While working at an insurance carrier, my team once searched commercial vehicle data to filter by elite car makes and models. We hadn't expected to find much and were surprised to discover that there were a number of commercial auto policies with elite luxury vehicles on them. From there my team looked at the associated driver data and found a few policies where young drivers had been added to the commercial auto policy, usually with the same last name as the business owner. We were able to put our findings into a report for the commercial auto underwriting team so they could review further and decide whether to take any action with their customers.
At Rate Retriever, we use competitive pricing data to give consumers personalized rate estimates with geo-specific carrier results and pricing options. This has allowed us to learn which zip codes offer the highest opportunity for conversion for different insurance companies so that we can send them high-quality leads. The insight we glean from this is oftentimes unexpected as a provider that has highly competitive pricing for a given profile in one zip code may be one of the most expensive options in another.
When analyzing insurance data, specifically to to predict losses, one of the techniques that provided unexpected results was the grouping insurance policies into portfolios that were samples based on various characteristics like risk metrics, demographics etc. Creating portfolios and then generating high level aggregated metrics allowed for increased accuracy when trying to predicting things like loss ratio in a particular portfolio.
By implementing text mining techniques, we analyze insurance policy documents to identify key clauses or exclusions that significantly impact claim outcomes. This approach focuses on the often-overlooked aspect of policy design and how it influences claim outcomes. For example, we discovered an obscure clause related to water damage coverage in homeowners' policies that resulted in a high number of denied claims. By addressing and clarifying this clause, the insurance company was able to improve customer satisfaction and reduce potential legal disputes.
By analyzing customer service interactions and call center logs using text mining and sentiment analysis techniques, we gain insights into customer satisfaction, pain points, and issues. Identifying recurring themes and sentiment can guide improvements in customer service, product offerings, and operational processes. For example, analyzing customer complaints may reveal common pain points that can be addressed to enhance customer experience. By implementing sentiment analysis, we can identify positive or negative sentiment in customer interactions, enabling better understanding of customer satisfaction levels and areas that require attention.
Applying text mining techniques to analyze insurance policy documents allows for the extraction of key terms and clauses, providing unexpected insights into coverage, exclusions, and potential legal vulnerabilities. By analyzing policy documents, insurers can identify areas for improvement, enhance customer satisfaction, and reduce potential disputes.