Importance of Big Data Analytics for Insurance Services Provider
With millions of claims to handle, adjusters find it extremely difficult to sift through all the documents. High-risks claims turn out to be very expensive to the company and need priority handling. The cost involved in processing high- and low-risk claims has a huge impact on any insurer. Other than processing costs, fraudulent insurance claims processing adds to the expenses. Therefore, any effective analytical solution to identify both high and low-risk claims proves to be beneficial.
Quantzig offers predictive analytics solutions to the insurance companies to identify potential high-risk claims beforehand and subsequently reduce future costs involved with the claims. Our big data engagement has combined decision analysis with logistic regression technique to classify high and low-risk claims. Logistic regression models use claim characteristics to assign risk score for each claim, and decision analysis is used to come up with an optimal cut-off point to identify high-risk claims.
Business Challenge [spacer height=”10px”]
To reduce the future costs involved in high-risk claims, the client – a leading insurance company in the US – approached Quantzig to obtain solutions to identify potential high-risk claims beforehand. High-risk claims are extremely costly and can wreak havoc in an organization. The client was using heuristic approaches based on disability type etc. to identify potential high-risk claims but the results were highly inaccurate. The client wanted the big data engagement team to develop an effective analytical solution to identify both high and low-risk claims. While high-risk claims would be subject to intensive claim management intervention, and low-risk claims would be processed directly. The primary scope of this big data engagement was to identify potential high-risk claims beforehand and subsequently intervene to reduce future costs involved with the claims.
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Solutions Delivered [spacer height=”10px”]
Quantzig’s big data engagement combines decision analysis with logistic regression technique to classify high and low-risk claims. To meet the specific needs of the client, the research team analyzed a plethora of data including injury dates, payment details, nature of the injury, the tenure of employment, and monthly payments using big data analytics. We employed various variable selection techniques like PCA, variable clustering along with the strength of prediction tests to identify the top predictors developed individual logistic regression models for each disability type because of multiple levels of the categorical variables for an aggregate model. The big data solutions identified the risk probability threshold value for each model by assessing the expected future cost savings at that cutoff value.
Benefits of Big Data Analytics
In a span of a few weeks, the client was able to identify the propensity for each claim to be high risk with big data analytics. Claims with more than 60 workdays lost were more likely to be high-risk claims, and high correlation between estimated loss of earnings and percentage of disability were considered important to reduce the cost incurred. The predictive models were trained on historical claims and evaluated with an out of the sample and out of time data. Worker’s gender, age, and the amount of disability days paid were the most significant variables in predicting high-risk claims in all the models. The big data analytics engagement solutions completely automated the claims classification process which helped the client in significantly reducing the claims processing time. The team analyzed the extremely high-risk claims from usual and non-risky disabilities to identify the fraudulent claims and flagged the respective characteristics for future enablement using big data analytics. The big data analytics assessment showed that for the top three costliest disabilities, 68% higher probability of incurring a high-risk claim was observed in women when compared to men.
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