Insurance
Harness data to meet customers’ changing needs while effectively assessing and protecting against new horizons of risk.
Why AI Now?
Getting it right in insurance is harder than ever. The complexity of risk is rising due to climate change, terrorism and cybercrime. Smart homes and autonomous vehicles are creating a complex, new industry dynamics and unprecedented considerations when crafting policies. The analytics behind today’s underwriting, valuation and fraud detection need to be re-invented to be lightning fast, laser accurate and adaptable to changing demands. Failure to deliver means, at best, lower profit and dissatisfied customers. At worst, it exposes insurers to massive losses. RapidMiner enables insurance companies to harness their data to meet customers’ changing needs while effectively assessing and protecting themselves against new horizons of risk.
Insurance Industry Use Cases
Drive Revenue
- Optimize pricing to price sensitivity by geography and individual clients
- Understand customer segments to expand multichannel strategies
- Assess the long-term value of each customer to personalize service
Cut Costs
- Streamline claims processing by automating data-dependent steps
- Use business process mining to find opportunities to be more effective
- Streamline underwriting to achieve real-time speed, cutting costs and delivering better service
Avoid Risks
- Immediately identify fraudulent and unwarranted claims or policy applications
- Reduce risk & ensure compliance with precise and efficient scoring
Highlighted RapidMiner Impact
A European property and casualty provider used data available from aggregators to predict competitors’ pricing, and made adjustments to win more customers.
A life insurance provider was able to stake out a market position as a premium provider based on the accuracy of its conversion predictions.
A vehicle insurance provider analyzed driver log data and created differentiated pricing based on the safety of drivers, increasing profit.
A diversified US insurance company used claims data to determine the best course of action in litigation scenarios, increasing the rate of successful outcomes.
A life insurance provider increased the precision of its actuarial models, better predicting risk for senior policies and increasing long-term profit.
A Medicare auditor determined early indicators of fraud and used them to find bad actors more quickly.
A health insurer identified potential medication safety issues for at-risk patients and reduced unnecessary, excessive and risky opioid prescriptions.
A health reinsurer used predictive scoring to more profitably underwrite special diseases and other risky candidates.
What Our Customers Say
“I am in love with its easy to use interface and the new functionalities being added.”
– Assistant Professor