Credit scoring is a method that banks and financial institutions employ it, with the current and past information of applicants to evaluate the probability of not reimbursement of loans and also to grant them scores. Credit scoring models generally categorize credit applicants based on finanicial factors in to two classes: the "good credit" class that is able to perform financial commitment and the "bad credit" class that should not be granted credit due to the high probability of defaulting on the financial commitments. Credit scoring is an analytical technique for risk assessment. Credit risk is the most challenging risk to which financial institution are exposed. The huge amounts of waste or deferment loans indicate the lack of suitable models and systems to evalute and manage credit risks. Credit scoring system is one ot the main tools to manage and control credit risks. Regarding to the enoumous growth of information and experiences in banking industry, specially in two decades ago and also the growth of potentials credit applicants, it needs to develop more completed and sophisticated models that can automatically perform credit granting and supervise people finanical health. Since an improvement in accuracy even as a small percent might led into significant savings, more sophisticated models should be proposed for significantly improving the accuracy of the credit scoring models. In this thesis we have proposed a hybrid credit scoring model based on Adaboost and Decision Trees Algorithm (DTA). In this model several decision trees are aggregated and formed a powerfull classifier. This process is implemented sequentially. Adaboost assignes a coefficient to each tree base on classification accuracy of the tree and also improvement of previous trees deficiencies on samples classifications
