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.

Data Mining for Credit Scoring

2008 ◽  
pp. 2449-2463
Author(s):  
Indranil Bose ◽  
Cheng Pui Kan ◽  
Chi King Tsz ◽  
Lau Wai Ki ◽  
Wong Cho Hung
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Probability Estimation ◽  
Financial Industry ◽  
Future Developments ◽  
History Of ◽  
The Relationship ◽  
Major Data

Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.

Data Mining for Credit Scoring

2008 ◽  
pp. 309-323 ◽  
Author(s):  
Indranil Bose ◽  
Cheng Pui Kan ◽  
Chi King Tsz ◽  
Lau Wai Ki ◽  
Wong Cho Hung
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Probability Estimation ◽  
Financial Industry ◽  
Future Developments ◽  
History Of ◽  
The Relationship ◽  
Major Data

Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.

scholarly journals How Secure Are Good Loans: Validating Loan-Granting Decisions And Predicting Default Rates On Consumer Loans

2002 ◽  
Vol 6 (3) ◽  
pp. 65-84 ◽  
Author(s):  
Jozef Zurada ◽  
Martin Zurada
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Banking Industry ◽  
Financial Institution ◽  
Credit Scoring ◽  
Risk Groups ◽  
Consumer Loans ◽  
Consumer Lending ◽  
Default Rates ◽  
Scoring Accuracy

The failure or success of the banking industry depends largely on the industrys ability to properly evaluate credit risk. In the consumer-lending context, the banks goal is to maximize income by issuing as many good loans to consumers as possible while avoiding losses associated with bad loans. Mistakes could severely affect profits because the losses associated with one bad loan may undermine the income earned on many good loans. Therefore banks carefully evaluate the financial status of each customer as well as their credit worthiness and weigh them against the banks internal loan-granting policies. Recognizing that even a small improvement in credit scoring accuracy translates into significant future savings, the banking industry and the scientific community have been employing various machine learning and traditional statistical techniques to improve credit risk prediction accuracy.This paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution deemed to be qualified customers. The data consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off and defaulted upon. The paper uses three different data mining techniques (decision trees, neural networks, logit regression) and the ensemble model, which combines the three techniques, to predict whether a particular customer defaulted or paid off his/her loan. The paper then compares the effectiveness of each technique and analyzes the risk of default inherent in each loan and group of loans. The data mining classification techniques and analysis can enable banks to more precisely classify consumers into various credit risk groups. Knowing what risk group a consumer falls into would allow a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued, on terms commensurate with the risk of default.

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 Identification of Default Payments of Credit Card Clients using Boosting Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 4990-4994
Keyword(s):  
Credit Card ◽  
Credit Rating ◽  
Learning Algorithm ◽  
Credit Scoring ◽  
Screening Method ◽  
Main Idea ◽  
Customer Data ◽  
Huge Data ◽  
Credit Default ◽  
History Of

Understanding the history of clients will act as a valuable screening method for banks by providing information that can categorize clients as defaulters on a loan. Customer credit rating is a grade process where the consumer is categorized by the grade. Credit scoring model used to ascertain credit risk from new and existing customer. Credit rating is an assessment used to measure the creditworthiness of the customer. For the huge customers related dataset we can use various classification techniques used in the field of data mining. The main idea is by analyzing the customer data and by combining machine-learning algorithm to identify the default credit card user. Default is a keyword, used for predicting the customer who cant repay the amount on time. Predicting future credit default accounts in advance is highly tedious task. Modern statistical techniques are usually unable to manage huge data. The proposed work focus mainly on ensemble learning and other artificial intelligence technique.

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.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

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