Published on 18|5|2020 by Lucie Kolářová & David Radosta
Czech financial and investment advisers have a new regulation to comply with. From 1st May 2020, they are obliged to record telephone calls with clients in which they deal with investments. That way, CNB aims to ensure financial advisers and intermediaries comply with rules. Most financial advisory companies and advisers were against this. It is a burden both procedurally and financially; in addition, they may need to employ people to evaluate the calls. That is when AI comes to the rescue, transcribing calls, evaluating them and structuring the data to allow for searching for specific information. As a result, financial and investment advisory firms will gain a new world of insights about customers that would otherwise be difficult to access.
One of the use cases for SentiSquare Call Analysis are investment-related conversations between advisors and customers. The analysis will be performed as an on-premise solution, ensuring data protection compliance. The SentiSquare Analytics tool structures and visualises the output, also providing semantic search in the transcribed calls. The project thus focuses on monitoring investment calls in terms of compliance, topic detection and easy search the data.
Off-line speech recognition (convert audio recordings to text).
Off-line processing and analysis of call transcripts.
Automatic topic detection and keyword extraction (for a quick overview of what the call is about).
Automatic categorization of calls into predefined classes (with a focus on the topic of investment).
SentiSquare Analytics for easy semantic search and insight into transcribed calls.
why are they reluctant to buy a specific product
have the benefits of the product been explained detailed enough
what alternatives the customer is thinking about
what risks does he see
how is your agent dealing with customer concerns
Reliably: SentiSquare Analytics user interface is a proven system for working with text data.
Safely: The analysis is performed on the investment company's servers.
Smartly: Provides semantic search in transcribed calls.
Simply: This makes it easy to detect, track and search for all investment calls
Quickly: Deployment within 4 weeks
Algorithms from SentiSquare are based on distributional semantics. This allows the algorithm to understand the meaning of words from raw text only (i.e. without human supervision). Two words are semantically similar when they appear in similar contexts (they are similarly distributed across texts). Meaning is represented as a vector of real numbers. Geometrically speaking, meaning is a point in a high-dimensional semantic space. Words that are semantically similar tend to be close in semantic space. This way, algorithms understand the meaning of any text, even when expressed in different words (synonyms) or in a different style.
“We believe that we will be able to help financial consulting companies with the analysis of recorded calls in a few short weeks, so that extra work will not burden them in terms of time or money, and they will be able to continue their work without interruption. Our clients can just leave the robotic tasks to us. The biggest benefit of this analytics getting information about customers that would otherwise be difficult to obtain. They can easily read about whether the customer has a good understanding of the offer, what they worry about, what they consider to be a risk. The output of the analysis also serves as an overview of how individual consultants are doing. This intelligence makes a direct impact on sales,” says Jan Tichý, sales manager SentiSquare.
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SentiSquare is a technology company that deals with customer-generated text analysis. As one of the few companies uses artificial intelligence based on principles of distribution semantics, which provides many competitive advantages, such as language independence. The company was founded in 2014 as a spin-off by team of researchers at the Faculty of Applied Sciences of the University of West Bohemia in Pilsen. SentiSquare currently supplies technology to the contact centers of large companies such as T-Mobile, E.ON or Albert.
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