{"title":"Utilizing machine learning techniques to process customer claims automatically","authors":"S. Sader","doi":"10.17676/hae.2019.36.15","DOIUrl":null,"url":null,"abstract":"In this paper, Supervised Machine Learning was used to develop a new approach to handle customers’ claims which were gathered from a real-case company. Supervised machine learning was used with accurate data in order to develop a machine learning model. This model was deployed and used to evaluate new un-evaluated claims by examining their content variables and assigning a ranking value for each claim expressing its priority. The goal of this experiment was to show evidence on the ability of new technologies such as Machine Learning to automate quality management traditional activities, improve efficiency and effectiveness, and support a new approach to “Quality 4.0”. Other goals were to improve customer satisfaction by enhancing responsiveness to their claims and to convince the company (the real case of this study) to extend the project for further applications.","PeriodicalId":104429,"journal":{"name":"Hungarian Agricultural Engineering","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hungarian Agricultural Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17676/hae.2019.36.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, Supervised Machine Learning was used to develop a new approach to handle customers’ claims which were gathered from a real-case company. Supervised machine learning was used with accurate data in order to develop a machine learning model. This model was deployed and used to evaluate new un-evaluated claims by examining their content variables and assigning a ranking value for each claim expressing its priority. The goal of this experiment was to show evidence on the ability of new technologies such as Machine Learning to automate quality management traditional activities, improve efficiency and effectiveness, and support a new approach to “Quality 4.0”. Other goals were to improve customer satisfaction by enhancing responsiveness to their claims and to convince the company (the real case of this study) to extend the project for further applications.