Credit scoring is an application of financial risk forecasting to consumer lending. In this study, statistical analysis is applied to credit scoring data from a financial institution to evaluate the default risk of consumer loans. The default risk was found to be influenced by the spread, the age of the consumer, the number of credit cards owned by the consumer. A lower spread, a higher number of credit cards and a younger age of the borrower are factors that decrease the risk of default. Clients receiving the salary in the same banking institution of the loan have less chances of default than clients receiving their salary in another institution. We also found that clients in the lowest income tax echelon have more propensity to default.