A Novel Semi-supervised Framework for Call Center Agent Malpractice Detection via Neural Feature Learning

TL;DR

This paper introduces a semi-supervised neural feature learning framework that improves detection of agent malpractice in call centers, reducing classification errors and enhancing agent performance.

Contribution

It presents a novel semi-supervised approach combining neural feature learning with clustering, tailored for real-world call center malpractice detection.

Findings

Significantly reduced malpractice classification error.

Enhanced agent performance indicated by silence metrics.

Effective parameter tuning for neural network features.

Abstract

This work presents a practical solution to the problem of call center agent malpractice. A semi-supervised framework comprising of non-linear power transformation, neural feature learning and k-means clustering is outlined. We put these building blocks together and tune the parameters so that the best performance was obtained. The data used in the experiments is obtained from our in-house call center. It is made up of recorded agent-customer conversations which have been annotated using a convolutional neural network based segmenter. The methods provided a means of tuning the parameters of the neural network to achieve a desirable result. We show that, using our proposed framework, it is possible to significantly reduce the malpractice classification error of a k-means-only clustering model which would serve the same purpose. Additionally, by presenting the amount of silence per call as a key performance indicator, we show that the proposed system has enhanced agents performance at our call center since deployment.