Deep learning for credit scoring: Do or don't?
Developing accurate analytical credit scoring models has become a major focus for financial
institutions. For this purpose, numerous classification algorithms have been proposed for …
institutions. For this purpose, numerous classification algorithms have been proposed for …
Statistical and machine learning models in credit scoring: A systematic literature survey
In practice, as a well-known statistical method, the logistic regression model is used to
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …
Credit scoring with a feature selection approach based deep learning
VS Ha, HN Nguyen - MATEC web of conferences, 2016 - matec-conferences.org
In financial risk, credit risk management is one of the most important issues in financial
decision-making. Reliable credit scoring models are crucial for financial agencies to …
decision-making. Reliable credit scoring models are crucial for financial agencies to …
A deep learning approach for credit scoring using credit default swaps
Abstract After 2007–2008 crisis, it is clear that corporate credit scoring is becoming a key
role in credit risk management. In this paper, we investigate the performances of credit …
role in credit risk management. In this paper, we investigate the performances of credit …
Deep learning vs. gradient boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring
M Schmitt - arXiv preprint arXiv:2205.10535, 2022 - arxiv.org
Artificial intelligence (AI) and machine learning (ML) have become vital to remain
competitive for financial services companies around the globe. The two models currently …
competitive for financial services companies around the globe. The two models currently …
Credit scoring: a review on support vector machines and metaheuristic approaches
RY Goh, LS Lee - Advances in Operations Research, 2019 - Wiley Online Library
Development of credit scoring models is important for financial institutions to identify
defaulters and nondefaulters when making credit granting decisions. In recent years …
defaulters and nondefaulters when making credit granting decisions. In recent years …
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
Many years have passed since Baesens et al. published their benchmarking study of
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …
Making deep learning-based predictions for credit scoring explainable
Credit scoring has become an important risk management tool for money lending
institutions. Over the years, statistical and classical machine learning models have been the …
institutions. Over the years, statistical and classical machine learning models have been the …
A comparison of data mining techniques for credit scoring in banking: A managerial perspective
Credit scoring is a very important task for lenders to evaluate the loan applications they
receive from consumers as well as for insurance companies, which use scoring systems …
receive from consumers as well as for insurance companies, which use scoring systems …
A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification
Credit scoring focuses on the development of empirical models to support the financial
decision‐making processes of financial institutions and credit industries. It makes use of …
decision‐making processes of financial institutions and credit industries. It makes use of …