文章基本信息

标题：Optimization of operational costs of Call centers employing classification techniques
其他标题：La optimización de los costes operativos del Call Center empleando técnicas de clasificación
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作者：Amanda Ferreira de Moura ; Cíntia Maria de Araújo Pinho ; Domingos Márcio Rodrigues Napolitano 等
期刊名称：Research, Society and Development
电子版ISSN：2525-3409
出版年度：2020
卷号：9
期号：11
页码：1-25
DOI：10.33448/rsd-v9i11.10491
出版社：Grupo de Pesquisa Metodologias em Ensino e Aprendizagem em Ciências
摘要：The provision of credit to customers of banking chains through call center services has always been one of the resources that generate significant income for financial institutions, however, the service offers a cost, which is often above desirable to guarantee profitable contracting to Bank. Based on this, this work aims to evaluate the optimization of operational costs of call center, using classification techniques, through experimentation of supervised machine learning techniques to perform the classification task, in order to generate a predictive model, which offers a better performance in the operation of offering bank credit, to carry out an effective and productive action, conceiving greater savings for the company in identifying the public with greater adherence. For this, a database comprising 11,162 call records made from a bank offering its customers a letter of credit was employed. The results showed value correlations between variables, such as duration of the call, marital status, education level and even recurrence in adhering to subscribers' credit agreements. Through the application of the PCA to reduce dimensionality and classification models, such as AdaBoost, Gradient Boosting, SVM RBF, Naive Bayes, Random Forest, it was possible to perceive the consumer profile with good acquiescence for the investment proposal and a group of people with a high probability of not adhering to the letter of credit, so it was possible to outline an action directed to the public predisposed to the offer, minimizing expenses reaching greater profitability.
其他摘要：The provision of credit to customers of banking chains through call center services has always been one of the resources that generate significant income for financial institutions, however, the service offers a cost, which is often above desirable to guarantee profitable contracting to Bank. Based on this, this work aims to evaluate the optimization of operational costs of call center, using classification techniques, through experimentation of supervised machine learning techniques to perform the classification task, in order to generate a predictive model, which offers a better performance in the operation of offering bank credit, to carry out an effective and productive action, conceiving greater savings for the company in identifying the public with greater adherence. For this, a database comprising 11,162 call records made from a bank offering its customers a letter of credit was employed. The results showed value correlations between variables, such as duration of the call, marital status, education level and even recurrence in adhering to subscribers' credit agreements. Through the application of the PCA to reduce dimensionality and classification models, such as AdaBoost, Gradient Boosting, SVM RBF, Naive Bayes, Random Forest, it was possible to perceive the consumer profile with good acquiescence for the investment proposal and a group of people with a high probability of not adhering to the letter of credit, so it was possible to outline an action directed to the public predisposed to the offer, minimizing expenses reaching greater profitability.
关键词：Call center; Machine learning; Supervised models; Classification and predictive model.
其他关键词：Call center; Machine learning; Supervised models; Classification and predictive model.