Just as underwriting is crucial in acquiring and growing an insurance company’s customer base, claims management is paramount to protecting company assets and smaller insurance companies are leaning on predictive analytics in greater numbers than ever before. Many are utilizing specialized software for insurance companies designed to deliver this type of analytical data faster and with increasingly greater accuracy to improve their companies’ bottom line and give a greater edge to their competitive advantage.
Making use of the pertinent data for predictive analytics is not always an easy task. Just possessing the data is not enough. The information must be proportionately weighted and analyzed in an environment of continuously changing industry standards. Economic factors, as well as business and consumer strategies and behaviors, come into play as well.
While history does not always repeat itself exactly, tracking historical trends following industry-proven protocol for predictive analytics is a powerful method for projecting future trends in claims submissions, policy administration, and business growth. Unfortunately, the process can be complicated and difficult to navigate despite the abundant availability of data points.
For example, to run this type of analytical process without specialized software for insurance companies, a company would need the capability to identify and classify data points, determine trends, discard data that should be discarded, and create a result that is usable for strategic planning, both long and short-term. Using specially designed software for insurance companies can help to ease this process by running the formulas and algorithms based on simple data entry to produce the results needed. Overall, such software is not difficult to use but is designed to assess risk and also propose solutions for management of the risk.
Predictive analytics can also help insurance companies identify patterned behaviors that may indicate a propensity for fraud, promote cross-sell opportunities, improve operations, and reduce the risk of loss of customers.
While the process may appear to be more troublesome than it is worth, the truth of the matter is that, for most companies, the results are worth the effort. For example, leaning on customer-supplied information alone is unreliable at best because statistics show that only about 20% of voluntarily supplied customer information is accurate. Predictive analytics, on the other hand, provides solid projections for future trends based on actual historical data. This allows smaller insurance companies to better prepare for the future.