🧠Fraud Detection

This podcast is a detailed mock interview focused on applying unsupervised and semi-supervised machine learning techniques to fraud detection.

The dialogue explores the candidate's expertise in using autoencoders, isolation forests, and graph-based methods like graph neural networks.

It covers aspects such as model architecture, hyperparameter tuning, handling dynamic data, addressing false positives, and assembling different models.

The interview also emphasizes practical considerations for deployment, interpretability, and mitigating common pitfalls in real-world fraud detection systems.

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