I haven’t looked at the dataset they use for that tutorial, but I assume that the lower threshold is necessary because of class imbalance, i.e., there are many more loans that did not default than those that did. Without balancing the data and with a threshold of 0.5, the model will most likely always predict non-default. They’ve chosen to deal with that by using a low threshold so that the predictions are more sensitive to detecting defaults.