Heuristic, feature-based, and E2E models
Before we dive into developing models to detect fraud, let's take a second to pause and ponder over the different kinds of models we could build.
A heuristic-based model is a simple "rule of thumb" developed purely by humans. Usually, the heuristic model stems from having an expert knowledge of the problem.
A feature-based model relies heavily on humans modifying the data to create new and meaningful features, which are then fed into a (simple) machine learning algorithm. This approach mixes expert knowledge with learning from data.
An E2E model learns purely from raw data. No human expertise is used, and the model learns everything directly from observations.
In our case, a heuristic-based model could be created to mark all transactions with the TRANSFER transaction type and an amount over $200,000 as fraudulent. Heuristic-based models have the advantage that they are both fast to develop and easy to implement; however, this comes with a pay-off, their...
