Credit Card Application Management System
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Zenodo
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| Auteurs principaux: | , , |
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| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2025
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| _version_ | 1866902009286754304 |
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| author | Aren, Harshit Singh, Ambuj Kumar, Joney |
| author_facet | Aren, Harshit Singh, Ambuj Kumar, Joney |
| contents | <p><span>In the modern banking industry, efficient and accurate credit card application processing is crucial. Traditional methods rely heavily on manual verification and rule based systems, which can be time-consuming and prone to errors. This research explores the use of machine learning (ML) techniques to improve the accuracy and efficiency of credit card application management. Various ML algorithms, including decision trees, logistic regression, and neural networks, are analyzed for their predictive capabilities in determining the creditworthiness of applicants. The study demonstrates how ML can enhance decision-making, reduce fraud, and streamline the application process.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15108211 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Credit Card Application Management System Aren, Harshit Singh, Ambuj Kumar, Joney Information Technology Credit card application machine learning decision-making fraud detection credit scoring automation risk assessment <p><span>In the modern banking industry, efficient and accurate credit card application processing is crucial. Traditional methods rely heavily on manual verification and rule based systems, which can be time-consuming and prone to errors. This research explores the use of machine learning (ML) techniques to improve the accuracy and efficiency of credit card application management. Various ML algorithms, including decision trees, logistic regression, and neural networks, are analyzed for their predictive capabilities in determining the creditworthiness of applicants. The study demonstrates how ML can enhance decision-making, reduce fraud, and streamline the application process.</span></p> |
| title | Credit Card Application Management System |
| topic | Information Technology Credit card application machine learning decision-making fraud detection credit scoring automation risk assessment |
| url | https://doi.org/10.5281/zenodo.15108211 |