Fraud models are used across many key industries, yet they are very challenging to get right in production. Adapting to evolving patterns, accounting for imbalanced data sets, and limited features can all negatively impact model performance and lead to missed fraudulent transactions. ML observability alleviates common fraud model pain points to improve customer satisfaction, mitigate the impacts of fraud, and enhance model transparency.
See how Arize can help you accurately calculate crucial performance metrics and put guardrails on your model. Monitor for false negatives and false positives rates to ensure accuracy, compare your model to a baseline to surface drastic changes, identify drifting concepts that don’t account for fraud, and analyze hard failures in your data quality pipeline to keep the fraudsters at bay.
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