/plushcap/analysis/aporia/aporia-optimizing-ml-driven-fraud-detection-a-comprehensive-guide-to-monitoring-and-performance

How to optimize ML fraud detection: A guide to monitoring & performance

What's this blog post about?

Fraud detection is a crucial aspect of data science, with increasing demand due to rising fraud cases and advanced techniques used by fraudsters. In recent years, AI-powered fraud detection systems have been widely adopted for their precision, cost-effectiveness, and operational efficiency. This article discusses various fraud detection techniques using data monitoring and machine learning predictive models. It covers descriptive statistics, handling missing values, identifying outliers, and applying Benford's law to detect anomalous records. The article also explores AI predictive modeling techniques such as linear regression, logistic regression, decision trees, neural networks, and ensemble methods like random forest. Additionally, it highlights the importance of monitoring metrics like confusion matrix, AUC-ROC curve, and using tools like Aporia for robust ML monitoring and explainability in fraud detection models.

Company
Aporia

Date published
Aug. 27, 2023

Author(s)
Noa Azaria

Word count
1737

Hacker News points
None found.

Language
English


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