Multiple instance learning for credit risk assessment with transaction data
作者：
Highlights：
• A machine learning methodology is proposed for analyzing and scoring personal credit.
• Multiple instance learning is developed to extract credit features from transaction data.
• Transaction features are combined with personal and application features for feature engineering.
• Our research contributes to advance the computational method for credit scoring.
摘要
•A machine learning methodology is proposed for analyzing and scoring personal credit.•Multiple instance learning is developed to extract credit features from transaction data.•Transaction features are combined with personal and application features for feature engineering.•Our research contributes to advance the computational method for credit scoring.
论文关键词：Credit risk assessment,Feature engineering,Transaction behavior,Multiple instance learning
论文评审过程：Received 13 January 2018, Revised 12 July 2018, Accepted 20 July 2018, Available online 22 July 2018, Version of Record 31 October 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.07.030
