Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods

Zhang, Xiaoming; Yu, Lean

doi:10.1016/j.eswa.2023.121484

Cited by 8 publications

(6 citation statements)

References 280 publications

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“…Classifiers were categorized into three groups: individual classifiers (logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), naïve Bayes (NB), decision tree (DT)), ensemble classifiers (random forest (RF), XGBoost (XGB), LightGBM (LGBM), CatBoost (CAT)), and balanced classifiers (balanced bagging classifier (BBC), bal-anced random forest (BRF)). The criterion for selecting classifiers is based on the summary of [43] by reviewing 281 credit-risk-models-related articles (our experiment did not include deep learning algorithms such as artificial neural network [44] and convolutional neural network, although the summary mentioned them, and the reason for their exclusion is their complexity compared with individual classifiers and the higher risk of overfitting they entail; however, their performance does not surpass that of other classifiers [45]). Subsequently, all training sets were subjected to evaluation using these 11 classifiers.…”

Section: Framework and Evaluation Metricsmentioning

confidence: 99%

Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction

Zhao,

Cui,

Ding

et al. 2024

Mathematics

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Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) are significantly fewer than negative samples (nondefaults). It poses a serious challenge known as the class imbalance problem, which can substantially impact data quality and predictive model effectiveness. To address the problem, various resampling techniques have been proposed and studied extensively. However, despite ongoing research, there is no consensus on the most effective technique. The choice of resampling technique is closely related to the dataset size and imbalance ratio, and its effectiveness varies across different classifiers. Moreover, there is a notable gap in research concerning suitable techniques for extremely imbalanced datasets. Therefore, this study aims to compare popular resampling techniques across different datasets and classifiers while also proposing a novel hybrid sampling method tailored for extremely imbalanced datasets. Our experimental results demonstrate that this new technique significantly enhances classifier predictive performance, shedding light on effective strategies for managing the class imbalance problem in credit risk prediction.

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Section: Framework and Evaluation Metricsmentioning

confidence: 99%

Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction

Zhao,

Cui,

Ding

et al. 2024

Mathematics

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“…Figure 5a shows that the validation error is higher than the fitting error, and that the BWASD requires 10 iterations to optimize the NN structure. Particularly, BWASD returned N * = [0, 0, 3, 3, 4, 6] with c * = [4,3,3,4,2,3] and p * = 0.9 for the specific run, while MWASD returned 3,4,3,4]. That is, the NN trained under BWASD has 6 hidden layer neurons, while the NN trained under MWASD has 5.…”

Section: Datasetmentioning

confidence: 99%

“…Particularly, BWASD returned N * = [0, 0, 2, 2, 2, 5, 5] with c * = [2, 1, 1, 4, 4, 2, 2] and p * = 0.95 for the specific run, while MWASD returned N * = [0, 1, 2] with c * = [4,3,4]. That is, the NN trained under BWASD has 7 hidden layer neurons, while the NN trained under MWASD has 3.…”

Section: Datasetmentioning

confidence: 99%

“…Given that credit risk emerges mostly when borrowers are unable or unwilling to pay, rigorous background screening of a customer prior to approval of a loan failing is an essential necessity to sustain oneself in such a business [1,2]. Keep in mind that the amount of non-performing loans in the economy is significant because these loans weigh on bank profits and use valuable resources, limiting banks' ability to grant new loans [3,4]. Problems in the banking sector can swiftly spread to other sections of the economy, jeopardizing employment and economic growth [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Credit and Loan Approval Classification Using a Bio-Inspired Neural Network

Mourtas,

Katsikis,

Stanimirović

et al. 2024

Biomimetics

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Numerous people are applying for bank loans as a result of the banking industry’s expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions.

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“…Their study reveals a growing preference for advanced methods like ensembles and neural networks over traditional techniques like decision trees and logistic regression, often leading to better predictive results. Zhang and Yu (2024) offer an in-depth review of consumer credit risk assessment. They pinpoint a notable gap in research about data traits and stress the importance of multiscenario modeling in machine learning.…”

Section: Credit Scoring: An Overviewmentioning

confidence: 99%

Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach

Suhadolnik,

Ueyama,

Da Silva

2023

JRFM

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Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning models in credit scoring and review current research findings. Our results indicate that ensemble models, particularly XGBoost, outperform traditional algorithms such as logistic regression in credit classification. Researchers and experts in the subject of credit risk can use this work as a practical reference as it covers crucial phases of data processing, exploratory data analysis, modeling, and evaluation metrics.

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Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods

Cited by 8 publications

References 280 publications

Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction

Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction

Credit and Loan Approval Classification Using a Bio-Inspired Neural Network

Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach

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