Credit risk models can be classified into reduced-form or structural form. This thesis focuses on the structural model with the idea of moving-barrier options to develop a new default prediction scheme. We adopt a flexible and efficient tree algorithm to find the implied barriers to serve as a company's default boundary. Case studies of some well-known companies in the U.S. suggest the implied barrier is very informative about coming defaults. For listed U.S. companies between 1991 and 2018, the proposed implied barrier-based default prediction performs very well in terms of precision and accuracy. This new prediction methodology not only makes the default probabilities operative but is also founded upon good financial rationale.