신용카드 대손회원 예측을 위한 SVM 모형

• 영문명: Credit Card Bad Debt Prediction Model based on Support Vector Machine
• 발행기관: 한국IT서비스학회
• 저자명: 김진우(Jin Woo Kim) 지원철(Won Chul Jhee)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제11권 제4호, 233~250쪽, 전체 18쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2012.12.31

영문 초록

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

목차

Abstract

1. 서론

2. 신용카드 회원 대손처리 가능성 예측

3. SVM 기반의 분류 모형

4. 대손처리 가능성 예측 모형의 설계

5. 실험

6. 결론

참고문헌

저자소개

키워드

Credit Card Delinquency Bad Debt SVM(Support Vector Machine) Data Imbalance Ensemble Model Data Mining