A team will build a binary fraud detector using only two features: account age and transaction month. The class distributions (shown) suggest certain separability. Which model is likely to achieve the highest accuracy?

The provided image shows a clear non-linear separation between the two classes, making a Support Vector Machine (SVM) with a non-linear kernel (like RBF or polynomial) an excellent choice. SVMs are highly effective at finding optimal separating hyperplanes, especially when the data is not linearly separable, by mapping the data into a higher-dimensional space. Logistic regression is a linear model and would struggle with the non-linear boundary. A single-layer perceptron is also a linear classifier. While LSTMs are powerful for sequential data, this problem involves static, two-dimensional features, making an LSTM an overly complex and inappropriate choice.