1. A method for predicting expected and unexpected loss outcomes for a portfolio of loans using a computer system coupled to a database, the loans issued by a lender to a plurality of borrowers, said method comprising the steps of: recording key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss; recording actual default and loss information in the database for each borrower included within the historical portfolio of loans; comparing the key account and risk attributes with the actual default and loss information over a predetermined period of time, the comparison is performed by the computer system; dividing the historical portfolio of loans into a first sample of loans and a remaining hold-out sample of loans; selecting the first sample of loans from the historical portfolio of loans, the first sample of loans including the key account and risk attributes; determining loss drivers by using the computer system to electronically compare the key account and risk attributes of the first sample of loans with the actual default and loss information of the first sample of loans, the loss drivers being a set of the key account and risk attributes that are predictive of a loss; building, via the computer system, a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes using the first sample of loans, the building of the regression tree based model includes dividing the first sample of loans into buckets of expected and unexpected loss outcomes based on the loss drivers, each bucket showing corresponding expected and unexpected loss outcomes relative to a change in at least one of the loss drivers; determining a final model by analyzing the remaining hold-out sample of loans with the regression tree based model and confirming the predictive accuracy of the final model, the final model representing a relationship between a set of loss drivers, and expected and unexpected loss outcomes; and predicting the expected and unexpected loss outcomes for a second portfolio of loans using the final model and the set of loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender, the prediction is performed by the computer system.