Insurance Companies Use NLP Technology to Analyze Text and Reduce Fraud
Identifying fraud and delivering outstanding, timely customer service to deserving claimants are long-standing challenges for insurance companies. How can insurance companies better use available data to service their quality clients?
Insurance companies collect text-based data, in many languages and dialects, from an array of sources: applications, claims, email, social media, adjuster notes, medical records, police statements, surveys, research studies, underwriter notes, competitive intelligence and more. This vast quantity of unstructured and often conversational data piles up. While individual employees deal with the data that crosses their desks, until recently, very few companies have been able to look at the data as a whole; to observe trends, spot new topics, identify potential problems including individual and group fraud, or flag new business indicators.
What is Invisible Data and How Can it be Used to Detect Fraud?
By most estimates, up to 90% of the information available in an organization is actually unstructured or unconnected data, and that percentage is likely to increase with the growth of social media. Compounding the problem is that social media is largely conversational, and conversational language is ambiguous. Key messages buried in text data are not easy to discern or process and may as well be called invisible data.
For an industry that is driven by data, text analytics is still new to most insurance companies though more and more companies are now using the technology to improve efficiencies, drive product development, and increase customer satisfaction and loyalty.
So, What is Text Analytics or NLP?
Text analytics is the use of computer software to annotate and extract information from electronic text sources, to analyze that information and to discover and identify patterns for business purposes. Truly, pattern discovery is the true value of text analytics or Natural Language Processing (NLP).
Two Main Text Analytic/NLP Technologies
Within text analytics, there are two main technologies:
Sentiment analysis, which is also known as opinion mining, locates and extracts sentiment from online materials. Sentiment analysis identifies the subjective information and attitude in a source document or message, allowing organizations the ability to monitor social media in real time and to respond accordingly.
Text mining provides powerful ways to explore and analyze unstructured data to discover previously unknown concepts and patterns.
Detect Fraud with NLP and Text Analytics
Insurance companies are now using text analytics or NLP to mine the details contained in applications, adjuster notes, and other unstructured text sources. One benefit of this analysis is helping prioritize cases for SIU examiners. Specifically, NLP and text analysis helps examiners find common phrases or descriptions of an accident among multiple claimants, which is usually an indicator of organized fraud.
Overall, text analytics and NLP helps businesses identify and react to crucial information, both objective and subjective, which translates to better business practices and ideally, a better customer experience, which is a win-win for everyone.