Seyed Mahdi Sadatrasoul - Information Technology Management department, Management school, Kharazmi, Tehran, Iran.
Omid Mahdi Ebadati - Information Technology Management department, Management school, Kharazmi, Tehran, Iran.
Amir Amirzadeh Irani - Information Technology Management department, Management school, Kharazmi, Tehran, Iran.

Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s incomes, costs, expenses, assets and liabilities. Smoothing can affect credit scoring models reliability, it can cause to providing/not providing facilities to a non-worthy/worthy organization orderly, which are both known as decision errors and are reported as “type I” and “type II” errors, which are very important for Banks Loan portfolio. This paper investigates this issue for the first time in credit scoring studies on the authors knowledge and searches. The data of companies associated with a major Asian Bank are first applied using logistic regression. Different smoothing scenarios are tested, using wilcoxon statistic indicated that traditional credit scoring models have significant errors when smoothing procedures have more than ۲۰% change in adjusting company’s financial statements and balance sheets parameters.

data mining, Credit scoring, credit models, revenue smoothing, Stacking