February 26, 2024

Securing Digital Finance: Applying Machine Learning for Fraud Analysis

View Full Paper

Ask AI

Bookmark

Machine learning algorithms significantly outperform traditional fraud detection systems across digital finance platforms, particularly after oversampling unbalanced transaction data.
Evaluating 284,807 real credit card transactions demonstrates that random forest, decision trees, and neural networks accurately capture complex fraud patterns in high-volume settings.
Comparative testing using k-nearest neighbors and logistic regression highlights that targeted preprocessing enables robust fraud prevention across expanding digital finance systems.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Empirical evaluation demonstrates that machine learning models effectively identify fraudulent credit card transactions, suggesting enhanced financial security via data oversampling.

Cite This Study

Khaldy et al. (2024) studied this question.

synapsesocial.com/papers/68e778e0b6db6435876edfa1 https://doi.org/10.1109/iccr61006.2024.10533140

View Full Paper

Ask AI

Bookmark

Implication

Empirical evaluation demonstrates that machine learning models effectively identify fraudulent credit card transactions, suggesting enhanced financial security via data oversampling.

Securing Digital Finance: Applying Machine Learning for Fraud Analysis

Key Points

Discussion

Implication

Cite This Study

Securing Digital Finance: Applying Machine Learning for Fraud Analysis

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider