Insurance companies collect huge volumes of text on a daily basis and through multiple
channels (their agents, customer care centers, emails, social networks, web in general).
The information collected includes policies, expert and health reports, claims and
complaints, results of surveys, relevant interactions between customers and no-customers
in social networks, etc. It is impossible to handle, classify, interpret or extract the essential
information from all that material.
Fraud detection
According to Accenture, in a report released in 2013, it is estimated that in Europe
insurance companies lose between 8,000 and 12,000 million euros per year due to
fraudulent claims, with an increasing trend. Additionally, the industry estimates that between
5% and 10% of the compensations paid by the companies in the previous year were due to
fraudulent reasons, which could not be detected due to the lack of predictive analytic tools.
Analysis of the Voice of the Customer
The customer experience is a key element in the commercial success in the insurance
sector, where differentiation between the insurers’ products is not easy. Companies are
trying to know their customers and their opinions by means of satisfaction surveys and
directly through social networks.
When this type of analysis is applied to comments in open social networks, it is possible to
detect trends in the sector, identify the brand perception (which concepts, activities or
entities we are associated with, and how we differentiate ourselves from competitors),
qualify the corporate reputation of our company or brand, or provide early warning of
possible reputational crisis. The activities that have to do with market research and
competition analysis are essential to determine the company’s product strategy.
Topics Extraction Application Programming Interface
Topics Extraction enables to tag names of people, places or organizations in any type of
content, in order to make it more findable and linkable to other contents. Tagging this
information facilitates to structure any type of unstructured information (text, audio or video)
and get its semantic mark.
Text Classification API
Text Classification assigns one or more categories to a text to facilitate its management,
allowing it to filter, sort, or group texts. Search engines, newspapers, or e-commerce portals
categorize their content or their products to facilitate the search and navigation. Understand
your clients and what they say about your products by categorizing the conversations on
social networks or contact centers.
Corporate Reputation API
Corporate Reputation analysis enables us to understand the impact that the opinions and
perceptions of the public have on a number of reputational dimensions or axes of an
organization. Companies and agencies have been doing this analysis offline and on a
periodic basis, through reports prepared from interviews and surveys. With this API it is
possible to include in this analysis the immense volume of spontaneous and real-time
information provided by social networks, forums, blogs or news, to assess their impact on
the reputation and immediately detect potential crises.
Benefits that text mining provides to the insurance industry
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Improves the employees’ productivity
Reduces the workload of Customer Service Centers
Reduces the response to clients’ time
Early detection of evidence of fraud and fraudulent customers
Value provided by text mining to insurance companies
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Increases the customers’ and employees’ degree of satisfaction
Improves the client retention and recommendation
Improves the vision on the market scenario
Reduces the indirect costs in the provision of services
Increases benefits, as a result of all the previous ones