Fraud and interpretability of machine learning models


In the category - Analytics

Interpretability: the missing link in Machine Learning adoption for fraud detection

Machine learning methods are increasingly used, especially in anti-fraud products (developed by Ercom Analytics and other vendors) to capture weak signals and spot patterns in data that humans would otherwise miss.

If the relevance of these methods for fraud detection is widely recognized, they are still mistrusted in certain industries such as banking, insurance or healthcare, due to their black box nature. The decisions made by a predictive model can be difficult to interpret by a business analyst, in part because the complexity behind of the calculations and the lack of transparency in the “recipe” that was used to produce the final output. Therefore, it seems quite understandable that an analyst having to make an important decision, for example granting a credit application or refusing the reimbursement of healthcare expenses, is reluctant to automatically apply the predictive model output without understanding the underlying reasons…

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