Credit scoring methods: Latest trends and points to consider

Markov, Anton; Seleznyova, Zinaida; Lapshin, Victor

doi:10.1016/j.jfds.2022.07.002

Cited by 34 publications

(11 citation statements)

References 127 publications

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“…A very interesting literature review held in [28] analyses and figures out the metrics tested and used by different articles studied. This article also tackles a specific issue concerning the evolution of credit scoring evaluation and research studies still lacking today.…”

Section: Zhang Et Al Presented In Their Articlementioning

confidence: 99%

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

BERRADA,

BARRAMOU,

ALAMI

2024

IJACSA

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As the core business of the banking system is to lend money and then get it back, loan default is one of the most crucial issues for commercial banks. With data analysis and artificial intelligence, extracting valuable information from historical data, to lower their losses, banks would be able to classify their customers and predict the probability of credit repayment instead of relying on traditional methods. As most actual research is focused on individuals' loans, the novelty of the present paper is to treat corporate loans. Its main objective is to propose a model to address the problem using selected machine learning algorithms to classify companies into two classes to be able to predict loan defaulters. This paper delves into the Corporate Loan Default Prediction Model (CLD PM), which is designed to forecast loan defaults in corporations. The model is grounded in the CRISP-DM process, commencing with comprehending corporate requirements and implementing classification techniques. The data acquisition and preparation phase are critical in testing the selected algorithms, which involve Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, XGBoost, and Adaboost. The model's efficacy is assessed using various metrics, namely Accuracy, Precision, Recall, F1 score, and AUC. Subsequently, the model is scrutinized using an actual dataset of loans for Moroccan real estate firms. The findings reveal that the Random Forest and XGBoost algorithms outperformed the others, with every metric surpassing 90%. This was accomplished by utilizing SMOTE as an oversampling method, given the dataset's imbalance. Furthermore, when concentrating on financial statements, selecting the five most significant financial ratios and the company's age, Random Forest was adept at predicting defaulters with good results: accuracy of 90%, precision of 75%, recall of 50%, F1 score of 60% and AUC of 77%.

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Section: Zhang Et Al Presented In Their Articlementioning

confidence: 99%

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

BERRADA,

BARRAMOU,

ALAMI

2024

IJACSA

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“…These benchmark datasets, extensively utilized in credit risk research over decades, were readily accessible, facilitating the replication of our experiment. In contrast, private datasets, notably those from Lending Club, have gained popularity in the last 5 years due to their richer feature sets and larger data volumes [47], enabling the training of more complex models, each distinct in terms of size, feature dimension, and IR. Notably, the LC dataset stands out as an example of an extremely imbalanced and large dataset.…”

Section: Datasetsmentioning

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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“…Credit scoring (skor kredit) adalah metode yang sering dipakai untuk mengevaluasi gagal bayar atau resiko kredit kepada customer [6]. Risiko kredit adalah risiko dampak paling signifikan bagi bank atau lembaga keuangan mana pun, penilaian risiko kredit yang akurat memengaruhi neraca dan laporan laba rugi organisasi, bahkan mungkin memengaruhi domain yang tampaknya tidak terkait, misalnya seperti pemasaran dan pengambilan keputusan [7], akibatnya pemberian kredit tanpa adanya metode prediksi yang akurat akan sangat berisiko terhadapat calon debitur gagal dalam melakukan pembayaran kredit yang akhirnya lembaga keuangan akan merugi.…”

Section: Pendahuluanunclassified

Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring

Naufal,

Suseno

2023

JoSYC

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After the Covid-19 pandemic, the banking sector faced significant challenges in contributing to achieving national goals in terms of increasing living standards and equalizing the regional economy. Hundreds of millions of low-income people have no credit or bank accounts because they do not have sufficient credit history to warrant the credit scores assigned to them. An estimated 1.7 billion people (31% of the adult population) do not have an account with a financial institution. People today are usually concentrated in developing countries, especially in China 204 million, India 357 million and Indonesia 102 million people. Because it is very difficult to make accurate predictions in determining credit worthiness for low-income people. Cooperatives are financial institutions that have a crucial role in channeling financing to members and the community to develop their businesses. An inappropriate credit distribution process can have a negative effect on KSP, resulting in frequent losses. This risk is known as problem loans, the cause is the KSP's failure to analyze the credit of prospective debtors. Therefore, calculations are needed to detect opportunities for credit risk default by prospective debtors objectively and precisely so that loan problems do not occur. Credit scoring is a method used to evaluate credit risk in terms of loan applications from consumers [4]. In this research we will provide a solution using classification techniques with feature selection methods in the Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) to predict the credit risk of prospective debtors failing to make loan payments. The application of the SVM algorithm in credit scoring research is because SVM is good at data classification. However, the standard SVM model still does not produce optimal results due to the difficulty of determining the best parameters, therefore researchers will optimize it with the Feature Selection and PSO algorithms to determine the best parameters. The results from the combination of several parameters using PSO-SVM obtained an accuracy of 87.23%, therefore the application of this method was proven to improve the performance of the SVM algorithm to increase its accuracy results in predicting the feasibility of granting credit.

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Credit scoring methods: Latest trends and points to consider

Cited by 34 publications

References 127 publications

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction

Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring

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