A Comparative Analysis of Bank Customers' Loan Propensity Using Machine Learning Methods

SARIZEYBEK, Ali Tezcan; Sevli, Onur

doi:10.38016/jista.1036047

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…This suggests that the model cannot accurately assess how well it fits real-world data. In addition, in line with the findings of this study, Sarizeybek and Sevli [47] found that the success rates obtained using 10-fold cross-validation were significantly higher than a single training-test partitioning in their study predicting customers' propensity to take loans.…”

Section: Discussion (Tartişma)supporting

confidence: 86%

“…Similar to the findings of this study, Meshref [34], in his study on loan approval prediction, found that feature selection improves the performance of machine learning models rather than using all features. Sarizeybek and Sevli [47] achieved an average performance increase of 7% with the K-Best method in their study on customers' propensity to take loans. Similarly, in this study, the performance of the models increased by about 19% when feature selection was made with the K-Best method.…”

Section: Discussion (Tartişma)mentioning

confidence: 99%

“…In this method, the dataset is divided into K parts and each part is used as a test set and the remaining part is used as a training set. In this way, K different models are created, and accuracy values are obtained [47]. In this research, the number of folds was set as 10 for all algorithms.…”

Section: XXmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

Sinap

2024

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Loan prediction plays an important role in the process of evaluating loan applications by financial institutions. Machine learning models can automate this process and make the lending process faster and more efficient. In this context, the main objective of this research is to develop models for loan approval prediction using machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest and to compare their performances. In addition, determining the effect of K-Best and Recursive Feature Elimination feature selection methods on model performances is another important objective of the research. Furthermore, the evaluation of the effectiveness of techniques such as cross-validation (K-Fold) and Train, Test and Validation in measuring the performance of models is also among the objectives of the research. The findings revealed that married individuals are more likely to be approved for loans than single individuals, high income individuals more likely than low-income individuals, males more likely than females, and university graduates more likely than non-university graduates. According to the performance measures, Random Forest was the most successful algorithm with an accuracy rate of 97.71% in loan approval prediction. To achieve this accuracy rate, feature selection was performed with the Recursive Feature Elimination method and the measurement was made with the cross-validation method. It was found that the feature selection methods have a significant impact on the model performances and the Recursive Feature Elimination method was the most successful method. Moreover, the highest accuracy rate achieved by the Random Forest algorithm, which showed the highest performance in all cases, was measured by cross-validation.

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Section: Discussion (Tartişma)supporting

confidence: 86%

Section: Discussion (Tartişma)mentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

Sinap

2024

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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“…Nevertheless, in contexts where the emphasis lies on interpretability and cost-effectiveness rather than the intricacy of deep learning models, classification algorithms arise as a more pragmatic option. The straightforwardness and efficient utilization of resources by classification algorithms render them highly suitable for scenarios with constrained computational resources or where a transparent comprehension of the decision-making process is imperative [41]. These insights actively contribute to ongoing discussions regarding the selection of appropriate methodologies in the field of fraud detection, providing valuable considerations for real-world applications.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of machine learning techniques for credit card fraud detection: Dealing with imbalanced datasets

Sinap

2024

Turkish Journal of Engineering

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The main objective of this research is to evaluate the performance of machine learning algorithms in the field of credit card fraud detection and then compare them according to various performance metrics. Seven different supervised classification algorithms including Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, K-Nearest Neighbors and Support Vector Machine were used. The performance of these algorithms was measured through a comprehensive evaluation of metrics including Accuracy, Precision, Recall, F-Score, AUC and AUPRC values. Furthermore, ROC curves and confusion matrices were used to evaluate these algorithms. The data preparation phase is critical in this study. The data imbalance problem arises as an unequal distribution between fraudulent and non-fraudulent transactions. Addressing this imbalance is imperative for successful model training and subsequent reliable results. Various techniques, such as Scaling and Distribution, Random Under-Sampling, Dimensionality Reduction, and Clustering, are employed to ensure an accurate evaluation of model performance and its ability to generalize effectively. As a result, the "Random Forest" and "K-Nearest Neighbors" algorithms exhibit the highest performance levels in this research with 97% accuracy rates. This study contributes significantly to the ongoing fight against financial fraud and provides valuable guidance for future research efforts.

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Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm

ÇİMEN,

GARİP,

YALÇIN

et al. 2023

jista

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Machine learning methods can generally be categorized as supervised, unsupervised and reinforcement learning. One of these methods, Q learning algorithm in reinforcement learning, is an algorithm that can interact with the environment and learn from the environment and produce actions accordingly. In this study, eight different on-line methods have been proposed to determine online the value of the learning parameter in the Q learning algorithm depending on different situations. In order to test the performance of the proposed methods, these algorithms are applied to Frozen Lake and Car Pole systems and the results are compared graphically and statistically. When the obtained results are examined, Method 1 has produced better performance for Frozen Lake, which is a discrete system, while Method 7 has produced better results for the Cart Pole System, which is a continuous system.

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A Comparative Analysis of Bank Customers' Loan Propensity Using Machine Learning Methods

Cited by 3 publications

References 7 publications

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

Comparative analysis of machine learning techniques for credit card fraud detection: Dealing with imbalanced datasets

Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm

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