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.

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.

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.

Modelling Credit Default in Microfinance—An Indian Case Study

2017 ◽  
Vol 16 (3) ◽  
pp. 246-258 ◽  
Author(s):  
P. K. Viswanathan ◽  
S. K. Shanthi
Keyword(s):  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Relative Importance ◽  
Microfinance Institutions ◽  
Scoring Method ◽  
Credit Score ◽  
Credit Default ◽  
Key Drivers

Credit score models have been successfully applied in a traditional credit card industry and by mortgage firms to determine defaulting customer from the non-defaulting customer. In the light of growing competition in the microfinance industry, over-indebtedness and other factors, the industry has come under increased regulatory supervision. Our study provides evidence from a large microfinance institutions (MFI) in India, and we have applied both the credit scoring method and neural network (NN) method and compared the results. In this article, we demonstrate the capability of credit scoring models for an Indian-based microfinance firm in terms of predicting default probability as well the relative importance of each of its associated drivers. A logistic regression model and NN have been used as the predictive analytic tools for sifting the key drivers of default.

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.

Student Performance in E-Learning Environments

2011 ◽  
pp. 149-194
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Ion Gh. Rosca ◽  
Radu Mogos ◽  
Maria-Iuliana Dascalu
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Learning Environments ◽  
Business Administration ◽  
Data Sources ◽  
Future Developments ◽  
Learning Platform ◽  
E Learning ◽  
The Relationship ◽  
Economic Studies

The aim of this chapter is to explore the application of data mining for analyzing performance and satisfaction of the students enrolled in an online two-year master degree programme in project management. This programme is delivered by the Academy of Economic Studies, the biggest Romanian university in economics and business administration in parallel, as an online programme and as a traditional one. The main data sources for the mining process are the survey made for gathering students’ opinions, the operational database with the students’ records and data regarding students activities recorded by the e-learning platform are. More than 180 students have responded, and more than 150 distinct characteristics/ variable per student were identified. Due the large number of variables data mining is a recommended approach to analysis this data. Clustering, classification, and association rules were employed in order to identify the factor explaining students’ performance and satisfaction, and the relationship between them. The results are very encouraging and suggest several future developments.

The relationship of the scleractinian corals to the rugose corals

Paleobiology ◽  
1980 ◽  
Vol 6 (02) ◽  
pp. 146-160 ◽  
Author(s):  
William A. Oliver
Keyword(s):  
Critical Points ◽  
Scleractinian Corals ◽  
Convincing Evidence ◽  
Early Triassic ◽  
Rugose Corals ◽  
History Of ◽  
The Subject ◽  
Relationship Of ◽  
The Relationship ◽  
Biologic Factors

The Mesozoic-Cenozoic coral Order Scleractinia has been suggested to have originated or evolved (1) by direct descent from the Paleozoic Order Rugosa or (2) by the development of a skeleton in members of one of the anemone groups that probably have existed throughout Phanerozoic time. In spite of much work on the subject, advocates of the direct descent hypothesis have failed to find convincing evidence of this relationship. Critical points are:(1) Rugosan septal insertion is serial; Scleractinian insertion is cyclic; no intermediate stages have been demonstrated. Apparent intermediates are Scleractinia having bilateral cyclic insertion or teratological Rugosa.(2) There is convincing evidence that the skeletons of many Rugosa were calcitic and none are known to be or to have been aragonitic. In contrast, the skeletons of all living Scleractinia are aragonitic and there is evidence that fossil Scleractinia were aragonitic also. The mineralogic difference is almost certainly due to intrinsic biologic factors.(3) No early Triassic corals of either group are known. This fact is not compelling (by itself) but is important in connection with points 1 and 2, because, given direct descent, both changes took place during this only stage in the history of the two groups in which there are no known corals.

Investigating the Relationship Between Self-Esteem and Stigma Among Young Adults With History of Suicide Attempts

Crisis ◽  
2016 ◽  
Vol 37 (4) ◽  
pp. 265-270 ◽  
Author(s):  
Meshan Lehmann ◽  
Matthew R. Hilimire ◽  
Lawrence H. Yang ◽  
Bruce G. Link ◽  
Jordan E. DeVylder
Keyword(s):  
Young Adults ◽  
Suicide Attempts ◽  
Self Esteem ◽  
High Risk Population ◽  
High Functioning ◽  
People With Mental Illness ◽  
History Of ◽  
The Individual ◽  
The Impact ◽  
The Relationship

Abstract. Background: Self-esteem is a major contributor to risk for repeated suicide attempts. Prior research has shown that awareness of stigma is associated with reduced self-esteem among people with mental illness. No prior studies have examined the association between self-esteem and stereotype awareness among individuals with past suicide attempts. Aims: To understand the relationship between stereotype awareness and self-esteem among young adults who have and have not attempted suicide. Method: Computerized surveys were administered to college students (N = 637). Linear regression analyses were used to test associations between self-esteem and stereotype awareness, attempt history, and their interaction. Results: There was a significant stereotype awareness by attempt interaction (β = –.74, p = .006) in the regression analysis. The interaction was explained by a stronger negative association between stereotype awareness and self-esteem among individuals with past suicide attempts (β = –.50, p = .013) compared with those without attempts (β = –.09, p = .037). Conclusion: Stigma is associated with lower self-esteem within this high-functioning sample of young adults with histories of suicide attempts. Alleviating the impact of stigma at the individual (clinical) or community (public health) levels may improve self-esteem among this high-risk population, which could potentially influence subsequent suicide risk.

Misreading Egypt

2018 ◽  
Author(s):  
Jesse Schotter
Keyword(s):  
New Media ◽  
Narrative Form ◽  
Experimental Film ◽  
Tower Of Babel ◽  
Avant Garde ◽  
Sergei Eisenstein ◽  
History Of ◽  
The One ◽  
The Relationship ◽  
Modern Technologies

The first chapter of Hieroglyphic Modernisms exposes the complex history of Western misconceptions of Egyptian writing from antiquity to the present. Hieroglyphs bridge the gap between modern technologies and the ancient past, looking forward to the rise of new media and backward to the dispersal of languages in the mythical moment of the Tower of Babel. The contradictory ways in which hieroglyphs were interpreted in the West come to shape the differing ways that modernist writers and filmmakers understood the relationship between writing, film, and other new media. On the one hand, poets like Ezra Pound and film theorists like Vachel Lindsay and Sergei Eisenstein use the visual languages of China and of Egypt as a more primal or direct alternative to written words. But Freud, Proust, and the later Eisenstein conversely emphasize the phonetic qualities of Egyptian writing, its similarity to alphabetical scripts. The chapter concludes by arguing that even avant-garde invocations of hieroglyphics depend on narrative form through an examination of Hollis Frampton’s experimental film Zorns Lemma.

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