Paper Info
Title
A Self-adaptive Indicator Selection Approach for Solving Credit Risk Assessment
Abstract
Credit risk assessment, which aims at identifying high-risk users, plays a critical role in financial institutions. A common method is to use the greedy strategy to generate an interpretable rule set to classify all the users into high-risk or non-risk users. During each iteration, the greedy strategy utilizes a pre-defined indicator function to evaluate which rule is the best and then adds it to the rule set. However, in reality, the indicator function is designed manually and requires much domain knowledge and expert experience. Worse still, we need to design a suitable indicator for every situation, which is tedious and time-consuming work. This motivates us to propose a self-adaptive indicator that can be adapted to different situations without too much human intervention. In this paper, we see the indicator as a weighted sum of several sub-indicators. By tuning the weights, the indicator can be adapted to different situations automatically. That is, we transform this indicator selection problem into a weights tuning problem. To find the best weight of self-adaptive indicators, machine learning methods and black-box optimization are utilized. The experimental results demonstrated that our self-adaptive indicator can select a better rule set to identify more high-risk users compared to the human-defined indicator.
Year
DOI
Venue
2022
10.1109/COMPSAC54236.2022.00249
2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022)
Keywords
DocType
Citations 
indicator selection, black-box optimization, machine learning, credit risk assessment
Conference
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Yongfeng Gu100.34
Yue Ning200.34
Hao Ding300.34
Kecai Gu400.34
Daohong Jian500.34
Zhou Xu600.34
Hua Wu700.34
Jun Zhou800.68