Credit risk prediction in an imbalanced social lending environment

Namvar, Anahita; Siami, Mohammad; Rabhi, Fethi A.; Naderpour, Mohsen

doi:10.48550/arxiv.1805.00801

Cited by 2 publications

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“…Maximum likelihood estimate is used to find the coefficients. As such, logistic regression, when applied to a set of features Xi (i=1,...,n), predicts the likelihood that an ensemble will belong to a specific class [7]. Eq.…”

Section: Modelsmentioning

confidence: 99%

Analysis of Credit Default Prediction Based on Logistic Regression, Random Forest and KNN Model

Wen

2024

AEMPS

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Addressing the prevalent issue of limited access to loans for individuals with inadequate or non-existent credit histories is imperative to foster financial inclusion and safeguard vulnerable populations from unscrupulous lenders. With the help of unique characteristics, this research seeks to improve financial accessibility for the unbanked population by creating a safe and satisfying financing environment. The paper uses an imbalanced dataset that was gathered from Kaggle to examine how machine learning techniques can be applied to forecast credit risk. specifically, Logistic Regression, Random Forest, KNN, and Synthetic Minority Over-sampling Technique (SMOTE) are utilized in this study. The findings reveal an enhancement in accuracy following the resolution of the imbalanced dataset challenge. By employing these machine learning models, the research seeks to not only bridge the credit gap for the underserved but also mitigate the risks associated with lending to individuals lacking conventional credit histories. A thorough strategy for managing credit risk in the context of financial inclusion is shown by the investigation of different algorithms and the application of methods. These results contribute to the discourse on leveraging machine learning to create more equitable lending practices, ensuring that the unbanked population can access financial resources responsibly.

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Section: Modelsmentioning

confidence: 99%

Analysis of Credit Default Prediction Based on Logistic Regression, Random Forest and KNN Model

Wen

2024

AEMPS

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A comparative analysis of consumer credit risk models in Peer-to-Peer Lending

Trinh

2024

JEFAS

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PurposeThe purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.Design/methodology/approachThe author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.FindingsThe results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.Originality/valueThe main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

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Credit risk prediction in an imbalanced social lending environment

Cited by 2 publications

References 0 publications

Analysis of Credit Default Prediction Based on Logistic Regression, Random Forest and KNN Model

Analysis of Credit Default Prediction Based on Logistic Regression, Random Forest and KNN Model

A comparative analysis of consumer credit risk models in Peer-to-Peer Lending

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