• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.39-40
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• 2024

본 연구는 SHAP(SHapley Additive exPlanations)을 활용하여 신용카드 사용자의 연체 가능성을 예측하는 기계학습 모델의 해석 가능성을 강화하는 방법을 제안한다. 대규모 신용카드 데이터를 분석하여, 고객의 나이, 성별, 결혼 상태, 결제 이력 등이 연체 발생에 미치는 영향을 명확히 하는 것을 목표로 한다. 본 연구를 토대로 금융기관은 더 정확한 위험 관리를 수행하고, 고객에게 맞춤형 서비스를 제공할 수 있는 기반을 마련할 수 있다.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.