The application of Predictive Analytics in Insurance Company is for the core insurance functions of marketing, underwriting and claims can provide meaningful benefits to insurers. The greater availability of data, technological advances, and pressure to maintain competitive advantage has seen insurers leverage predictive analytics to make informed decisions.
Marketing Analytics: Increasing Hit and Retention Ratio
Predictive Analytics in Insurance Company can be used to market insurance products and services more efficiently. Let’s examine this from the context of a hit ratio, a term describing how often a marketing function can generate a sale from every contact made with a potential customer. So, if an agent registers two sales for every 10 potential customers, his hit ratio is 20%. Predictive analytics can help marketers analyze customers’ purchasing patterns. Marketers can use the data gleaned from this exercise to focus on customers who are more likely to buy, and consequently increase their hit ratio. Predictive analytics can also help insurers retain customers (and improve retention ratio) by identifying those who are more likely to buy insurance from a different provider.
Underwriting Analytics: Increasing efficiency and protecting against future losses
The goal of underwriting is to accurately assess the eligibility of customers to receive the product, and price the product to protect against such losses. Insurers can decide on a model score to separate eligible candidates from their risky/non-eligible counterparts. Predictive models can be used to screen and filter out applicants who do not meet the model score. It can save considerable man hours and effort that would otherwise be spent in manually researching and evaluating applicants. The use of predictive models can also minimize the inevitable human error that creeps into the underwriting process. The model score can be further used as a rating mechanism to base different price or product points.
Claim Analytics: Separating true and false accurately
The Insurance Research Council estimates that one out of every five auto injury insurance claims may appear to be fraudulent. Property-casualty insurers often have a challenging task to identify the small number of fraudulent claims from the several thousand claims filed each year. They are prone to two kinds of errors – identifying a legitimate claim as being fraudulent, and failing to identify a dishonest claim and considering it to be legitimate. Predictive analysis can help insurers determine the legitimacy of claims more accurately. It can also be used to prioritize claims for handling after identifying them at an early stage. In this way, predictive analytics tools can drive efficiency and improvements in the claims handling process.
For Predictive Analytics in Insurance Company to work wonders, an accurate model and clean data are prerequisites. We have endeavoured to achieve this in our Insurance Analytics (IA+) solution at GrayMatter.