AI Thrust Seminar | COSLE: Cost-Sensitive Loan Evaluations
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Effective loan evaluation can help lenders make informed investment decisions. Despite the use of classification algorithms, existing methods do not consider the return of loans in the core learning stage and thus fail to explore the relationship between the return of loans and their final loan payoff outcomes. In this study, we propose a systematic loan evaluation framework called COst Sensitive Loan Evaluation (COSLE). Specifically, we first develop an instance-aware misclassification cost (IMCO) matrix, which specifies personalized cost for each loan. Then, we present a differential labelling algorithm called DILA cost for assigning node labels and assessing the corresponding cost. By integrating these enhancements into the tree-induction process, we construct a node splitting measurement called COG index. It exploits the relationship between the return information and the final payoff outcome. Additionally, we design the LER evaluation metric to measure the ability of a loan evaluation model to increase the lender’s return. Extensive experiments based on the Lending Club dataset show that COSLE can effectively increase lenders’ return.
Wenjun Zhou is currently an Associate Professor at the Department of Business Analytics and Statistics, Haslam College of Business, the University of Tennessee Knoxville. She received her Ph.D. degree from Rutgers University, a M.S. degree from the University of Michigan-Ann Arbor, and a B.S. degree from the University of Science and Technology of China. Dr. Zhou's general research interests are data mining, business analytics, and statistical computing. She has published prolifically in refereed journals and conference proceedings, such as INFORMS Journal on Computing, IEEE Transactions on Knowledge and Data Engineering (TKDE), European Journal of Operational Research (EJOR), Machine Learning, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), and IEEE International Conference on Data Mining (ICDM). Dr. Zhou is a recipient of the Best Paper Award at INFORMS Data Science 2018, Best Student Paper Award at AOM 2017, Best Paper Award at WAIM 2013, Best Student Paper Runner-Up Award at KDD 2008, and Best Paper Runner-Up Award at ICTAI 2006. Dr. Zhou is a senior member of the ACM and the IEEE, and a member of INFORMS.