Over the past decade, there has been a significant shift in how the insurance industry operates. Predictive analytics has become the newest way for insurance companies to set themselves apart from the competition.
Predictive analytics and other related tools such as artificial intelligence, machine learning, and big data use statistics and probabilities to predict outcomes.
Predictive analytics tools now can collect data from customer interactions, telematics, agent interactions, and even social media to better understand and predict the behavior of insureds and manage their relationships, claims, and underwriting.
Here are 5 ways Predictive Analytics in the insurance industry is changing the game of the competition.
Underwriting:
Predictive underwriting is about using historical data to predict the probability of risk. Predictive underwriting requires insurers’ data as well as external data such as social media, credit agencies, and government agencies. Together they form a single dataset that can be used for analytics purposes.
This data is now modeled to improve risk prediction. The predictive model enables underwriters to get an automated result based on which they can make decisions
Claims:
Predictive models compare factors associated with new and pending claims against those of past losses. The factors can be characteristics of the claimant (including age, education, income, etc.), insured data, liability, and other data. Analysis of past claims fitting similar fact patterns can provide insight for new and pending claims.
Marketing:
Clustering models can help insurers reach their customers with the level of personalization that customers have come to expect, through increasingly granular customer segmentation, while association rules help match promotions and offer to each individual customer based on customer segments.
Clustering models can analyze billions of consumer interest variables and touchpoints, identify specific customer’s interests, and group customers with similar interests for targeting. Segmentation also doesn’t have to be static. Dynamic segmentation takes into account the fact that customers’ behaviors are rarely fixed.
Identifying Fraud:
Predictive analytics can help find hidden clues in the data. Analyzing data across claims could reveal patterns that can help identify the fraudster. For example: individuals who repeatedly claim under various policies.
Moreover, Insurers can use social network data about their policyholders, vendors, associates, and their associations and interactions to predict fraud. Many of the insurers have leveraged structured data to a certain extent and there are a lot of opportunities to exploit the unstructured data around them.
Churn:
Predictive analytics can help identify those customers who are likely to cancel or lower the coverage. For Insurers, churn means high costs in the form of lost profits, but also the entire customer lifetime value to their revenues.
Churn analysis is a classification problem. In the first step, often forecasting techniques such as random decision forests, regression procedures, or neural networks are used to classify typical churners and non-churners. In the second step, the same model can be further used on non-classified customers, where churn probability will be calculated based on the differentiation pattern learned in the first step.
