. This includes comparing your loss and incident data mentioned above with exposure data. Exposure data is unique to each organization and usually collected outside of the insurance world. Exposure data types include:

  • Payroll
  • Sales and Revenue
  • Total Insured Value
  • Miles Driven
  • Number of Units Sold
  • Number of Visitors or Customers
  • Number of Deliveries Made
  • Build and Floor design

Exposure data is critical because it gives your organization a true outlook of your performance over time when compared with your loss runs.

Add Value With Analytics & Action Plans

Using data analytics, we can help you form an action plan to balance the insurance piece of your risk management program with the right tension of deductibles and safety resources so that you can ultimately purchase the least amount of insurance possible. In doing so, your organization will be more profitable and efficient, improve employee engagement and productivity and improve overall performance.

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