Common mistakes when building machine learning models

>When building a binary classifier, many practitioners immediately jump to logistic regression because it’s simple. But, many also forget that logistic regression is a linear model and the non-linear interaction among predictors need to be encoded manually. Returning to fraud detection, high order interaction features like "billing address = shipping address and transaction amount I am not seeing the argument here. Can we just not encode binary conditions with dummy variables?