Conversational Computational Empathy

In today's customer-centric business environment, providing efficient and personalized support is crucial for organizations to maintain customer satisfaction and loyalty. Traditional customer call support systems often fall short in terms of empathy and fraud detection, leading to suboptimal customer experiences. Neural Network Architecture like Transformer is suitable for capturing contextual information and dependencies between words making it suitable for processing customer call logs. At first, the text is transcribed from the call and processed. Then Transformer-based model for Intent Classification, Sentiment Analysis and Fraud Detection is leveraged to understand and interpret the intentions and emotions expressed by customers during a call. It will use this understanding to respond empathetically and appropriately providing a personalized and engaging customer support experience. Text to speech conversion is used and threshold pause duration of about 2 seconds is considered to be ready to give response.