Default of credit card clients

Donated on 1/25/2016

This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods.

Dataset Characteristics

Multivariate

Subject Area

Business

Associated Tasks

Classification

Feature Type

Integer, Real

# Instances

30000

# Features

23

Dataset Information

Additional Information

This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.

Has Missing Values?

No

Introductory Paper

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
IDIDIntegerno
X1FeatureIntegerLIMIT_BALno
X2FeatureIntegerSexSEXno
X3FeatureIntegerEducation LevelEDUCATIONno
X4FeatureIntegerMarital StatusMARRIAGEno
X5FeatureIntegerAgeAGEno
X6FeatureIntegerPAY_0no
X7FeatureIntegerPAY_2no
X8FeatureIntegerPAY_3no
X9FeatureIntegerPAY_4no

0 to 10 of 25

Additional Variable Information

This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables: X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. X2: Gender (1 = male; 2 = female). X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). X4: Marital status (1 = married; 2 = single; 3 = others). X5: Age (year). X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above. X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.

Papers Citing this Dataset

Credit Default Mining Using Combined Machine Learning and Heuristic Approach

By Sheikh Islam, William Eberle, Sheikh Ghafoor. 2018

Published in ArXiv.

Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting

By Clement Fung, Jamie Koerner, Stewart Grant, Ivan Beschastnikh. 2018

Published in ArXiv.

0 to 3 of 3

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3 citations
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Creators

I-Cheng Yeh

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