Claims Analytics Helps a Leading Insurance Company Save Millions
Quantzig’s recent claims analytics engagement for a leading insurance company guarantees 85% increase in correct classification of high-risk claims compared to heuristic ways. Claim handling and risk management Many insurance companies are utilizing key data analytics, performance indicators, and an advocacy-based model to successfully manage claims. Sifting through documents manually and priority handling of high-risk claims is […]READ MORE >>
Quantzig’s recent claims analytics engagement for a leading insurance company guarantees 85% increase in correct classification of high-risk claims compared to heuristic ways.
Claim handling and risk management
Many insurance companies are utilizing key data analytics, performance indicators, and an advocacy-based model to successfully manage claims. Sifting through documents manually and priority handling of high-risk claims is very challenging for service providers. The use of the effective analytical solution to identify both high and low-risk claims will help companies to reduce processing costs, identify fraudulent insurance claims, and process claim requests quicker. The claims analytics team at Quantzig offers predictive analytics solutions to the insurance companies to identify potential high-risk claims and subsequently reduce future costs involved with the claims. This engagement has combined decision analysis with logistic regression technique to classify high and low-risk claims. By leveraging these services, insurance companies develop individual logistics regression models for different disabilities and categorize them according to the risks associated with it.
By using logistic regression model, companies are developing effective solutions to identify and manage risks levels of different claims. These models are specifically designed to analyze data sets such as injury dates, nature of the injury, the tenure of employment, payment details, and monthly payments to identify the risk probability threshold value for each model and assess the expected future cost savings at that cutoff value.
Use of predictive analytics tools
Predictive analytics is a statistical and analytical technique that assess the past events to anticipate the future. In the insurance sector, it analyzes historical data such as nature of the injury, treatment, insured data, liability, characteristics of the claimant, attorneys involved, and venue to formulate settlement values for the losses incurred. This tool helps in ranking the priority of the claim process and amount of compensation that is associated with the claim. It removes manual intervention and results in faster claim settlement.
Improving consumer experience
The smart implementation of innovative technology and other analytical tools will improve consumer satisfaction. Big data helps insurance companies improve claim management, customer retention, and channel productivity. Moreover, these technologies can be leveraged to identify the key trends and device appropriate business strategies to expand to larger markets. By increasing the speed and accuracy of settlements, companies can promote consumer confidence and build a stronger reputation in the market.
Outcomes and solutions offered
With hands-on expertise in claims analytics, Quantzig helped a leading insurance company by providing the various variable selection techniques such as PCA, variable clustering along with the strength of prediction tests to identify the top predictors developed individual logistic regression models. Some of the solutions offered are as follows:
- Improved claim examiner efficiency based on highly accurate predictive model and business rule based on solution
- Showed results that among the top three costliest disabilities, 68% higher probability of incurring a high-risk claim was observed in women when compared to men
- Developed an alert tracker tool to flag the high-risk claims based on the predicted probability from the regression model
- Automated the claims classification process to help the client reduce the claims processing time significantly
- Analyzed the extremely high-risk claims from usual and non-risky disabilities to identify the fraudulent claims and flagged the respective characteristics for future enablement
The complete case study on claims analytics helped a leading insurance company identify potential high-risk claims is now available.
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