Guide to fraud prediction models

As digitalization accelerates, fraud has reached unprecedented levels, with losses amounting to $42 billion in 2020, impacting nearly half of all companies. This situation highlights the necessity and efficacy of fraud prediction models, which leverage technological advancements like big data and modern algorithms. These models can be categorized mainly into profile-specific and transaction-specific types, focusing on user-level and transaction-level fraud, respectively. Additionally, fraud detection employs rules-based models, which rely on pre-set rules, and machine learning models, which automatically identify fraudulent patterns from large datasets. Key features for these models include transaction timing, location, cost-to-average spend ratio, and email information. Evaluating these models presents unique challenges due to the imbalance between fraudulent and non-fraudulent data, making accuracy an inadequate metric. Instead, precision and recall are recommended to measure the effectiveness of fraud detection. These metrics help balance the need to correctly identify fraudulent activities while minimizing false positives.