How Zopa enhanced their fraud detection application using Amazon SageMaker Clarify

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Summary

• This post is co-authored by Jiahang Zhong, Head of Data Science at Zopa.
• To combat this, Zopa uses advanced ML models to flag suspicious applications for human review, while leaving the majority of genuine applications to be approved by the highly automated system.
• SHAP values of individual predictions can be computed via a SageMaker Clarify processing job and made available to the underwriting team to understand individual predictions.
• Zopa’s data scientists use an informative baseline sample from the population of past approved non-fraud applications, to explain why those flagged instances are considered suspicious by the model.