A carregar...
Publicação
Machine Learning Algorithms applied to Credit Card Fraud Detection: A Comparative Analysis of Models Performances
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.57 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
This dissertation addresses the growing challenge of credit card fraud detection amidst
increasing online consumer transactions. The research assesses the effectiveness of various
learning algorithms, comparing traditional, ensemble, and deep learning models in
identifying fraudulent activities.
Both empirical and theoretical approaches were employed, analyzing synthetic and real
transaction data. Key performance metrics included accuracy, precision, recall, F1-score, and
AUC-ROC. The study also evaluated model attributes such as interpretability and ease of
modification, utilizing cross-validation and hyperparameter tuning to ensure robust results.
The findings indicate that ensemble algorithms, notably Random Forest and Gradient
Boosting, excel in accuracy, while deep learning models are proficient at detecting complex
fraud patterns but lack interpretability. Features emphasizing temporal aspects and
customer behavior significantly boost performance.
Despite these advancements, challenges remain, including class imbalance, the trade-off
between model complexity and interpretability, and the necessity for real-time detection.
These challenges underscore the difficulty of developing a universally effective fraud
detection system.
In conclusion, while machine learning algorithms show significant promise for fraud
detection, no single solution fits all scenarios. A combination of multiple models often
produces better outcomes. This research provides practical insights for financial institutions
and regulators, recommending future efforts to focus on developing real-time detection
algorithms, improving model interpretability, exploring adaptive learning techniques, and
investigating federated learning for diverse, privacy-preserved data use. These steps will
enhance current fraud detection systems, making them more efficient, transparent, and
adaptable, thereby bolstering financial security.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Machine Learning Algorithms Credit Card Fraud Detection Real-Time Detection SDG 8 - Decent work and economic growth