How predictive analytics is quickly changing the P&C industry
Agents, brokers and underwriters can save time, money and resources by making the most of digital data.
Predictive analytics has been immensely useful within the insurance industry — especially in appraising and controlling risk. Through data mining, predictive modeling, statistics, machine learning, and artificial intelligence (AI), the most common use cases have led to accurate insights that aid in underwriting policies and personalized client services.
As the tools for accurate analytics are improving, predictive analytics is disrupting the insurance industry, especially within underwriting, claims management, and distribution.
The massive amount of structured and unstructured data continues to expand, which provides both a challenge and an advantage for insurance firms as underwriters, brokers, and agents have access to more information. However, consumers now demand more personalized services and are more likely to switch to a carrier that can better understand and meet their needs.
What is predictive analytics in insurance?
Predictive analytics is the use of various statistical techniques from data mining, predictive modeling, AI, and machine learning to analyze historical and real-time data to make predictions about future events. Insurers can collect large amounts of data from various sources to better understand and predict the behaviors of their current and prospective clients. For example, property and casualty (P&C) companies may collect data from customer interactions, social media, telematics, agent interactions, smart home applications, and more to understand their audience better, manage their client relationships, and assist in underwriting and claims processes.
By converting data into actionable insights on customers, agents, and markets, as well as implementing factual strategies that drive long-term growth, insurance firms can better understand and identify their greatest market opportunities, create a strategy for sustainable growth, and provide personalization for customers.
Predictive analytics has been around for decades, and its use cases continue to expand. In the past, brokers may have used predictive analytics to change a premium on a policy, but today they can analyze and extract insights from dozens of data points to obtain more factual insights, provide personalized policies to clients, and better understand their market.
Willis Tower Watson, the third-largest insurance broker in the world, reported that more than 60% of insurers stated that predictive analytics helped reduce underwriting expenses. Additionally, another 60% stated that the additional insights gained from predictive analytics have increased sales and profitability. These numbers are expected to grow significantly over the next few years as predictive analytics is providing value in multiple insurance applications.
Three ways predictive analytics helps underwriters, brokers and agents
1. Obtaining data-driven insights
Data is used in nearly every aspect of the underwriting, distribution, and sales processes. The insights gained from data are the most valuable resources that brokers, underwriters, and agents can have to understand current and prospective clients. A greater volume of data means more insights, but it can be challenging to identify and extract the right data points for accuracy.
An organized data management system can help with accuracy and makes it easier to use predictive modeling. If data is scattered across various systems, valuable information can be missed, and data can be wasted. When data is managed in one system, it makes it easier to use predictive analytics to build and analyze customer profiles, provide insights into risk, pinpoint marketing opportunities, and predict profit from each customer.
Combining predictive analytics with an organized data management system maximizes the value of data. With an organized data management system, insurers can tend to customer needs more quickly through the cloud, and predictive analytics can be used to deliver factual insights from the data management platform by analyzing client interactions.
2. Understanding the 360-degree view of customers
Digital transformation has enabled greater opportunities to interact with customers closely. Today, customers are more likely to stay loyal to insurance carriers that engage with them and understand their needs.
By understanding what drives people’s behaviors and decision-making processes, agents can tailor their policies to their clients, brokers can identify the right insurance carrier, and underwriters can better identify risk. Using the insights from predictive analytics, agents can even understand which customers they can profit from the most, which ones will stay loyal, and which are at risk of canceling or lowering coverage. These 360 insights on customers result in more efficient marketing and sales execution, happier clients, and an increase in revenues.
3. Providing personalization
Personalization builds trust, value, and loyalty. Nearly half of customers have left a company for a competitor that better understood and delivered on their needs. The data-driven insights from predictive analytics enable agents, brokers, and underwriters to understand their customers’ behaviors and needs better by getting facts on a customer’s history, behavior, and interests.
Insurers can also work smarter by segmenting customers into groups that share similar interests, needs, and expectations. This enables a quicker and smarter approach to targeting as strategies are identified for each group of customers based on their needs.
Moving forward, use cases for predictive analytics will expand to help forecast events and gain valuable insights into different aspects of insurance. Agents, brokers, and underwriters can make the most of digital data and save time, money, and resources with predictive analytics. Insurance firms that use it can gain a competitive advantage from the insights gained in the market, on customers, and on competitors.
The opinions expressed here are the author’s own.
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