Predictive Analytics in Insurance-Case Studies
Some case studies of predictive analytics in the insurance industry are pricing, risk selection and product optimization, and fraud detection.
Pricing, Risk Selection & Product Optimization
- With the increase in variety and complexity of data sources, information gathered by insurers becomes more actionable.
- This is because data that is not gathered through outside channels, for example criminal records and credit history, is more direct, and can hence provide valuable insights for insurers.
- Since the improving data insights are majorly comprised of firsthand information, they result in the improvement in pricing and risk selection through predictive analysis.
- Companies are currently in a position to use "pay-as -you-go and dynamic pricing models" based off of the predicted risk of the customers, their buying taste and behavioral signals.
- Predictive analysis algorithms provide insurers with the ability to adjust the quoted premiums dynamically.
- An insurer based in the United Kingdom used telematics to support a large client reduce the risk of accident-causing driving maneuvers by 53%.
- Fraud zaps corporate profits and paddles the price of goods for both business enterprises and consumers.
- Predictive analytics, like entity analytics, is capable of determining who an individual is, and if they are who they claim to be.
- Infinity, a property and casualty company, uses predictive analysis technology to spot "potentially fraudulent claims" and quicken the payment of the legitimate claims.
- After incorporating predictive analysis, the claim fraud system raised the success rate in going after fraudulent claims from 50 — 88%.
- In turn, the amount of time needed to refer the questionable claims for investigative action also reduced by 95%.
In order to come up with the overview of the case studies of predictive analytics in the insurance industry, the research team sourced for the most relevant resources. By so doing, we encountered and obtained older resources, some dating back to 2013. However, we saw it important to obtain information from them due to their deep relevance to the case studies.