Predictive analytics

Predictive analytics

Swaminathan Kannan

Swaminathan Kannan,

Data analytics and more specifically Predictive Analytics find usage in all key areas of success for insurers.

Customer retention and growth: Prospecting, lead generation and efficient onboarding:
Because the cost of attracting and underwriting new customers is many times more than the cost of keeping current customers, customer retention is a high priority for most insurance companies. With high customer churn levels across many insurance products and geographies, it is critical for insurers to identify at-risk customers as early as possible, while there is still time to take necessary actions to retain them. By integrating predictive analytics into customer retention strategies, insurers can provide the early warning that agents call center representatives and other employees need to keep their best customers longer and improve customer lifetime value. Predictive analytics provide ability to:

  • Discover policy termination patterns and profiles of customer who leave for a deeper understanding of why they left
  • Predict future customer value to determine if insurer wants to retain an at-risk customer, and at what cost
  • Predict what offer or service would prevent a customer from switching insurers, and what price will cover the risk of insuring that customer for another term.

Claims Management:
Predictive analytics is a particularly powerful resource for determining how to treat an individual claim at every stage of the claims lifecycle. It can be used to ‘right track’ claims, instantly identifying simple claims for quick approval and flagging suspicious claims for follow up. Fast tracking of simple claims improves customer satisfaction and can help improve loyalty. Also, with quicker identification of claims that require additional investigation, firms can significantly reduce unnecessary, inflated or fraudulent payouts. These critical capabilities help insurers increase their profitability by giving them the ability to:

  • Resolve legitimate claims more quickly.
  • Assign the right resources to the right claim at the right time
  • Automatically detect new forms of fraud through analytics that ’learn’ from data
  • Determine if a claim will involve a legal dispute
  • Determine if a claim can be recovered from a third party insurer.

Risk base pricing:
Insurance carriers typically apply broad-brush business rules to quote new policy applications or policy renewals. By more accurately assessing the risk of an individual applicant, the carrier is able to underwrite to a profit. Predictive analytics provides deep insight in to the variables and interrelationships that define risks, so insurers can craft premiums and coverage that are the best fit for customers’ needs and most financially rewarding for the company. Predictive analytics enables insurers to perform deeper analysis of risk, allowing for many more optimized price points. This helps reduce underwriting leakage and retention risks, while increasing profitability for each individual customers.

Agent Management: 
Intermediaries such as agents and brokers remain a major route to market for insurance carriers. Relationships with these stakeholders can be improved through the use of predictive analytics from two perspectives; growing the business and managing interactions. Carriers have a broader perspective of customers than the individual agent. This insight can be provided to sales representatives in the field to help agents win and keep more business. For example, the carrier could predict the best cross sell leads for an individual agent, and the best offer for each customer.  Carrier can also analyse agent attributes and use models to predict the most effective ways to increase agent retention, manage risks and control costs.

Superior customer experience:
Filing an insurance claim is the moment of truth in the relationship between an individual and their insurance company. Similarly, other interactions such as taking out a new policy or making administrative changes to a policy have the power to “make or break” the relationship with a customer. With predictive analytics, insurers can accurately measure how the customer experiences these interactions and relate that experience to customer decisions such as policy renewal, uptake of additional products and services and positive or negative feedback. Gathering and leveraging this information helps insurers understand where to make improvements that will eliminate customer dissatisfaction and strengthen areas of competitive differentiation.