The insurance industry is uniquely primed for advanced analytics, due to long term, in-depth experience applying statistics to assess risk. 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. Forward-looking insurance companies are embedding analytics into every aspect of their organization, from assessing underwriting risk and optimizing claims management, to detecting fraud, waste, and abuse.
Elder Research has deep experience helping insurance companies assess and identify areas for growth, providing training and support, and delivering actionable insights by deploying effective predictive models and data visualization tools. Some insurance sectors where Elder Research has provided analytics solutions include:
- Life Insurance
- Long-term Disability Insurance
- Long-term Care Insurance
- Property and Casualty Insurance
- Health Insurance (see Healthcare Analytics page)
Elder Research has over 20 years of 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, Elder Research can streamline your insurance claims process while reducing cost, improving resource utilization, and increasing customer satisfaction.
Elder Research enhances the utilization of existing insurance data, as well as incorporating third party data sources to identify new opportunities for underwriting, improving underwriting risk management, and creating more precise analytical predictions.
Insurance FRAUD, WASTE, AND ABUSE ANALYTICS
With a long history of detecting and aiding in the recovery of funds lost to fraud, waste and abuse, Elder Research can help insurance companies identify unusual claims activity and prioritize your fraud prevention and recovery caseload. Learn more about fraud analytics.
Digitizing and transforming unstructured text data into forms suitable for text analytics provides powerful insight that can be leveraged to automate the loan decision process.
Workers Compensation Insurance ANALYTICS
The complexity and volume of claims from an aging workforce, a growing dependency on and inappropriate use of prescription drugs, increasing obesity and other comorbidities, and fraud are key factors driving skyrocketing treatment and lost work costs in workers compensation insurance. Predictive analytics is quickly becoming an essential capability to evaluate risk early and avoid unnecessary expenses. Analytics can proactively direct and prioritize adjuster efforts and cases for utilization reviewers by identifying the risk of subsequent complicating factors and suggesting early interventions to improve outcomes and minimize total claim costs.
There are many opportunities to apply analytics in insurance and reinsurance. Elder Research 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:
Elder Research 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 helping patients and 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 claims management and improved customer experience. Read the Case Study
Improving Claims Approval Speed and Accuracy
Elder 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. Read the Case Study
Detecting Health Insurance Fraud, Waste, and Abuse
Elder Research developed a predictive fraud detection model that scored and ranked Medicare and Medicaid dental insurance claims by risk. The solution generated leads with the highest potential return on investment for investigators and increased the fraud detection rate from 5% to 48% for the top 50 riskiest providers identified by the model. Having explainable scoring was a key component of success, since the model results would be used as evidence to warrant opening an investigation for providers identified as suspicious. Read the Case Study