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Unlocking QRC: NLP in Deciphering Messages

Customers touch points often receives millions of supportive inputs and are classified as Query, Request and Complaints (QRC) . Due to manual segmentation, the inputs are wrongly classified. RBI has stated that often Banks / NBFCs classify customer complaints as queries, and advices that such practice understates problems.

Tagging wrong descriptions and inconsistent description matching were widely witnessed in segmenting the customer inputs. Besides failing to comply with regulatory needs, it also mislead customer experience.

We deployed our patented NLP IP to classify with 99% accuracy. As each input received from our omni channel terminals are accurately segmented under QRC, we developed value added service by calculating Ai driven Satisfaction Score.

We identified the need to streamline the operation process at all omni channel touch points on priority. We handhold our client to start addressing issue based improvement to improve CX Score. .

Ground-breaking Algorithm on Turning Point Discovery

This ground-breaking algorithm revolutionizes the way Turn Around Discovery is made: Thematic relationship among objects based on context!.

Textual and indicative pointer-based forecasting to derive behavioral implications and future trends along with turning points.

With its unique Ai & ML Algorithms, this solution addresses the core challenges head-on, offering unprecedented benefits and advantages.

Advantges of Ai driven NLP Solution in QRC

Helped to accurately segment QRC from omni channel inputs besdis improved compliance and improved Customer Satisfaction. With Thematic relationship, the system forecasts upcoming spikes of cases based on News Items on RBI Regulatory Notifications. In addition to this, Satisfaction Score is embedded into the periodic QRC records. This helps our client to get early warning signales on complaints, whihc was proactively addressed . .

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