One of the questions that I always get when I talk about credit risk modeling (Loan payment default, credit card payment default) is about the algorithms’ or models’ prediction limitations.
How can we implement a solution if the prediction probability is lower? How can we use the model or algorithm effectively for real-world problems?
Have chalked out what are all the available methods to predict the probability of default, while not getting into them detail since that’s not what this article’s intent is.
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