Cost-sensitive multiple-instance learning method with dynamic transactional data for personal credit scoring

Abstract

We study how to assess an applicant’s credit risk with dynamic transactional data. The problem arises when an applicant applies for loans from financial institutions. A traditional credit-risk assessment model utilizes individual demographic and loan information from an application form. Nevertheless, dynamic transactional data is good indicators of an applicant’s credit risk. However, the lack of available data and the preexisting limitations of conventional approaches limit the use of the dynamic transactional data. In this study, we propose a cost-sensitive multiple-instance learning (MIL) approach to evaluate applicants’ credit scores that incorporate their dynamic transactional data and static individual information. Traditionally, MIL approaches can handle the variable number of input instances. However, to facilitate the implementation of MIL into credit scoring, we extend the MIL to consider the dynamic transactional data and cost-sensitive problem simultaneously. We compare our model with several benchmark MIL models by testing them on real-world data sets. Experimental results show that our model outperforms most benchmarks in many widely used criteria.