[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2021.26.04.093

A Classification Model for Illegal Debt Collection Using Rule and Machine Learning Based Methods

Kim, Tae-Ho (Graduate School of Information Security, Korea University)
Lim, Jong-In (Graduate School of Information Security, Korea University)

Publication Information

Journal of the Korea Society of Computer and Information / v.26, no.4, 2021 , pp. 93-103 More about this Journal

Abstract

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.

Keywords

Illegal Debt Collection; Machine Learning; Classification Machine Learning; Text Mining; Document classification; Support Vector Machine;

Citations & Related Records

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