5 Advanced Analytics Applications for Insurers

Nothing expands demographic outreach like a powerful web presence and strong search engine optimization (SEO). With modern tools, any company can take charge of its outreach, while also monitoring its public image in real-time.

In recent years, the proactive use of advanced analytics has become one of the most empowering tools for monitoring that image, as well as gathering business intelligence. Using information analyses, a corporate entity can now act as its own data scientist, interpreting statistical patterns for greater business intelligence. Here, we will look at 5 advanced analytics applications for insurance professionals, and how these concepts can benefit every element of daily workflow.

1. Finding Your Demographic

 

In an era of mobile-centric consumerism, larger demographics now depend upon customization for all their good and services. This is where big data comes into play and, with insurance, the practice is no different. Analyses of customer needs puts insurers in a unique position to seek their target demographic utilizing advanced analytics. Additionally, this solution works in two ways. Advanced analytics tools provide the life insurance company with deeper insight into clients’ needs, as well as customized customer engagement. Accurate data mining through analytics also yields invaluable cluster analysis for learning product preferences and past customer experiences.

Marketing strategies are created out of these advanced analytics techniques. Elements of SEO and social media content tools all play a part in outreach strategy. More so than with traditional analytics, this solution uses data in obtaining customers’ sentiment analysis, as well as creating notifications of your new financial products. In short, advanced analytics gives the business user a competitive advantage.

2. Real-Time Risk Management

Ask yourself “What is advanced analytics going to do to protect my existing corporate assets?” As a data-gathering tool, an advanced analytics program utilizes smaller applications. These analytic techniques include text mining, consolidation of unstructured data, pattern matching, and other statistical methods for the use of data science. Not only do these applications benefit a life insurance company in outreach but, additionally, can aid a company in assessing real-time risk management.

All forms of insurance deal with risk. With life insurance, however, using data analytics as a predictive measure becomes seamless. For example, advanced analytics can be applied towards combing data sources for a client’s previous driving record, health concerns, or overall “risky” lifestyle. Each of these factors can be calculated for estimating your offered premium and life insurance quote.

3. Predictive Claims Assessment

Additionally, advanced analytics creates predictive claim assessments for life insurance policyholders. A major element in life insurance coverage is in making accurate predictions for the mitigations of potential risk against financial strength. Much like its functions in drafting risk management, an in-depth analytics platform works well in forecasting potential future events based upon a client’s lifestyle. This adds crucial deep learning insights into their overall portfolio. This predictive analysis can protect your company’s finances, while also acting as the next step in providing your client with the most affordable life insurance quote.

4. Fraud Detection

If predictive analytics functions of an advanced platform help create an accurate client portfolio, what other important business operations can it perform? High up on the list of advanced analytics benefits is its ability for fraud detection.

Among the many business intelligence tools the advanced platform offers, this analytic function can save your company a fortune in fraudulent claims. Specifically, advanced predictive analytics detect statistical models for potential fraud, then compares this data set against historical data of previous fraud activity from similar claims. Used together, an accurate prediction of a fake claim can aid your team in making better decisions involving insurance payouts. This predictive modeling function helps the screening process and creates a working template for future fraudulent activity.

5. Client Lifetime Value Prediction

Finally, the algorithms of advanced analytics can be valuable for determining a life insurance policy’s term life. An advanced platform measures the correlation between diverse data sets and Customer Lifetime Value (CLV). Ultimately, this can dictate the amount of life insurance and the type of life insurance best suits your client’s needs. Additionally, you can determine the predictive analytics of that client’s behavior and their long-term profitability to your company, giving you peace of mind as their primary life insurance provider.