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Is it time for text analytics in Financial Services?

By Alan D. Duncan | September 21, 2016 | 0 Comments

Information ManagementData ScienceData QualityBusiness IntelligenceBusiness AnalysisAnalyticsData and Analytics Strategies


Lately, Gartner account teams have been highlighting an increasing interest in text and voice analytics within their financial services clients. This is sometimes characterised as the desire for “natural language processing” or NLP (although more completely, NLP covers a much broader set of language-related analytical processes that may also include Natural Language Question/Answering (NLQ), Natural Language Generation (NLG), speech analytics and machine learning analytics applied to language-based data sets. See also “Hype Cycle for Data Science, 2016“).

Use cases where text analytics is being applied include:

  • Improving customer intimacy and customer service with sentiment analysis and voice of the customer (VoC) solutions on customer feedback to drive customer satisfaction, loyalty and revenue.
  • Evaluation of customer interactions with call-center service agents to identify hidden product, sales or complaint challenges
  • Insurance analysis to assess customer claim narratives and to derive feedback for new underwriting models
  • Customer support and social monitoring
  • Identifying likely fraudulent, potential compliance and legal risks by scanning through all text-based information and interactions

I would also highlight the following broader considerations:

  • Gartner observes an increasing uptake in text analytics solutions, although this is not yet pervasive.
  • The complex challenges of analyzing text data are significant and require a combination of linguistic, statistical and algorithmic techniques to synthesize valuable business output. Therefore, the computational complexity involved typically requires specialist analytic tools to enable the development of such solutions.
  • While most text analytics offerings support a single use case well, vendors’ claims of more general capabilities should be treated with caution.

We therefore differentiate between different classes of technology provider in the text analytics space:

  • Text analytics workbench providers, which offer all three core architectural components as a combination of application platforms, development environments, SaaS or plug-in components.
  • Solution specialists, which provide solutions to address particular business issues with text analytics as a feature and not as the defining functionality (for example, an e-discovery product that includes text analytics as one of many required modules, VoC applications that include text analytics to analyze customer Web feedback, and email sentiment to define risk of churn).
  • Component providers, which deliver specific text analytics plug-ins that augment the functionality of business applications from other solution vendors.
  • Generalist analytics vendors, which offer a variety of different (and sometimes disparate) text analytics tools and capabilities, typically as part of a much broader portfolio of information management and analytics solutions.

For further exploration of the capabilities for text analytics, see Gartner’s Market Guide for Text Analytics

The Gartner Blog Network provides an opportunity for Gartner analysts to test ideas and move research forward. Because the content posted by Gartner analysts on this site does not undergo our standard editorial review, all comments or opinions expressed hereunder are those of the individual contributors and do not represent the views of Gartner, Inc. or its management.

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