500 points by ml_wizard 1 year ago flag hide 12 comments
ml_guru 1 year ago next
Great article on using ML for fraud detection! The section on anomaly detection was particularly insightful.
datasciencefan 1 year ago next
Agreed, anomaly detection is an essential technique for detecting unusual patterns in data. Have you tried using autoencoders for this purpose? They provide great results!
datascientist123 1 year ago next
Several techniques can be used to address imbalanced datasets, such as oversampling, undersampling, or SMOTE. Moreover, recall or AUC-PR can be used as evaluation metrics rather than accuracy.
deeplearninglad 1 year ago prev next
Anomaly detection in fraud detection is very effective, but don't forget about feature engineering. It's crucial for building accurate predictive models.
securityenthusiast 1 year ago prev next
In addition to anomaly detection, using supervised learning to classify transactions as fraudulent or not can also be beneficial. The author touched on this to some extent, but it would be interesting to see more examples on this approach.
codesandqueries 1 year ago next
Yes, supervised learning models such as Random Forest, XGBoost, or even LSTM networks can be effective in such scenarios. It's essential to train these models on high-quality, labeled data.
cloudyinsights 1 year ago prev next
I've personally seen successful fraud detection projects with a hybrid approach: first applying unsupervised learning (anomaly detection or clustering), followed by supervised learning.
aiintraining 1 year ago prev next
Does anyone have experience implementing these models in a live environment? Real-time fraud detection is a different beast, with numerous challenges.
devopsjohn 1 year ago next
Model deployment, monitoring, and fast retraining are crucial for real-time fraud detection. Having a well-architected cloud infrastructure with containerized microservices can help with that.
machinelearningz 1 year ago next
In addition to DevOpsJohn's suggestions, I'd also recommend using AI-driven anomaly detection tools like Amazon Lookout for Equities, which can bolster the capabilities of your ML models at scale.
quantprodigy 1 year ago prev next
Just a reminder that it's essential to comply with all relevant regulations when implementing ML-driven fraud detection systems. Regulatory oversight is increasing in this area, especially with the growth of AI.