Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2025
|
| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.15066269 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866902237061578752 |
|---|---|
| author | VELIDI, SRIRAM CHOWDARY |
| author_facet | VELIDI, SRIRAM CHOWDARY |
| contents | <p>Financial institutions suffer substantial losses due<br>to fraudulent online transactions. Traditional fraud detection<br>methods often fail to identify evolving fraud patterns due to<br>dataset imbalance and high false negative rates. This paper<br>proposes an adaptive fraud detection system integrating a valueat-risk (VaR) metric with machine learning techniques. The<br>system uses historical simulation to estimate potential fraudrelated losses and employs K-Nearest Neighbors (KNN) to classify<br>fraudulent transactions. The proposed approach enhances fraud<br>detection accuracy while minimizing false negatives, providing an<br>effective fraud prevention framework for financial institutions.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15066269 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Adaptive Fraud Detection in Online Transactions Using Machine Learning Techniques VELIDI, SRIRAM CHOWDARY Machine learning <p>Financial institutions suffer substantial losses due<br>to fraudulent online transactions. Traditional fraud detection<br>methods often fail to identify evolving fraud patterns due to<br>dataset imbalance and high false negative rates. This paper<br>proposes an adaptive fraud detection system integrating a valueat-risk (VaR) metric with machine learning techniques. The<br>system uses historical simulation to estimate potential fraudrelated losses and employs K-Nearest Neighbors (KNN) to classify<br>fraudulent transactions. The proposed approach enhances fraud<br>detection accuracy while minimizing false negatives, providing an<br>effective fraud prevention framework for financial institutions.</p> |
| title | Adaptive Fraud Detection in Online Transactions Using Machine Learning Techniques |
| topic | Machine learning |
| url | https://doi.org/10.5281/zenodo.15066269 |