改进的支持向量机的个人信用评估模型
华北理工大学, 冀唐学院, 河北唐山 063210
| 摘 要： | 信用消费已经成为越来越多人的选择，信用消费的膨胀在带来便利的同时，随之而来的失信问题给信托机构带来不可估量的损失，如故意欠款，恶意透支消费等，给信托机构在经营运行中造成的巨大损失。一方面，我国目前未能建立完善的个人信用记录，客户信用信息难以共享，获得相关数据的途径有限，个人征信系统不够完善，因而建立完善、自动化的个人信用评估体系，科学识别个人信用风险，从而实现银行信贷收益的最大化，是十分必要的。在银行风险管理中，面临的主要问题是如何衡量和规避银行的财务风险，而个人信用评估是其中难度最大也是最重要的一部分。本文利用XGBOOST算法对特征进行重要度排序，并根据模型准确度与特征数量之间的关系选取特征数量与具体的特征，然后将数据中的80%作为训练集带入传统的支持向量机模型中，并用剩余的数据进行测试，准确率过低，因此，在个人信用风险评估方面，对传统的支持向量机引入二次曲面，建立无核二次曲面支持向量机模型，准确率提高了9.8个百分点，对银行引用风险控制具有指导意义。 |
| 关 键 词： | 个人信贷评估; 支持向量机; 二次曲面; XGBOOST |
| DOI： | 10.57237/j.cst.2022.01.004 |
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收稿日期: 2022-10-05
接受日期: 2022-12-05
在线出版日期: 2022-12-15
Improved Support Vector Machine Personal Credit Rating Model
Ji Tang College of North China University of Science and Technology, Tangshan 063210, China
| Abstract: | Credit consumption has become the choice of more and more people. While the expansion of credit consumption brings convenience, the consequent problem of discredit brings incalculable loss to trust institutions, such as intentional arrears, malicious overdraft consumption, etc. to the trust in the operation of the huge losses caused. On the one hand, our country is unable to establish perfect personal credit record, customer credit information is difficult to share, access to relevant data is limited, personal credit system is not perfect, therefore, it is necessary to establish a perfect and automatic personal credit evaluation system, identify the personal credit risk scientifically, and realize the maximization of bank credit income. In the bank risk management, the main problem is how to measure and avoid the bank's financial risk, and personal credit assessment is the most difficult and important part. In this paper, we use the XGBOOST algorithm to sort the importance of features, and select the number of features and the specific features according to the relationship between the model accuracy and the number of features, then 80% of the data was taken into the traditional Support vector machine model as a training set and tested with the remaining data with low accuracy, so that, in terms of individual credit risk assessment, this paper introduces the quadric surface into the traditional Support vector machine and builds the kernel-free quadric surface Support vector machine model, which improves the accuracy by 9.8 percentage points and has a guiding significance for the bank reference risk control. |
| Keywords: | Personal Credit Assessment; Support Vector Machine; Quadric XGBOOST |
基金
| 1. | 河北省人力资源和社会保障研究项目 (课题编号: JRS-2022-2025). |
参考文献
| [1] | Altman E. I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy [J]. Journal of Finance, 1968 (4): 589-609. |
| [2] | Steenackers A., Goovaerts M. J. A Credit Scoring Model for Personal Loans [J]. Insurance: Mathematics and Economics, 1989 (1): 31-34. |
| [3] | 方匡南, 章贵军, 张惠颖. 基于Lasso-logistic模型信用风险预警方法 [J]. 数量经济技术经济研究, 2014 (2): 125-136. |
| [4] | Henley W. E., Hand D. J. A k-Nearest-Neighbour Classifieror Assessing Consumer Credit Risk [J]. Journal of the Royal Statistical Society, 1996 (1): 77-95. |
| [5] | Carter C., Catlett J. Assessing Credit Card Applications Using Machine Learning [J]. IEEE Expert, 1987 (3): 71-79. |
| [6] | 刘弘. 基于神经网络的贷款违约识别研究 [J]. 统计研究, 2008 (7): 61-65. |
| [7] | 庞素琳, 巩吉璋. C5.0分类算法及在银行个人信用评级中的应用 [J]. 系统工程理论与实践, 2009 (12): 94-104. |
| [8] | Koutanaci F. N., Sajedi H., Khanbabaei M. A Hybrid Data Mining Model of Feature Selection Algorithms and Ensemble Learning Classifiers for Credit Scoring [J]. Journal of Retailing and Consumer Services, 2015 (27): 11-23. |
| [9] | 莫赞, 张灿凤, 魏伟, 等.基于Bagging集成的个人信用风险评估方法研究 [J]. 系统工程, 2019 (1): 143-151. |
| [10] | 白鹏飞, 安琪, Nicolaas Frans de ROOIJ, 等. 基于多模型融合的互联网信贷个人信用评估方法 [J]. 华南师范大学学报 (自然科学版), 2017 (6): 119-123. |
| [11] | 钟金宏, 邵晶晶, 李兴国. 基于组合分类策略的个人信用风险评估研究 [J]. 合肥工业大学学报 (自然科学版), 2020 (7): 996-1002. |
| [12] | 查志成，梁雪春. 基于特征选择算法的个人信用预测模型 [J]. 计算机工程与技术，2022, 43 (06): 1678-1685. |
| [13] | 陈舒期，梁雪春, 改进的SSVM 集成算法在信用风险评估中的应用 [J]. 计算机工程与计.2019, 040 (10): 2822-2826. |
| [14] | Niu K, et al., Resampling Ensemble Model Based on Data Distribution for Imbalanced Credit Risk Evaluation in P2P Lending [J]. Information Sciences. 2020, 536. |
| [15] | 夏金莲. 我国个人征信体系建设中信用信息采集制度的构 [J]. 成都大学学报 (社会科学版), 2020 (03): 36-44. |
