Guide to fraud prediction models | Census

Fraud prediction models are becoming increasingly important as the world becomes more digitized, with record-highs of fraudulent activity reaching $42 billion in 2020. There are four types of fraud models: profile-specific models that identify fraudulent users, transaction-specific models that identify fraudulent transactions, rules-based models that use hard-coded rules to detect fraudulent activity, and machine learning models that learn from features to identify signals of fraudulent behavior. When choosing features for a model, it's essential to include as many signals indicating fraudulent activity as possible. Evaluating a fraud model is different from normal machine learning models due to the significant imbalance between fraudulent and non-fraudulent profiles/transactions, making accuracy an unsuitable metric. Instead, precision and recall metrics are used to assess the model's performance, with precision being suitable for scenarios where the cost of classifying a non-fraudulent transaction as fraudulent is high, and recall being critical when identifying every single fraudulent transaction.

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