A Hybrid Credit Scoring Model Using Neural Networks and Logistic Regression

2019 ◽  
pp. 251-258 ◽  
Author(s):  
Lkhagvadorj Munkhdalai ◽  
Jong Yun Lee ◽  
Keun Ho Ryu
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Credit Scoring ◽  
Scoring Model ◽  
Credit Scoring Model

scholarly journals Methodology for the validation of the credit scoring model of the retail portfolio

2021 ◽  
Vol 73 (7) ◽  
pp. 41-44
Author(s):  
Y.S. Zhieru
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Final Stage ◽  
Logistic Regression Model ◽  
Credit Scoring ◽  
Real Data ◽  
Scoring Model ◽  
Credit Scoring Model

The final stage of constructing a logistic regression model is checking its validity and testing it on real data. The degree of validity of a logistic regression model is evidenced by its ability to correctly classify borrowers, the model's ability to distinguish "good" borrowers from "bad" borrowers.

Technology credit scoring model with fuzzy logistic regression

2016 ◽  
Vol 43 ◽  
pp. 150-158 ◽  
Author(s):  
So Young Sohn ◽  
Dong Ha Kim ◽  
Jin Hee Yoon
Keyword(s):  
Logistic Regression ◽  
Credit Scoring ◽  
Scoring Model ◽  
Fuzzy Logistic Regression ◽  
Credit Scoring Model

scholarly journals A COST-SENSITIVE LOGISTIC REGRESSION CREDIT SCORING MODEL BASED ON MULTI-OBJECTIVE OPTIMIZATION APPROACH

2019 ◽  
Vol 26 (2) ◽  
pp. 405-429 ◽  
Author(s):  
Feng Shen ◽  
Run Wang ◽  
Yu Shen
Keyword(s):  
Logistic Regression ◽  
Credit Scoring ◽  
Classification Error ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Scoring Model ◽  
Multi Objective ◽  
Proposed Model ◽  
The Cost ◽  
Credit Scoring Model

Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models.

scholarly journals Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea

2021 ◽  
Vol 14 (1) ◽  
pp. 130
Author(s):  
Sunghyon Kyeong ◽  
Daehee Kim ◽  
Jinho Shin
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Credit Scoring ◽  
Model Performance ◽  
Sampled Data ◽  
Discrimination Power ◽  
Log Data ◽  
Scoring Model ◽  
Internet Bank ◽  
Credit Scoring Model

The credit scoring model is one of the most important decision-making tools for the sustainability of banking systems. This study is the first to examine whether it can be improved by using system log data that are stoed extensively for system operation. We used the log data recorded by the mobile application system of KakaoBank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from KakaoBank’s log data, we created a credit scoring model by utilizing variables with high information values and logistic regression, the most common method for developing credit scoring models in financial institutions. To prove our hypothesis on the improvement of credit scoring model performance, we performed an independent sample t-test using the simulation results of repeated model development and performance measurement based on randomly sampled data. Consequently, the discrimination power of the proposed model using logistic regression (neural network) compared to the credit bureau-based model significantly improved by 1.84 (2.22) percentage points based on the Kolmogorov–Smirnov statistics. The results of this study suggest that a bank can utilize the accumulated log data inside the bank to improve decision-making systems, including credit scoring, at a low cost.

scholarly journals Design and Development of Credit Scoring Model for Conventional Banks for Individual Borrowing Case Study on PT BPR Sungai Puar District Agam

2018 ◽  
Vol 1 (1) ◽  
pp. 43-56
Author(s):  
Rio Hendriadi ◽  
Anne Putri ◽  
Dona Amelia ◽  
Rany Syafrina
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Credit Scoring ◽  
Scoring Model ◽  
Secondary Sources ◽  
Conventional Banks ◽  
Credit Scoring Model ◽  
Conventional Bank

Objective – This research is conducted to design and to develop credit scoring model on conventional bank in order to determine individual loan, the research takes place in PT BPR Sungai Puar, Kabupaten Agam. This model tries to evaluate the credit risk of BPR Sungai Puar.Design/methodology – The data are considered as secondary sources as they are taken from BPR Sungai Puar database by classifying them into two analysis tools including discriminant analysis and logistic regression. Results – The resuts are presentes inform of model and credit scoring perfection on PT BPR Sungai Puar Kabupaten Agam.Keywords Credit Scoring Model, Conventional Banks, Individual Loan

Decision Support Credit Scoring Model to Improve Loan Default Prediction in Financial Institutions

2019 ◽  
Vol 16 (8) ◽  
pp. 3514-3518
Author(s):  
Kamya Eria ◽  
Preethi Subramanian
Keyword(s):  
Logistic Regression ◽  
Credit Scoring ◽  
Vital Role ◽  
Quality Of Data ◽  
Loan Default ◽  
Scoring Model ◽  
Credit Score ◽  
Default Prediction ◽  
Credit Scoring Model

Credit scoring plays a vital role in assessing the creditworthiness of loan applicants thus speeding up the approval process. Credit score models however rely on the accuracy of classification models for their performance. This accuracy performance depends not only on the choice of data mining process; it is heavily influenced by the quality of data as well. Although no techniques can be favored over the other, it has been evidenced that logistic regression has been widely employed as an industrial technique for its comprehensive simplicity. This study proposes a SEMMA-based credit scoring model developed with an improved Logistic Regression (LR) model. Improvements are by exclusion of irrelevant features and adjusting the partition ratios. The model has been compared with the predominant models and proved to contain outstanding results with minimal credit decision errors.

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