Within the banking industry, requests for credit cards are growing tremendously, and manually reviewing each application is frequently a tiresome task that is also prone to human error. In this situation, banks and other big financial institutions can use a machine learning model to forecast whether or not to grant the customer a credit card. Banks utilize machine learning techniques to process their financial data, extract knowledge from it, and use it for risk management and decision-making. This study has created, trained, and evaluated three classification models utilizing authentic Kaggle datasets. The main research goal is to assess and contrast the models based on how accurately they project the composition of the typical class. In this work, we examine the accuracy, F1 Score, Precision, and confusion matrix of different supervised machine learning models to estimate the probability that a credit card request would be approved. After testing three classifiers, it is discovered that Random Forest outperformed Decision Tree and Logistic Regression. Random Forest’s accuracy is 94.67%, precision is 0.85, recall is 0.980, and F1 Score is 2.940.
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25 March 2024
SECOND INTERNATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION NETWORKS (ICCCN 2022)
19–20 November 2022
Manchester, UK
Research Article|
March 25 2024
Automatic credit card approval prediction system Available to Purchase
Astha Bhaskar;
Astha Bhaskar
a)
1
Indira Gandhi Delhi Technical University for Women
, Delhi, James Church, New Church Rd, Opp. St, Kashmere Gate, New Delhi-110006, India
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Ritu Rani;
Ritu Rani
b)
1
Indira Gandhi Delhi Technical University for Women
, Delhi, James Church, New Church Rd, Opp. St, Kashmere Gate, New Delhi-110006, India
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Garima Jaiswal;
Garima Jaiswal
c)
2
Bennett University, Greater Noida
, Plot Nos 8-11, TechZone 2, Greater Noida, Uttar Pradesh 201310, India
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Amita Dev;
Amita Dev
d)
1
Indira Gandhi Delhi Technical University for Women
, Delhi, James Church, New Church Rd, Opp. St, Kashmere Gate, New Delhi-110006, India
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Arun Sharma;
Arun Sharma
e)
1
Indira Gandhi Delhi Technical University for Women
, Delhi, James Church, New Church Rd, Opp. St, Kashmere Gate, New Delhi-110006, India
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Poonam Bansal;
Poonam Bansal
f)
1
Indira Gandhi Delhi Technical University for Women
, Delhi, James Church, New Church Rd, Opp. St, Kashmere Gate, New Delhi-110006, India
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Umesh Gupta
Umesh Gupta
g)
3
SCSAI, SR University
, Ananthasagar, Hasanparthy, Hanumakonda, Warangal-506371, Telangana, India
g)Corresponding author: [email protected]
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Astha Bhaskar
1,a)
Ritu Rani
1,b)
Garima Jaiswal
2,c)
Amita Dev
1,d)
Arun Sharma
1,e)
Poonam Bansal
1,f)
Umesh Gupta
3,g)
1
Indira Gandhi Delhi Technical University for Women
, Delhi, James Church, New Church Rd, Opp. St, Kashmere Gate, New Delhi-110006, India
2
Bennett University, Greater Noida
, Plot Nos 8-11, TechZone 2, Greater Noida, Uttar Pradesh 201310, India
3
SCSAI, SR University
, Ananthasagar, Hasanparthy, Hanumakonda, Warangal-506371, Telangana, India
AIP Conf. Proc. 2919, 050007 (2024)
Citation
Astha Bhaskar, Ritu Rani, Garima Jaiswal, Amita Dev, Arun Sharma, Poonam Bansal, Umesh Gupta; Automatic credit card approval prediction system. AIP Conf. Proc. 25 March 2024; 2919 (1): 050007. https://doi.org/10.1063/5.0184623
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