Big Data plays a vital role in the insurance industry. Junks of data make it difficult to sift and sort the data during claims processing. And they may not make the best decision if all the data were not considered. How could the insurance companies handle large volumes of claims when there is less time? How do they make the best use of the available data?
Here is where Big Data Analytics help claims adjusters to flag the claims that requires critical inspection. This article enlists the 5 areas where data analytics makes a big difference in claims processing:
1) Fraud Detection in claims processing
Around 1 in 10 insurance claims are fraudulent claims. Predictive analytics helps to identify these frauds avoid unnecessary hefty payouts. It employs a set of rules, data modeling & data mining, and exception reporting to identify the frauds faster. The identification also becomes effective at each stage of the claims cycle.
The need for subrogation often disappear in the large volumes of data. And most of the claims data consists of adjuster notes, medical records, and police records. Text analytics enables searching through unstructured data and helps identify phrases that indicate a subrogation case. It helps to detect the subrogation cases much earlier. Consequently, it can maximize the loss recovery and decrease the loss expenses.
Insurance agencies implement fast-track processes to settle the claims. Therefore, they can lower the costs and guarantee fairness. Although, settling a claim on-the-fly can be a risky business if there is an overpayment. The companies can optimize the limits for instant payouts by analyzing the claim histories. It can reduce the claim cycle times. Furthermore, the company can achieve higher customer satisfaction and decreased labor costs.
When a claim is reported for the first time, it is difficult to predict its size and duration. For this purpose, claims forecasting and accurate loss reserving is necessary. Big data analytics helps to accurately compute the loss reserve based on comparative claim analysis. So, whenever there is an update to claims data, the loss reserve can be reassessed. Therefore, the company can estimate the money required for future claims.
It is logical to assign the most complex claims to experienced claim adjusters. However, the claims are generally assigned based on the limited available data. Consequently, the high reassignment rates can affect the claim duration and the customer experience. Data mining methodologies are employed to rank and prioritize the claims to the most appropriate claims adjusters. The claims can even be automatically settled in some cases.
A major portion of an organization’s loss adjustment ratio lies within defending the disputed claims. Insurance companies apply big data analytics to calculate the litigation propensity score. Accordingly, they can find which claims are more likely to end up in litigation. Then, those claims can be assigned to senior adjusters to settle them faster and cheaper.
The above-mentioned points are some which makes Big Data Analytics an integral part of claims processing. Now that insurance has become a commodity, it is important for carriers to improve their claims processing. Analyzing the data completely helps to deliver a measurable ROI with cost savings. Leverage your data today. Check out our big data analytics services and see how you can make a difference in the industry. Request a FREE demo today.