Credit Risk Assessment using Machine Learning Techniques

Aithal, Varsha; Jathanna, Roshan David

doi:10.35940/ijitee.a4936.119119

Cited by 14 publications

(3 citation statements)

References 14 publications

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“…In the following, the most important and commonly used ones for classification problems are presented [11]: Many actual studies use all these evaluation metrics and others to choose the best one. Authors of [27] choose accuracy to conclude that random forest delivered the best results compared to other algorithms. [4] a metric according to profit for peer-to-peer lending as accuracy can be non-sufficient.…”

Section: B Related Work: Algorithms and Metricsmentioning

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: B Related Work: Algorithms and Metricsmentioning

confidence: 99%

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

BERRADA,

BARRAMOU,

ALAMI

2024

IJACSA

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“…For credit risk assessment and prediction, Aithal & Jathanna ( 2019 ) compared algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART). They found that the most suitable algorithm is Random Forest algorithm, which can predict credit risk of banks with the highest accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis

2023

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This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research’s status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance.

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“…The third research, named Credit Risk Assessment using Machine Learning Techniques, focused on the accuracy of credit evaluation by utilizing different machine learning algorithms. Aithal, V and Jathanna, R. D [4] aimed to discover the best analysis model in order to determine credibility. The methodologies, which they selected, are Logistic Regression, Random Forest, Neural Network, Support Vector Network, Naïve Bayes Classifier, and Classification and Regression Trees.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Various Machine Learning Techniques in Credit Risk Area

Li¹

2023

BCPBM

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Implementing machine learning techniques to credit scoring is a popular method, which is widely used by many financial institutions and banks at present. As the fast development of machine learning tools, these technologies could provide people more accurate predictions and help enterprises avoid future risk. A supervised machine learning technique is utilized in this research as the classification approach. In this experiment, several machine learning algorithms will be compared in order to present the performance by evaluating the type of credit risk. The data is about assessing customers of a German banking systems from the UCI Machine Learning Repository, which contains 5000 instances and 21 attributes. The final result of this research shows the comparison of 12 scenarios among different combinations of balancing methods, feature selection methods, and predictive algorithms, which finally presents that the collection of Adaptive Synthetic, Boruta and k-Nearest Neighbor receives the highest accuracy score.

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Credit Risk Assessment using Machine Learning Techniques

Cited by 14 publications

References 14 publications

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

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

Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis

Evaluating Various Machine Learning Techniques in Credit Risk Area

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