Credit Card Fraud Detection
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2025
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| author | C.Aarthi, A.Ajisha, Dr.R.Ravi |
| author_facet | C.Aarthi, A.Ajisha, Dr.R.Ravi |
| contents | <p> The banking industry is seriously threatened by credit card fraud, as illegal transactions result in<br>billions of dollars being lost annually worldwide.This study presents a machine learning-based method for<br>detecting credit card fraud in real-time. The suggested method makes use of an interactive web application<br>created with Streamlit that incorporates a pre-trained classification model. Thirty numerical features that<br>reflect transaction characteristics are used to train the model using anonymised transaction data. Through<br>the interface, users enter transaction details, and the system classifies the transaction as either legitimate or<br>fraudulent based on its prediction of the likelihood of fraud. By spotting obscure patterns in the feature<br>space, the model shows excellent accuracy in finding anomalies. The purpose of this application is to help<br>analysts and financial institutions quickly determine the legality of transactions and reduce the risks<br>associated with fraud. The system is a useful tool for both industry and research because it is portable, easy<br>to use, and deployable across multiple platforms. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15400188 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Credit Card Fraud Detection C.Aarthi, A.Ajisha, Dr.R.Ravi <p> The banking industry is seriously threatened by credit card fraud, as illegal transactions result in<br>billions of dollars being lost annually worldwide.This study presents a machine learning-based method for<br>detecting credit card fraud in real-time. The suggested method makes use of an interactive web application<br>created with Streamlit that incorporates a pre-trained classification model. Thirty numerical features that<br>reflect transaction characteristics are used to train the model using anonymised transaction data. Through<br>the interface, users enter transaction details, and the system classifies the transaction as either legitimate or<br>fraudulent based on its prediction of the likelihood of fraud. By spotting obscure patterns in the feature<br>space, the model shows excellent accuracy in finding anomalies. The purpose of this application is to help<br>analysts and financial institutions quickly determine the legality of transactions and reduce the risks<br>associated with fraud. The system is a useful tool for both industry and research because it is portable, easy<br>to use, and deployable across multiple platforms. </p> |
| title | Credit Card Fraud Detection |
| url | https://doi.org/10.5281/zenodo.15400188 |