A banking company wants to build a neural network to predict who will default on 30-year fixed-rate home mortgage loans

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A banking company wants to build a neural network to predict who will default on 30-year fixed-rate home mortgage loans. Historically, approximately 2.5% of individuals default. Given the small percentage of defaulters, what are some of the problems that may be encountered when fitting a neural network model? Is this a problem specific to neural networks, or is this a problem other modeling techniques have as well? What are some of the solutions that can be implemented to overcome the insufficient minority class problem? Provide two or three examples.