In the Universal Bank data in this chapter only 10% of the records represented customers that had taken out a personal loan (the target variable). If we were to score a new customer based upon the attributes we used in the algorithm, we would be accurate in the prediction about 90% of the time if we always scored the individual as “not accepting a personal loan” because that indeed is what most customers have done in the past. Why not accept being correct 90% of the time with this very simple decision rule?