Research Paper Undergraduate 1,309 words

Credit Card Application Variables: Approval and Limit Analysis

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Abstract

This paper analyzes the relative influence of six key variables — age, marital status, home ownership, annual income, job status, and credit history rating — on two distinct credit card decisions: whether to approve an application and how much credit to extend. Drawing on applicant data, the paper identifies credit history rating and annual income as the dominant predictors of both approval and credit limit, while characterizing age, marital status, and job status as secondary factors that operate largely through their correlation with income and creditworthiness. The analysis concludes with an applied case study evaluating a prospective applicant and recommending an appropriate credit limit based on the observed patterns.

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What makes this paper effective

Clear separation between two distinct decision points — approval versus credit limit — prevents analytical confusion and gives the paper a logical two-part structure.
Each variable is evaluated consistently across both sections, allowing direct comparison of how a factor's importance shifts depending on the decision being made.
The applied case study in the final section demonstrates how abstract variable analysis translates into a concrete, reasoned recommendation, grounding the data interpretation in practical outcomes.

Key academic technique demonstrated

The paper demonstrates comparative descriptive analysis: it systematically contrasts the mean values of approved versus denied applicants across multiple variables to rank each factor's predictive strength. Rather than treating variables in isolation, the author identifies interaction effects — for example, noting that age matters primarily through its influence on credit history and income rather than as a standalone predictor — which reflects a nuanced, multivariate way of thinking about data.

Structure breakdown

The paper is organized into three numbered sections. The first covers variable-by-variable impact on the binary approval decision. The second mirrors that structure to examine the same variables as predictors of credit limit amount. The third applies the findings to a specific applicant profile, synthesizing both analyses into a recommended credit limit. Each section builds logically on the last, moving from descriptive analysis to application.

Introduction to Credit Application Variables

Each variable in a credit card application has a different impact on the outcome. Some variables are powerful indicators on their own, showing a high correlation with the decision to grant credit. Others show a lower correlation with the approval decision but are meaningfully correlated with more important factors.

Factors Affecting Credit Approval

Age does not appear to be a major predictor of application success. The average age of successful applicants was only slightly higher than the mean for the group, while the average age of unsuccessful applicants was lower by a couple of years. Applicants from lower age groups were able to receive credit, while those from higher age groups sometimes did not.

Among those whose applications were rejected, applicants came from all age groups but in every case had a credit history rating under 50%. Among young applicants whose income fell within the normal range for approval but who were nonetheless unsuccessful, all had credit history ratings below the normal range of approved applicants. Age appears to be a factor in one's credit history rating, as those with the highest ratings were in their forties and fifties, while those with the lowest ratings were, on average, in their twenties and thirties. Thus, the impact of age is determined largely by the extent to which age affects credit history rating and income levels, rather than as an indicator in itself.

There is a very weak correlation between marital status and credit approval. It appears to be the factor of least importance among all the variables. Among those denied, there was a slight tendency towards being married, while those approved had a slightly greater-than-average chance of being single. However, both scores were fairly close to the mean for the applicant group as a whole.

Home ownership is a factor in whether an application is approved. It is not a strong factor, in that many applicants who do not own a home were granted credit, but the majority of applicants who did not receive credit also did not own a home. Home ownership is indicative of the applicant's overall financial position — it requires a healthy income level and strong credit in order to be granted a mortgage.

Annual income is a significant factor in the decision to grant an application. Among those denied, the mean annual income was $27,956, while among those granted credit the mean annual income was $42,508. It is unusual for a high-income applicant to be rejected under any circumstances, even among those with credit history ratings below the average for credit approval.

Job status is an indicator to the extent that it affects annual income. Among those approved, full-time status was more common than part-time, while among those not approved the split was more even. The average status of approved applicants was very near the average for the group as a whole, while unsuccessful applicants trended slightly towards part-time status.

Credit history rating is a strong indicator of approval. Among those not approved, the mean rating was 39.69%, while among those who were approved the mean rating was 68.11%. Furthermore, among those denied credit the highest rating was 51%, and among those approved the lowest rating was 45%.

The credit history rating is key because it measures the applicant's attitude towards credit. An unfavorable attitude makes extending credit to that applicant a risky proposition, regardless of how much income or financial stability they may otherwise have. Conversely, an applicant who has demonstrated a healthy attitude towards credit will likely be granted credit, even if they cannot afford to carry very much of it.

2 Locked Sections · 495 words remaining

Factors Affecting the Amount of Credit Granted · 310 words

"How variables determine the credit limit extended"

Case Study: Evaluating a Prospective Applicant · 185 words

"Applied recommendation for a specific applicant profile"

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Key Concepts in This Paper

Credit History Rating Annual Income Credit Approval Credit Limit Home Ownership Job Status Marital Status Variable Correlation Creditworthiness Applicant Profile