An explainable credit scoring framework: A use case of addressing challenges in applied machine learning

Name: An explainable credit scoring framework: A use case of addressing challenges in applied machine learning
Author: Güntay, Levent, Bozan, E., Tigrak, U., Durdu, T., Ozkahya, G. E.

İsim	An explainable credit scoring framework: A use case of addressing challenges in applied machine learning
Yazar	Güntay, Levent, Bozan, E., Tigrak, U., Durdu, T., Ozkahya, G. E.
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Credit scoring, Explainable model, Machine learning, Surrogate modeling
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166548313-1
Kayıt Numarası	d297c6c9-3c40-4f6b-a98d-f9defd801126
Lokasyon	International Finance
Tarih	2022
Örnek Metin	While Machine Learning (ML) classification algorithms can accurately classify a borrower's credit risk, the determinants of the credit score cannot be interpreted clearly by customers, decision makers and auditors. The lack of transparency of black-box credit scoring mechanisms reduces the trust in the banking system and has serious implications for the financing and growth of businesses. Recent regulations in the European Union and the United States require that credit decision mechanism should by explainable and transparent. We present a framework for developing an explainable credit scoring model. Our scientific novelty is to follow a simple and parsimonious Surrogate approach for credit scoring. This approach estimates an explainable white-box model that effectively fits to the in-sample forecasts of the most accurate 'black-box' model. We implement the Surrogate credit risk framework using check transactions data provided by a Turkish bank. We find that the Surrogate tree's performance is sufficiently close to performance of the most accurate black-box XGBoost model. Overall, our findings show that it is possible to develop a high-performing explainable credit scoring model with a minimal decrease in model accuracy.
DOI	10.1109/TEMSCONEUROPE54743.2022.9802029

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

An explainable credit scoring framework: A use case of addressing challenges in applied machine learning

Yazar Güntay, Levent, Bozan, E., Tigrak, U., Durdu, T., Ozkahya, G. E.

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Credit scoring, Explainable model, Machine learning, Surrogate modeling

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166548313-1

Kayıt Numarası d297c6c9-3c40-4f6b-a98d-f9defd801126

Lokasyon International Finance

Tarih 2022

Örnek Metin While Machine Learning (ML) classification algorithms can accurately classify a borrower's credit risk, the determinants of the credit score cannot be interpreted clearly by customers, decision makers and auditors. The lack of transparency of black-box credit scoring mechanisms reduces the trust in the banking system and has serious implications for the financing and growth of businesses. Recent regulations in the European Union and the United States require that credit decision mechanism should by explainable and transparent. We present a framework for developing an explainable credit scoring model. Our scientific novelty is to follow a simple and parsimonious Surrogate approach for credit scoring. This approach estimates an explainable white-box model that effectively fits to the in-sample forecasts of the most accurate 'black-box' model. We implement the Surrogate credit risk framework using check transactions data provided by a Turkish bank. We find that the Surrogate tree's performance is sufficiently close to performance of the most accurate black-box XGBoost model. Overall, our findings show that it is possible to develop a high-performing explainable credit scoring model with a minimal decrease in model accuracy.

DOI 10.1109/TEMSCONEUROPE54743.2022.9802029