Predicting acceptance of the bank loan offers by using support vector machines

AKÇA, Mehmet Furkan; Sevli, Onur

doi:10.35860/iarej.1058724

Cited by 5 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…That is mainly because, MSMOTE not only considers the distribution of minority classes but also rejects latent noise spots based on K-NN classi er method [21]. Experimental results also indicated that the MSMOTE algorithm used can result in better prediction of the minority class than SMOTE [22]. Moreover, MSMOTE when used with bagging based ensemble classi er it gives better accuracy, precision, F1-score, and recall than when it is used with boosting ensemble classi ers.…”

Section: Logistic Regressionmentioning

confidence: 97%

Bank Loan Classification of Imbalanced Dataset Using Machine Learning Approach

Babo

Beyene

2023

Preprint

View full text Add to dashboard Cite

Before giving loans to borrowers, banks decide whether the borrower is bad (defaulter) or good (non-defaulter). The prediction of borrower status whether the borrower will be a defaulter or a non-defaulter is not an easy task to the loan providing entity. In machine learning, building an automated loan default classification system is an optimization problem with an ultimate objective of improving loaner classification in loan decision making. However, this problem becomes difficult when there is a profile of imbalanced data since the classifier may misclassify the rare samples from the minority class. To solve this problem, we used a Modified Synthetic Minority Oversampling Technique (MSMOTE). It is an oversampling technique where synthetic data of the minority class is generated to balance with the majority class. This is combined with ensemble classifier technique to further improve the performance of bank loan prediction systems. MSMOTE is a variant of Synthetic Minority Oversampling Technique (SMOTE) algorithm. Bagging- and boosting- based ensemble techniques are applied on the imbalanced dataset to improve the performance of loan prediction. The dataset is gathered from Kaggle to validate the proposed scheme. Experimental results show that, among others, the proposed model, MSMOTE, when combined with adaptive boosting resulted in 95% of precision and accuracy. Whereas, MSMOTE combined with Bagging and Random Forest resulted in 99% of precision and accuracy.

show abstract

Section: Logistic Regressionmentioning

confidence: 97%

Bank Loan Classification of Imbalanced Dataset Using Machine Learning Approach

Babo

Beyene

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Regression, on the other hand, focuses on predicting a continuous numerical output associated with input data. It is used to predict a specific value or a continuous function of an output variable [20]. In this research, supervised classification algorithms are used because credit card fraud detection requires real-time intervention and requires identification between two main classes: fraudulent and non-fraudulent transactions.…”

Section: Algorithms Utilizedmentioning

confidence: 99%

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

Sinap

2024

Turkish Journal of Engineering

View full text Add to dashboard Cite

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.

show abstract

“…Data yang digunakan adalah data sekunder berisi informasi status peminjaman bank yang diperoleh dari [8] dan telah digunakan dalam penentuan prediksi penerimaan tawaran pinjaman dari bank menggunakan Support Vector Machines [9], dataset ini berisi informasi tentang status peminjaman atau pinjaman yang diberikan oleh sebuah bank kepada nasabahnya dengan jumlah 5000 nasabah dengan 12 variabel sebagai pelengkap data diri atau atribut, yang selanjutnya akan dibagi guna keperluan pengolahan dengan 70% untuk training dan 30% untuk pengujian.…”

Section: Metode 21 Dataunclassified

“…Pada beberapa penelitian sebelumnya evaluasi hasil percobaan dilakukan dengan menghitung nilai error yang diperoleh dari evaluasi nilai bobot. Pada penelitian ini untuk mengevaluasi hasil yang didapatkan, digunakan perhitungan Accuracy, Precision, Recall setelah diperoleh Confusion matrix [9]. Matriks ini membantu dalam mengukur sejauh mana model dapat memprediksi dengan benar kelaskelas target dari dataset yang diberikan.…”

Section: Evaluasiunclassified

Optimasi Parameter Artificial Neural Network (ANN) Menggunakan Particle Swarm Optimization (PSO) Untuk Pengkategorian Nasabah Bank

Larwuy

2024

JMKS

View full text Add to dashboard Cite

Artificial Neural Network (ANN), also known as Jaringan Saraf Tiruan, is one of the methods commonly used for pattern recognition, classification, forecasting, and regression, depending on the problem or data used. While the results obtained are generally good, there are often issues with determining the initial parameters as the initial weights, which can lead to non-convergence of results. This is why a method is needed to optimize the ANN parameters to achieve better outcomes. Particle Swarm Optimization (PSO) was chosen as the method to optimize the ANN parameters (PSO-ANN). The best parameter values for PSO were predefined, with w (inertia weight) set to 0.8 and c1 and c2 (acceleration coefficients) set to 1.5. Subsequently, PSO-ANN was trained using a bank customer dataset to determine the categories of customers with credit problems or not. The results were compared with using ANN without parameter optimization. The obtained results showed an Accuracy rate of 82.6%, Precision of 91.1%, and Recall of 37.1%. This represents an improvement compared to the results of ANN without parameter optimization, which had an Accuracy rate of 80.1%, Precision of 89.5%, and Recall of 32.4%.

show abstract

Predicting acceptance of the bank loan offers by using support vector machines

Cited by 5 publications

References 13 publications

Bank Loan Classification of Imbalanced Dataset Using Machine Learning Approach

Bank Loan Classification of Imbalanced Dataset Using Machine Learning Approach

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

Optimasi Parameter Artificial Neural Network (ANN) Menggunakan Particle Swarm Optimization (PSO) Untuk Pengkategorian Nasabah Bank

Contact Info

Product

Resources

About