Insurance has always been a data-driven industry. Insurers must analyze historical data to estimate the risk of covered incidents and the probable size of resulting losses to set their policy premiums.
However, data collection and analysis have become even more critical for success in the industry, thanks to the digital transformation in insurance.
Let’s explore the role of data analytics in insurance today, why it’s so important, and how insurers can use it to improve their businesses.
Data analytics in insurance involves collecting and analyzing large amounts of customer information to extract meaningful insights that can help insurers make better business decisions.
Insurance companies can use the conclusions they draw from data analytics to improve many aspects of their operations. For example, that can include generating more leads, enhancing the overall customer experience, and increasing underwriting accuracy.
Data analytics is especially beneficial for insurers that cater to clients with higher and less common risks. These companies need to design more unique or innovative insurance products to meet their customers’ needs.
For example, commercial insurance companies on the excess and surplus (E&S) market often serve contractors in the construction industry who can’t find standard policies with sufficient liability limits.
Because these carriers aren’t restricted by state-mandated rate and form regulations, they can create products that meet their client's needs. However, they’ll require additional data to set their coverage limits and policy premiums since they can’t rely on standard guidelines.
The insurance industry is notoriously slow to change but can no longer afford to cling to legacy systems. At this point, insurers must learn to incorporate data analytics and digital technology into their operations to be competitive in the marketplace.
Customers demand more from their providers than ever, and insurers need modern tools to meet their expectations. For example, customers increasingly refuse to settle for anything less than:
Insurers must gather and analyze data throughout the customer journey to provide these for their customers. For example, conversational chatbots are becoming an industry standard to help customers avoid waiting for real-time service representatives.
Data analytics are essential for identifying frequently asked questions and problematic processes, so software engineers can teach chatbots how to help.
However, insurers aren’t just under pressure from their customers. The competition is also increasing rapidly. InsurTech startups can provide customers with the experiences they want from start to finish, powered by data analytics and digital technology.
In addition, incumbent insurers are partnering with these startups to combine their respective advantages. As a result, insurers must employ similar tactics to be competitive.
Data analytics can help insurance companies improve different aspects of their operations. Let’s explore some of the most significant examples.
Insurers have historically relied on statistical analysis to set their policy premiums. However, modern data analytics have significantly enhanced the underwriting process. For example, insurers can now:
Because insurers can collect information at every stage of the customer journey, they can access more first-hand data. The applications for improving underwriting, risk assessment, and premium pricing are limitless.
Customer satisfaction is critical in the insurance industry. Data analytics can help insurers provide a better, more personalized customer experience.
For example, providing personalized coverage limits and policy premiums for each client was once burdensome. However, advanced data analytics and the internet of things (IoT) make it easy to offer pay-as-you-go products.
Similarly, data analytics help insurers make marketing feel relevant to each customer. That’s important in a world where individuals are constantly bombarded with advertisements via email, streaming services, social media, and mobile apps.
Insurance fraud is a significant expense for insurers, especially those that provide life, property, health, and workers’ compensation coverage. It costs businesses and consumers roughly $308.6 billion per year.
Insurers have traditionally used guidelines based on common red flags to minimize fraudulent claims, but it’s only so effective. Nowadays, data analytics can create sophisticated predictive models that help prevent and detect fraud.
The National Association of Insurance Commissioners expects the development of this technology to reduce fraud losses significantly in the future.
Below are examples of data analytics being implemented in the insurance industry. These scenarios give you an idea of how it works in practice.
Automated underwriting combines artificial intelligence, machine learning, and predictive data analytics to calculate prospective client risk levels. It can save insurers significant amounts of time and money.
For example, Pathpoint is a digital E&S insurance wholesaler that provides instant access to multiple A-rated carriers. Our platform leverages automation and data analytics to help insurance agents quote, bind, and issue in just a few minutes.
That saves you the hassle of manually filling out and submitting multiple forms, then waiting for days or weeks to get quotes from carriers. It also reduces the likelihood of human error, which often cause further delays.
In addition to saving you time, automated underwriting means you can get quotes to your clients faster and save them time, which increases the likelihood that they’ll choose to work with you over a competitor.
Policyholders are more than willing to switch providers if they think they can get faster customer service, lower policy premiums, or better coverage. As a result, insurers must retain their clients and protect their profitability.
Fortunately, data analytics can help insurers combat customer churn by identifying the behavioral trends that suggest a customer is about to switch providers. This opportunity can create preventive actions and mitigate the risk.
For example, IBM offers a solution called Behavior Based Customer Insight for Insurance that can help insurers use data from internal and external sources to understand policyholders and encourage brand loyalty.
Insurers must embrace data analytics and digital technology to enhance their operations, meet customer expectations, and compete within the marketplace.
