Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

Identifying fraudulent financial statements is important in open innovation to help users analyze and make investment decisions. It also helps be aware of the occurrence fraud by considering associated pattern. This study aimed find patterns ratios from on Stock Exchange Thailand using discretization frequent pattern growth (FP-Growth) association rule mining patterns. We found nine related statements. different others that have analyzed mathematics for each item. Moreover, this discovered six items fraud: (1) gross profit, (2) primary business income, (3) ratio income total assets, (4) capitals reserves debt, (5) long-term debt capital reserves, (6) accounts receivable income. The three other were studies focused profit assets.