Explain in easy to understand sentences and briefly summarize it

HOW TO DETECT FRAUDULENT FINANCIAL STATEMENT IN T Data mining method could identify valuable events that are hidden in large amounts of data for analysis, and summarizes the data in a structured model to provide a reference for decision-making. Data mining has many different functions, such as classification, association, clustering and forecasting (Seifert 2004). The classification function is used more frequently and the result can be used as the basis for decision making and prediction purposes. Regarding the issue of fraudulent financial statements, much of the past research has suggested the use of the data mining method because of its superiority in terms of forecasting after inputting large amounts of data for machine learning, as well as its accuracy in terms of classification and forecasting, which is far higher than that of conventional regression analysis. For example, artificial neural network (ANN), ANN is a system that imitates a biological neural network's computational capabilities because biological vision and hearing capabilities are superior to computer systems at the time. Therefore, it is expected to gain powerful computational capabilities by imitation. Second is decision tree (DT), DT is the simplest inductive learning method (Arminger et al. 1997). It is able to handle continuous and non- continuous variables. The generated DT can make out-of-sample predictions. Third is Bayesian belief network (BBN), plays an important role in issues of uncertainty and inference and has applied in many cases, such as natural resources (Newton et al. 2007), and medical diagnosis and software cost evaluation (Stamelos et al. 2003). Its inference depends on the acquisition of new information, and lastly, support vector machine (SVM). SVM is a set of artificial intelligent learning methods proposed by Varnik (1995). It is a machine learning method based on statistical learning theory and SRM (structural risk minimization). It primarily depends on using input training data to find an optimal separating hyperplane that can distinguish two or more types (class) of data through the learning mechanism. These all methods have been applied in order to detect fraudulent financial statements