🕵️Transformer-Based Fraud Detection

The podcast presents an interview exploring the intricacies of applying transformer models to fraud detection using textual data.

The candidate discusses specific challenges such as deliberate data obfuscation by fraudsters and the scarcity of representative labeled examples.

To mitigate overfitting while preserving subtle fraud signals, adversarial training and time-series aware validation are emphasized over standard regularization techniques.

The conversation further examines nuanced preprocessing steps tailored for fraudulent text, including advanced tokenization and entity masking.

Moreover, the interview covers strategies for integrating multi-modal data, ensuring model interpretability under production constraints, and tackling scalability and data drift in real-world fraud detection systems.