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Key Use Cases of Predictive Analytics for the Insurance Industry

Insurance CIO Outlook | Tuesday, March 30, 2021

Key Use Cases of Predictive Analytics for the Insurance IndustryThis article will find out what predictive analytics is and how it helps the insurance industry.

FREMONT, CA:  Artificial Intelligence helps insurance providers speed up tasks and make smart choices. In reality, using AI in solutions such as predictive analytics would open up new opportunities for innovation in the insurance industry and fundamentally change the way insurance companies evaluate risk.

This article will find out what predictive analytics is and how it helps the insurance industry.

What is Predictive Analytics?

Predictive analysis is the technology used to assimilate data into concrete observations and actionable knowledge. Using machine learning and broad data analysis, it examines historical patterns, builds risk assessment models based on available data, and predicts future events' outcomes.

Simply put, predictive analytics software can gather data to understand better and forecast the actions of the insured. Insurance firms typically collect data from telematics, agent and client contacts, and social media to monitor their partnerships, claims, and underwriting.

To achieve a competitive edge, insurance providers use predictive analytics in multiple areas. The most common uses are given below.

Identifying and Preventing the Risk of Fraud

The Insurance Fraud Alliance reports that $80 billion is lost annually from false claims in the US alone. For insurers in the United States and Canada, fraud accounts for an extra 5-10 percent of insurance costs.

Predictive analytics can help insurance firms establish a customer base with information that can be used to see whether new policy customers fall into a category of people who might commit fraud. In reality, the insurer may feed the model with past data and then use it to identify new prospects and consumers.

Identifying the Risk of Discontinuance of the Customer

Predictive insurance analytics can help professionals classify consumers who are likely to cancel or reduce insurance coverage. Advanced data analytics that would be made accessible by predictive analytics will help insurers identify clients that are dissatisfied with their coverage or carrier. Having this information in hand will allow insurers to provide personalized customer service and to fix potential issues. Without the use of predictive analytics, insurers can ignore these credible warning signs and waste valuable time.

Identification of Potential Markets

Predictive research can help insurers define and target new potential markets. Collected data may show trends of behavior and common characteristics so that insurers know where to focus their marketing efforts. Predictive modeling is a fantastic method for analyzing social media data when it comes to identifying future markets, as these networks have become increasingly relevant these days.

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