Profit- and risk-driven credit scoring under parameter uncertainty: A multiobjective approach
针对信用评分中成本和收益参数不确定的问题，提出基于最坏情况期望最小成本和最坏情况条件风险价值的多目标特征选择框架，实验表明该方法在利润和风险指标上优于传统方法。
Profit-driven artificial intelligence (AI) systems and profit-based performance measures are widely used in credit scoring. When assessing the performance of an AI system for credit scoring, previous research typically assumes that the cost and benefit parameters and their distributional information are available. In reality, however, these parameters and their distributions are often not precisely known. This study considers parameter uncertainty in the development of credit-scoring models and the estimation of profits and risks generated by those models. We propose a novel profit-based metric—the worst-case expected minimum cost (WEMC)—to estimate the profit of credit-scoring models with uncertain parameters. Furthermore, we introduce the worst-case conditional value-at-risk (WCVaR) metric to measure the loss incurred from employing a classification model in credit scoring under the deterioration of cost parameters. A multiobjective feature-selection framework based on WEMC and WCVaR is then presented for model development. Using a comprehensive bankruptcy database, we compare the proposed methods with wrapper methods that use traditional metrics as selection criteria, as well as filter and embedding methods. We conduct extensive experiments to evaluate the economic benefits of the proposed methods under different scenarios that simulate dynamic changes in macroeconomic conditions. The results suggest that the proposed methods outperform other feature-selection methods in the aspects of profit and risk performance metrics in most cases.