The study aimed at finding out the impact of applying modern financial analysis tools on detecting fraudulent practices in financial statements of listed banks at Damascus stock exchange (DSE). Traditional tools, such as individual (univariate) financial ratios and trend analyses, proved to have lag indicators whereas modern tools, such as quantitative statistical models, have a prediction power and searching for fraudulent practices in businesses. Two models of the independent variable, two types of fraudulent practices of the dependent variable, and one moderating variable were stated. The study adopted a descriptive and analytical approach by analyzing financial statements of a sample of 11 Syrian banks using statistical approaches to test the research hypotheses. Data of this research were gathered based on a series period from 2010 2014, and processed using the statistical package of social sciences (SPSS). The main results of the study are (1) There are impacts of financial analysis tools represented by LEAM2003 and the MJ1995 models on detecting fraudulent practices in listed banks at Damascus stock exchange. This is explained by the existence of many accounting distortions leading to fraudulent accounting practices, such distorting expenses and revenues classifications and deferring these items for future periods (fraud accounting) or recognizing recurring and nonrecurring future expenses this year with the aim of reducing the current year income (big bath accounting), exploiting the flexibility of IFRS and twitting governance procedures and instructions. (2) There is a positive relationship between the bank size and fraudulent practices where management and accountants of banks are able to conceal accounting treatments in order to accommodate local environments. (3) It is more suitable for the Syrian context to apply quantitative statistical models that are more effective in detecting accounting fraudulence compared with behavioral implications and other non-statistical models. The research recommended a way of detecting fraudulent accounting practices by reducing the gap between the tax-based income and the accounting-based income, and applying the benchmarking strategy, such as Sarbanes Oxley Act.