Credit risk evaluation of the credit card by grey data mining based on AHP-GRAP

2013 ◽  
pp. 321-328
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Risk Evaluation

Beyond Classification

2008 ◽  
pp. 1855-1876
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

Beyond Classification

2011 ◽  
pp. 139-161
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

scholarly journals Credit risk Evaluation by hybrid data mining technique

2012 ◽  
Vol 3 ◽  
pp. 194-200 ◽  
Author(s):  
Weimin Chen ◽  
Guocheng Xiang ◽  
Youjin Liu ◽  
Kexi Wang
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Hybrid Data

scholarly journals Performance Comparison of Data Mining Algorithm to Predict Approval of Credit Card

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 149
Author(s):  
Ipin Sugiyarto ◽  
Bibit Sudarsono ◽  
Umi Faddillah
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Selection ◽  
Credit Risk ◽  
Credit Card ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Performance Comparison ◽  
Support Vector ◽  
Data Mining Algorithm

Credit analysis needs to identify and assess the factors that can affect customers in returning credit. Accurate measurement and good management ability in dealing with credit risk is an effort to save the economic operations unit and be beneficial for a stable and healthy financial system. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which consists of several stages, namely Business Understanding (dataset), Data Processing (Feature Selection PCA & Dimension Reduce), Algorithm Models (NN+PSO, SVM, LR), Evaluation (Validation and Accuracy). This study has tested the model using a neural network using the PCA selection feature and optimized with the Particle Swarm Optimize (PSO) algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove that the use of a single Neural Network method produces an accuracy of 80.33%. whereas the use of PCA + Neural Network + PSO hybrid method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the Neural Network was optimized using PSO and used feature selection. The purpose of this study is to implement and compare Support Vector Machine, Logistic Regression and Neural Network algorithms based on PCA and optimize PSO (Particle Swarm Optimization) to improve accuracy in predictions of credit card approvals.

scholarly journals Credit Risk Management Using Automatic Machine Learning

2020 ◽  
Vol 14 (2) ◽  
Author(s):  
Bartłomiej Gaweł ◽  
Andrzej Paliński
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Logit Model ◽  
Credit Card ◽  
Traditional Approach ◽  
Automatic Machine ◽  
Classification Models ◽  
Credit Risk Management ◽  
Similar Accuracy

The article presents the basic techniques of data mining implemented in typical commercial software. They were used to assess the risk of credit card debt repayment. The article assesses the quality of classification models derived from data mining techniques and compares their results with the traditional approach using a logit model to assess credit risk. It turns out that data mining models provide similar accuracy of classification compared to the logit model, but they require much less work and facilitate the automation of the process of building scoring models.

Sign in / Sign up

Export Citation Format

Share Document