NLP to Save 7,000 Hours on Calls every month
Sber is to use Natural Language Processing (NLP) to improve its Centre for Corporate Solutions (CCS) and save over 7,000 hours of time spent on calls every month.
The single work space (SWS) system for operators has added a service using NLP, which transcribes the voice of callers into text in real time, analyses and classifies the query, finds the required information, and advises the operator on how to help the customer.
Sergey Lekhanov, Director of Centre for Corporate Solutions, Sberbank, said, "Businesses address CCS on a broad range of issues across more than 1,000 topics….The SWS.Assistant simplifies this task drastically by doing all the rough work for the operator, who can now focus on communicating with the client. As a result, we not only save time – for both the client and the operator – but also improve the quality of service."
The solution is supposed to save the entire bank an aggregate of over 7,000 hours a month.