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Kurumsal Kredi Riski Değerlendirmesinde Önemli Zorluklar: Bir Vaka Çalışması

Year 2024, Volume: 7 Issue: 2, 834 - 854, 11.03.2024
https://doi.org/10.47495/okufbed.1340798

Abstract

Bu makale, müşterinin temerrüde düşüp düşmediğini gösteren değişkeni tahmin ederek kurumsal kredi riskini değerlendirmeyi amaçlamaktadır. Bu amaçla kullanılan veri seti, Türkiye'de finans sektörünün önde gelen kuruluşlarından birinden temin edilmiştir. Genel olarak başvuru sahibinin verileri, kurumsal veriler, hissedar verileri ve başvuru sahibinin alacaklının kurumundaki kredi geçmişine atıfta bulunan 401 değişkenden oluşur. Diğerlerinden girdi değişkenlerini belirleyerek ve ardından bu girdileri inceleyerek güçlü bir şekilde ilişkili değişkenleri ve neredeyse tamamen eksik veya sıfır değerlerden oluşan değişkenleri kullanmaktan kaçınarak bu çok sayıda değişkeni azaltırız. Veri kümesindeki birçok değişkenin çok fazla eksik girişi vardır, ancak bunun haklı sebepleri vardır. Bu sorunu çözmek için, hangi değişken grubunun hangi müşteriyle ilişkili olduğunu yansıtan yedi alt küme oluşturduk. Veri seti dengesiz, yaklaşık %96 temerrüt dışı örneklerden ve onaylanmış krediler arasında yalnızca yaklaşık %4 temerrüt örneklerinden oluşuyor. Bu yazıda, eğitim setlerindeki örnekleri dengelemek için üç örnekleme tekniği kullanıyoruz; alt örnekleme, yüksek örnekleme ve sentetik azınlık yüksek örnekleme tekniği ve altı sınıflandırıcı uyguluyoruz; Rastgele Orman, Naif Bayes, Lojistik Regresyon, Destek Vektör Makinesi, Karar Ağacı ve K-En Yakın Komşu. Bu tekniklerin performansını ölçmek için, sırasıyla çoğunluk sınıfı ve azınlık sınıfının ne kadar iyi tahmin edildiğini ölçmek için duyarlılık ve özgüllük kullanırız. Sonuç olarak, eş zamanlı olarak %50'den fazla duyarlılık ve özgüllük elde ettik; burada alt örnekleme tekniği azınlık sınıfı için en iyi örnekleme tekniğiydi ve sentetik azınlık yüksek örnekleme tekniği ve yüksek örnekleme çoğunluk sınıfı için daha iyi performans gösterdi.

References

  • Abdou HA., Pointon J. Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent Systems in Accounting, Finance and Management 2011; 18(2-3): 59-88.
  • Chawla NV., Bowyer KW., Hall LO., Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 2002; 16: 321-357.
  • Chen S., Webb GI., Liu L., Ma X. A novel selective naïve Bayes algorithm. Knowledge-Based Systems 2020; 192: 105361.
  • De Ville B. Decision trees. Wiley Interdisciplinary Reviews: Computational Statistics 2013; 5(6): 448-455.
  • Elreedy D., Atiya AF. A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Information Sciences 2019; 505: 32-64.
  • Ghosh D., Vogt A. Outliers: An evaluation of methodologies. Joint statistical meetings, July 2012, page no: 3455-3460, United States.
  • Hearst MA., Dumais ST., Osuna E., Platt J., Scholkopf B. Support vector machines. IEEE Intelligent Systems and Their Applications 1998; 13(4): 18-28.
  • Hodge V., Austin J. A survey of outlier detection methodologies. Artificial Intelligence Review 2004; 22: 85-126. Ishwaran H. The effect of splitting on random forests. Machine Learning 2015; 99(1): 75-118.
  • Kaur G., Oberai EN. A review article on Naive Bayes classifier with various smoothing techniques. International Journal of Computer Science and Mobile Computing 2014; 3(10): 864-868.
  • Kingsford C., Salzberg SL. What are decision trees?. Nature Biotechnology 2008; 26(9): 1011-1013.
  • Kurniadi D., Abdurachman E., Warnars HLHS., Suparta W. The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm. IOP conference series: materials science and engineering, 18 April 2018, page no: 012039, Indonesia.

Crucial Challenges In Corporate Credit Risk Assessment: A Case Study

Year 2024, Volume: 7 Issue: 2, 834 - 854, 11.03.2024
https://doi.org/10.47495/okufbed.1340798

Abstract

This article aims to assess corporate credit risk by predicting the variable that indicates whether the customer has defaulted or not. The dataset used for this purpose is obtained from one of the leading institutions in the finance sector in Türkiye. It consists of 401 variables generally referring to the applicant's data, corporate data, shareholder data, and the applicant's credit history within the creditor's institution. We reduce this large number of variables by identifying the input variables from the others and then studying those inputs to avoid using strongly correlated variables and variables consisting almost entirely of missing or zero values. Many variables in the dataset have too many missing entries but for justifiable reasons. To solve this issue, we created seven subsets to reflect which group of variables relates to which customer. The dataset is imbalanced, consisting of about 96% non-default instances and only around 4% default instances among approved loans. In this paper, we use three sampling techniques to balance the instances in the training sets; under-sampling, oversampling, and synthetic minority oversampling technique, and we apply six classifiers; Random Forest, Naïve Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and K-Nearest Neighbor. To measure the performance of these techniques, we use sensitivity and specificity to measure how well the majority class and minority class were respectively predicted. As a result, we simultaneously achieved greater than 50% sensitivity and specificity, where the under-sampling technique was the best sampling technique for the minority class, and the synthetic minority oversampling technique and oversampling performed better for the majority class.

References

  • Abdou HA., Pointon J. Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent Systems in Accounting, Finance and Management 2011; 18(2-3): 59-88.
  • Chawla NV., Bowyer KW., Hall LO., Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 2002; 16: 321-357.
  • Chen S., Webb GI., Liu L., Ma X. A novel selective naïve Bayes algorithm. Knowledge-Based Systems 2020; 192: 105361.
  • De Ville B. Decision trees. Wiley Interdisciplinary Reviews: Computational Statistics 2013; 5(6): 448-455.
  • Elreedy D., Atiya AF. A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Information Sciences 2019; 505: 32-64.
  • Ghosh D., Vogt A. Outliers: An evaluation of methodologies. Joint statistical meetings, July 2012, page no: 3455-3460, United States.
  • Hearst MA., Dumais ST., Osuna E., Platt J., Scholkopf B. Support vector machines. IEEE Intelligent Systems and Their Applications 1998; 13(4): 18-28.
  • Hodge V., Austin J. A survey of outlier detection methodologies. Artificial Intelligence Review 2004; 22: 85-126. Ishwaran H. The effect of splitting on random forests. Machine Learning 2015; 99(1): 75-118.
  • Kaur G., Oberai EN. A review article on Naive Bayes classifier with various smoothing techniques. International Journal of Computer Science and Mobile Computing 2014; 3(10): 864-868.
  • Kingsford C., Salzberg SL. What are decision trees?. Nature Biotechnology 2008; 26(9): 1011-1013.
  • Kurniadi D., Abdurachman E., Warnars HLHS., Suparta W. The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm. IOP conference series: materials science and engineering, 18 April 2018, page no: 012039, Indonesia.
There are 11 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section RESEARCH ARTICLES
Authors

Btıssam Hajjaouı 0000-0003-0851-3428

Publication Date March 11, 2024
Submission Date August 10, 2023
Acceptance Date January 11, 2024
Published in Issue Year 2024 Volume: 7 Issue: 2

Cite

APA Hajjaouı, B. (2024). Crucial Challenges In Corporate Credit Risk Assessment: A Case Study. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 7(2), 834-854. https://doi.org/10.47495/okufbed.1340798
AMA Hajjaouı B. Crucial Challenges In Corporate Credit Risk Assessment: A Case Study. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. March 2024;7(2):834-854. doi:10.47495/okufbed.1340798
Chicago Hajjaouı, Btıssam. “Crucial Challenges In Corporate Credit Risk Assessment: A Case Study”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 7, no. 2 (March 2024): 834-54. https://doi.org/10.47495/okufbed.1340798.
EndNote Hajjaouı B (March 1, 2024) Crucial Challenges In Corporate Credit Risk Assessment: A Case Study. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 7 2 834–854.
IEEE B. Hajjaouı, “Crucial Challenges In Corporate Credit Risk Assessment: A Case Study”, Osmaniye Korkut Ata University Journal of Natural and Applied Sciences, vol. 7, no. 2, pp. 834–854, 2024, doi: 10.47495/okufbed.1340798.
ISNAD Hajjaouı, Btıssam. “Crucial Challenges In Corporate Credit Risk Assessment: A Case Study”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 7/2 (March 2024), 834-854. https://doi.org/10.47495/okufbed.1340798.
JAMA Hajjaouı B. Crucial Challenges In Corporate Credit Risk Assessment: A Case Study. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2024;7:834–854.
MLA Hajjaouı, Btıssam. “Crucial Challenges In Corporate Credit Risk Assessment: A Case Study”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 7, no. 2, 2024, pp. 834-5, doi:10.47495/okufbed.1340798.
Vancouver Hajjaouı B. Crucial Challenges In Corporate Credit Risk Assessment: A Case Study. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2024;7(2):834-5.

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