A Cost-Sensitive Logistic Regression Credit Scoring Model Based on Multi-Objective Optimization Approach

Abstract

Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models.

Financial Distress Prediction for Small and Medium Enterprises Using Machine Learning Techniques

“…Studies related to credit risk modelling have significantly grown during the past 50 years, with most notable breakthroughs by Altman (1968) and Merton (1974). For the last 20 years classical modelling techniques have been challenged, as researchers have been discussing modelling credit risk by applying different machine learning techniques (Qu et al, 2019;Atiya, 2001;Pickert, 2017;Shen et al, 2020;Uthayakumar et al, 2020). One of the first machine learning tool application in financial distress prediction was by Atiya (2001) who used Artificial Neural Networks in classic credit risk prediction model developed by Merton (1974).…”

Collection Scoring Models Development and Research Based on the Deductor Analytical Platform

“…where qthe probability of «bad» credit outcome. This approach is called logistic regression (Shen et al, 2020). The р function varies in the interval from -∞ to + ∞ (Fig.…”

“…Efficiency in this case is time and material costs reduction for working with borrowers who have overdue credit debt. The collection activity optimization is possible due to the modern methods of intellectual analysis of accumulated data use Dela Cruz Galapon, 2020) and the effective collection scoring models construction (Shen et al, 2020;Terko et al, 2019). Such models are able to minimize the time spent working with clients to collect overdue debts, and the total time spent on activities, and maximize profits from collection activities.…”

A Dynamic Credit Scoring Model Based on Survival Gradient Boosting Decision Tree Approach

“…(4) Misclassification cost. Since cost-sensitivity usually occurs in credit scoring, accuracy can seldom provide an overall evaluation on the label prediction (Shen et al, 2020). As a result, we follow Lohmann and Ohliger (2019) to evaluate the capability of label prediction by misclassification cost defined as follows:…”

“…In a highly imbalanced dataset, credit scoring model tends to predict all the applications as the majority class (usually non-default) and thus lacks the capability to discriminate risky ones (Sahin et al, 2013). Cost-sensitive learning is a solution to imbalanced dataset, which can be roughly divided into the direct method and indirect ones (Shen et al, 2020;Xia et al, 2017b). The direct cost-sensitive learning methods design models that are cost-sensitive in themselves, whereas the indirect methods transform the cost-insensitive models into cost-sensitive one by sampling or thresholding.…”