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Probability of Loan Default—Applying Data Analytics to Financial Credit Risk Prediction

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Intelligent Systems, Technologies and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1353))

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Abstract

In the banking industry, one of the important issues is how to establish credit worthiness of potential clients. With the possibility of collecting digital records of results of past credit applications (of all clients), it can be stipulated that machine learning techniques can be used in “credit decision support” systems. There exists a substantial body of literature devoted to this subject. Moreover, benchmark datasets have been proposed, to establish effectiveness of proposed credit risk assessment approaches. The aim of this work is to compare performance of seven different classifiers, applied to two different benchmark datasets. Moreover, capabilities of, recently introduced, methods for combining results from multiple classifiers, into a meta-classifier, will be evaluated.

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Notes

  1. 1.

    https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier.

  2. 2.

    https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html?highlight=svm#sklearn.svm.LinearSVC.

  3. 3.

    https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.

  4. 4.

    https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html?highlight=random%20forest#sklearn.ensemble.RandomForestClassifier.

  5. 5.

    https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier.

  6. 6.

    https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html?highlight=adaboost#sklearn.ensemble.AdaBoostClassifier.

  7. 7.

    https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn.

  8. 8.

    https://www.openml.org/d/260.

  9. 9.

    https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv.

  10. 10.

    https://github.com/hyperopt/hyperopt.

  11. 11.

    good—debtors not delayed in repayment.

  12. 12.

    bad—debtors delayed in repayment.

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Author information

Authors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Aleksandra Łuczak & Maria Ganzha

  2. Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

Authors
  1. Aleksandra Łuczak
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  2. Maria Ganzha
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  3. Marcin Paprzycki
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Corresponding author

Correspondence to Marcin Paprzycki .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  2. Indian Institute of Information Technology and Management Kerala, Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sushmita Mitra

  4. Simon Fraser University, Burnaby, BC, Canada

    Ljiljana Trajkovic

  5. College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Łuczak, A., Ganzha, M., Paprzycki, M. (2021). Probability of Loan Default—Applying Data Analytics to Financial Credit Risk Prediction. In: Paprzycki, M., Thampi, S.M., Mitra, S., Trajkovic, L., El-Alfy, ES.M. (eds) Intelligent Systems, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 1353. Springer, Singapore. https://doi.org/10.1007/978-981-16-0730-1_1

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