Multi-Class Support Vector Machine for Credit Scoring

2012 ◽  
Vol 235 ◽  
pp. 419-422 ◽  
Author(s):  
Bo Tang ◽  
Sai Bing Qiu
Keyword(s):  
Support Vector Machine ◽  
Credit Scoring ◽  
Real Life ◽  
Assessment Model ◽  
Behavior Assessment ◽  
Support Vector ◽  
Classification Problems ◽  
Scoring Model ◽  
Multiple Classification ◽  
Credit Scoring Model

The general credit scoring model is to solve the two classification problems, but in real life we often encounter multiple classification problems. This paper proposes a multi-class support vector machine, which can solve multiple classification problems in the behavior assessment model.

An Improved Support Vector Machine for Credit Scoring

2014 ◽  
Vol 513-517 ◽  
pp. 4407-4410 ◽  
Author(s):  
Bo Tang ◽  
Sai Bing Qiu
Keyword(s):  
Support Vector Machine ◽  
Credit Scoring ◽  
Real Life ◽  
Assessment Model ◽  
Support Vector ◽  
Classification Problems ◽  
Scoring Model ◽  
Multiple Classification ◽  
Improved Support Vector Machine ◽  
Credit Scoring Model

With the development of Chinas economy, credit scoring has become important. The general credit scoring model is to solve the two classification problems, but in real life we often encounter multiple classification problems. This paper proposes a multi-class support vector machine based on genetic algorithm, which can solve multiple classification problems in the behavior assessment model.

scholarly journals A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pranith Kumar Roy ◽  
Krishnendu Shaw
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Credit Scoring ◽  
Real Life ◽  
Financial Data ◽  
Loan Pricing ◽  
Scoring Model ◽  
Record Keeping ◽  
Return On Capital ◽  
Credit Scoring Model

AbstractSmall- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

scholarly journals Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization

2021 ◽  
Vol 11 (6) ◽  
pp. 5477
Author(s):  
Wirot Yotsawat ◽  
Pakaket Wattuya ◽  
Anongnart Srivihok
Keyword(s):  
Parameter Optimization ◽  
Missing Values ◽  
Credit Scoring ◽  
Gradient Boosting ◽  
Support Vector ◽  
Scoring Model ◽  
Ensemble Models ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
Credit Scoring Model

<span>Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyper-parameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants.</span>

scholarly journals Feature Selection in a Credit Scoring Model

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 746
Author(s):  
Juan Laborda ◽  
Seyong Ryoo
Keyword(s):  
Feature Selection ◽  
Credit Scoring ◽  
Superior Performance ◽  
Filter Method ◽  
Support Vector ◽  
Classification Algorithms ◽  
Scoring Model ◽  
Stepwise Selection ◽  
Forward Stepwise ◽  
Credit Scoring Model

This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed.

Boosted Decision Trees for Credit Scoring

2022 ◽  
pp. 270-292
Author(s):  
Luca Di Persio ◽  
Alberto Borelli
Keyword(s):  
Support Vector Machines ◽  
Decision Trees ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Support Vector ◽  
Scoring Model ◽  
Vector Machines ◽  
Boosted Decision Trees ◽  
The One ◽  
Credit Scoring Model

The chapter developed a tree-based method for credit scoring. It is useful because it helps lenders decide whether to grant or reject credit to their applicants. In particular, it proposes a credit scoring model based on boosted decision trees which is a technique consisting of an ensemble of several decision trees to form a single classifier. The analysis used three different publicly available datasets, and then the prediction accuracy of boosted decision trees is compared with the one of support vector machines method.

Building a Credit Scoring Model Based on Data Mining Approaches

2020 ◽  
Vol 30 (02) ◽  
pp. 147-169
Author(s):  
Jasmina Nalić ◽  
Goran Martinovic
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Banking Sector ◽  
Credit Scoring ◽  
Model Performance ◽  
Real Data ◽  
Support Vector ◽  
Scoring Model ◽  
Adopted Model ◽  
Credit Scoring Model

Nowadays, one of the biggest challenges in banking sector, certainly, is assessment of the client’s creditworthiness. In order to improve the decision-making process and risk management, banks resort to using data mining techniques for hidden patterns recognition within a wide data. The main objective of this study is to build a high-performance customized credit scoring model. The model named Reliable client is based on Bank’s real dataset and originally built by applying four different classification algorithms: decision tree (DT), naive Bayes (NB), generalized linear model (GLM) and support vector machine (SVM). Since it showed the greatest results, but also seemed as the most appropriate algorithm, the adopted model is based on GLM algorithm. The results of this model are presented based on many performance measures that showed great predictive confidence and accuracy, but we also demonstrated significant impact of data pre-processing on model performance. Statistical analysis of the model identified the most significant parameters on the model outcome. In the end, created credit scoring model was evaluated using another set of real data of the same Bank.

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