scholarly journals Peningkatan performance Logistic Regression menggunakan teknik Ensemble Bagging pada kasus Credit Scoring

2020 ◽  
Vol 1 (1) ◽  
pp. 21-27
Author(s):  
Firman Aziz
Keyword(s):  
Logistic Regression ◽  
Credit Scoring ◽  
Volume Data ◽  
Newton Raphson

Pengembangan model credit scoring yang efektif menjadi sangat penting karena volume data pelanggan industri kredit saat ini sangat besar. Penyelesaian masalah credit scoring berhasil mendapatkan kinerja prediktif terbaik menggunakan model metode statistik tetapi kinerjanya masih dapat ditingkatkan dengan memperkirakan parameter menggunakan persamaan nonlinear. Penelitian ini mengusulkan peningkatan metode Logistic Regression dengan melakukan teknik Ensemble dengan memperkirakan parameter Newton Raphson. Teknik Ensemble yang digunakan adalah bagging. Dua data yang akan digunakan dalam penelitian ini adalah German dan Australian Dataset. Hasil penelitian menunjukkan bahwa metode yang diusulkan berhasil mencapai kinerja terbaik dengan meningkatkan kinerja klasifikasi tunggal dengan akurasi sebesar 79.6 % untuk German Dataset dan 86.9 % untuk Australian Dataset.

Regression model of assessment of customer solvency and banking risks in the process of lending

2019 ◽  
pp. 69-73
Author(s):  
Zoryna Yurynets ◽  
Rostyslav Yurynets ◽  
Nataliya Kunanets ◽  
Ivanna Myshchyshyn
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Likelihood Ratio ◽  
Credit Scoring ◽  
Bank Loans ◽  
Management Decisions ◽  
Computer Data ◽  
Proposed Model ◽  
Loan Risk

In the current conditions of economic development, it is important to pay attention to the study of the main types of risks, effective methods of evaluation, monitoring, analysis of banking risks. One of the main approaches to quantitatively assessing the creditworthiness of borrowers is credit scoring. The objective of credit scoring is to optimize management decisions regarding the possibility of providing bank loans. In the article, the scientific and methodological provisions concerning the formation of a regression model for assessing bank risks in the process of granting loans to borrowers has been proposed. The proposed model is based on the use of logistic regression tools, discriminant analysis with the use of expert evaluation. During the formation of a regression model, the relationship between risk factors and probable magnitude of loan risk has been established. In the course of calculations, the coefficient of the individual's solvency has been calculated. Direct computer data preparation, including the calculation of the indicators selected in the process of discriminant analysis, has been carried out in the Excel package environment, followed by their import into the STATISTICA package for analysis in the “Logistic regression” sub-module of the “Nonlinear evaluation” module. The adequacy of the constructed model has been determined using the Macfaden's likelihood ratio index. The calculated value of the Macfaden's likelihood ratio index indicates the adequacy of the constructed model. The ability to issue loans to new clients has been evaluated using a regression model. The conducted calculations show the possibility of granting a loan exclusively to the second and third clients. The offered method allows to conduct assessment of client's solvency and risk prevention at different stages of lending, facilitates the possibility to independently make informed decisions on credit servicing of clients and management of a loan portfolio, optimization of management decisions in banks. In order for a loan-based model to continue to perform its functions, it must be periodically adjusted.

Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

2019 ◽  
Vol 28 (05) ◽  
pp. 1950017 ◽  
Author(s):  
Guotai Chi ◽  
Mohammad Shamsu Uddin ◽  
Mohammad Zoynul Abedin ◽  
Kunpeng Yuan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Logistic Regression ◽  
Credit Risk ◽  
Risk Prediction ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Hybrid Models ◽  
Multilayer Perceptrons ◽  
Inference Systems

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

scholarly journals An implementation of ensemble methods, logistic regression, and neural network for default prediction in Peer-to-Peer lending

2021 ◽  
Vol 39 (1) ◽  
pp. 163-197
Author(s):  
Aneta Dzik-Walczak ◽  
Mateusz Heba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Regression Model ◽  
Financial Institutions ◽  
Logistic Regression Model ◽  
Credit Scoring ◽  
Peer To Peer ◽  
Ensemble Model ◽  
Peer Lending

Credit scoring has become an important issue because competition among financial institutions is intense and even a small improvement in predictive accuracy can result in significant savings. Financial institutions are looking for optimal strategies using credit scoring models. Therefore, credit scoring tools are extensively studied. As a result, various parametric statistical methods, non-parametric statistical tools and soft computing approaches have been developed to improve the accuracy of credit scoring models. In this paper, different approaches are used to classify customers into those who repay the loan and those who default on a loan. The purpose of this study is to investigate the performance of two credit scoring techniques, the logistic regression model estimated on categorized variables modified with the use of WOE (Weight of Evidence) transformation, and neural networks. We also combine multiple classifiers and test whether ensemble learning has better performance. To evaluate the feasibility and effectiveness of these methods, the analysis is performed on Lending Club data. In addition, we investigate Peer-to-peer lending, also called social lending. From the results, it can be concluded that the logistic regression model can provide better performance than neural networks. The proposed ensemble model (a combination of logistic regression and neural network by averaging the probabilities obtained from both models) has higher AUC, Gini coefficient and Kolmogorov-Smirnov statistics compared to other models. Therefore, we can conclude that the ensemble model allows to successfully reduce the potential risks of losses due to misclassification costs.

scholarly journals The Use of PCA in Reduction of Credit Scoring Modeling Variables: Evidence from Greek Banking System

2018 ◽  
Author(s):  
Panagiota Giannouli ◽  
Christos E. Kountzakis
Keyword(s):  
Logistic Regression ◽  
Principal Components ◽  
Credit Scoring ◽  
Banking System ◽  
Real Data ◽  
Categorical Variables

In this paper, we use the Principal Components Logistic Regression as a technique to reduce the variables being used in Credit Scoring Modeling. Specifically, we construct two models in which greek enterprises are classified, through their credit behavior and we evaluate them, relying on real data. In general, we propose a general way to use PC Regression, in case that we have high correlations and categorical variables in the sample.

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.

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