Data Analytics Blog - Real-World Applications | MicroStrategy

Insurance and Analytics: Four Ways to Benefit from BI

Big data is transforming the business world every day. With access to unprecedented amounts of information on business performance and consumer behavior, companies have almost limitless possibilities for refining their products and services.

Take the insurance industry for example. It’s a model built on the principle of risk and a natural fit to leverage data to make better business decisions. Customers, both commercial and personal, take out policies based on their likelihood of a bad thing happening to them, and insurers offer them coverage based on their assessment of the cost of covering any claims.

As a people-centric and data-rich industry, insurance companies have many ways to refine their business practices using modern data analysis. Below are four key insurance company operations that can be transformed using modern analytics techniques.

Sales and Distribution

For insurance companies to succeed, they need to provide sales reps with information that helps them make fast and accurate recommendations while on site with brokers or clients. Traditional sales solutions weren’t equipped to handle the needs of a mobile sales team, and they often leave users struggling to find the in-depth insight into individual customers or prospects that sales professionals need to be effective.

Modern BI tools now provide sales reps with mobile access to resources that boost productivity and give sales teams a leg up on the competition. Think context-aware maps that help determine which account to visit next, multimedia content like sales presentations and training videos, or real-time access to quote analysis, buying patterns and demographics.

Fraud Detection

Fraud detection is of the highest priority for insurance professionals dealing with claims. How do you spot inconsistencies before a hefty payout is made? Most fraud solutions on the market today are rules-based, and unfortunately, it is too easy for fraudsters to manipulate and get around the rules.

Predictive analysis, on the other hand, uses a combination of rules, modeling, database searches and exception reporting to identify fraud sooner and more effectively at each stage of the claims cycle.

Underwriting and Claims Management

The claims process is typically the single largest expense for an insurer, so it’s critical that this process is as efficient and effective as possible. Processors and underwriters sort through incredible amounts of data—from adjusters’ hand-written notes to data from fraud lists and information stored in claims management databases. The accuracy of underwriter calculations is a critical factor in the success of the insurance company, and if the calculations aren’t precise, the company runs the risk of being overpriced in comparison to the market or even suffering significant losses from unexpected claims payout. And with so many claims to handle, it’s imperative that adjusters find ways to streamline their processes.

Powerful analytics enable underwriters to confidently act upon massive amounts of data related to customer credit history, risks, market information, and more. Furthermore, analytics allow claims adjusters to easily assess critical data related to policy information, police reports, loss, frequency, severity, and more. By mobilizing applications, adjusters can access claims information anywhere and directly input photos or notes from accident scenes, auto repair shops, or other relevant locations.

Customer Insight and Management

Like any industry, insurance is constantly evolving, and insurance companies that fail to keep up with change risk lower profit margins and losing market share to competitors. Given the limited opportunities for face time with customers, it is especially critical that insurers make the most of every customer interaction. One way to stay ahead of the curve is by analyzing data to gain greater insight into the preferences and behavior of consumers. Comprehensive customer profiles provide a better understanding of customers’ preferences, lifestyles, call center interactions, and other key characteristics. Insurers can leverage this data to deliver highly relevant and personalized offers and services.

Social media, for example, provides a particularly rich source of data, with information that’s accessible almost in real time. This enables insurers to create smarter marketing campaigns, adapt more quickly to consumer feedback, and create new products based on consumer preferences.

Analytics and big data are impacting all industries in different ways, but it’s certain that the insurance industry is one of the leading innovators of BI adoption. Analytics can flag claims for closer inspection, equip sales teams with information on accounts and upsell opportunities, and provide greater insight into the customer experience and preferences.

For more on how insurers can better leverage big data and excel in today's increasingly data-driven environment, check out our webcast: MicroStrategy 10 for Insurance.

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