APPLICATION OF DECISION TREES IN CREDIT SCORING

被引:0
|
作者
Kvesic, Ljiljanka [1 ]
机构
[1] Sveuciliste Mostaru, Fak Prirodoslovno Matemat & Odgojnih Znanosti, Matice Hrvatske Bb, Mostar 88000, Bosnia & Herceg
来源
EKONOMSKI VJESNIK | 2013年 / 26卷 / 02期
关键词
credit scoring; model; decision tree; exhaustive CHAID algorithm;
D O I
暂无
中图分类号
F [经济];
学科分类号
02 ;
摘要
Banks are particularly exposed to credit risk due to the nature of their operations. Inadequate assessment of the borrower directly causes losses. The financial crisis the global economy is still going through has clearly shown what kind of problems can arise from an inadequate credit policy. Thus, the primary task of bank managers is to minimise credit risk. Credit scoring models were developed to support managers in assessing the creditworthiness of borrowers. This paper presents the decision tree based on exhaustive CHAID algorithm as one such model. Since the application of credit scoring models has not been adequately explored in the Croatian banking theory and practice, this paper aims not only to determine the characteristics that are crucial for predicting default, but also to highlight the importance of a quantitative approach in assessing the creditworthiness of borrowers.
引用
收藏
页码:382 / 391
页数:5
相关论文
共 50 条
  • [31] Application of Support Vector Machines Method in Credit Scoring
    Zhang, Leilei
    Hui, Xiaofeng
    [J]. SIXTH INTERNATIONAL SYMPOSIUM ON NEURAL NETWORKS (ISNN 2009), 2009, 56 : 283 - 290
  • [32] Application of selected scoring models on corporate credit rating
    Novotna, Martina
    [J]. PROCEEDINGS OF THE 29TH INTERNATIONAL CONFERENCE ON MATHEMATICAL METHODS IN ECONOMICS 2011, PTS I AND II, 2011, : 511 - 516
  • [33] A federated interpretable scorecard and its application in credit scoring
    Zheng, Fanglan
    Erihe
    Li, Kun
    Tian, Jiang
    Xiang, Xiaojia
    [J]. INTERNATIONAL JOURNAL OF FINANCIAL ENGINEERING, 2021, 8 (03)
  • [34] A literature review on the application of evolutionary computing to credit scoring
    Marques, A. I.
    Garcia, V.
    Sanchez, J. S.
    [J]. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2013, 64 (09) : 1384 - 1399
  • [35] Application of the Scoring Model for Assessing the Credit Rating of Principals
    Janeska, Margarita
    Sotiroski, Kosta
    Taleska, Suzana
    [J]. TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS, 2014, 3 (01): : 50 - 54
  • [36] Bayesian data mining, with application to benchmarking and credit scoring
    Giudici, P
    [J]. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 2001, 17 (01) : 69 - 81
  • [37] Credit scoring
    Crook, JN
    Edelman, DE
    Thomas, LC
    [J]. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2005, 56 (09) : 1003 - 1005
  • [38] An interpretable decision tree ensemble model for imbalanced credit scoring datasets
    My, Bui T. T.
    Ta, Bao Q.
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 45 (06) : 10853 - 10864
  • [39] Principal Component Analysis and ReliefF Cascaded with Decision Tree for Credit Scoring
    Damrongsakmethee, Thitimanan
    Neagoe, Victor-Emil
    [J]. ARTIFICIAL INTELLIGENCE METHODS IN INTELLIGENT ALGORITHMS, 2019, 985 : 85 - 95
  • [40] Credit scoring based on a Bagging-cascading boosted decision tree
    Zou, Yao
    Gao, Changchun
    Xia, Meng
    Pang, Congyuan
    [J]. INTELLIGENT DATA ANALYSIS, 2022, 26 (06) : 1557 - 1578