Financial Statements Fraud and new techniques used to detect it

The main purpose of this study is to analyze fraud in financial statements and new techniques used to discover it in financial statements of Iran stock exchange.

This research is a descriptive research in which to analyze and interpret the types of fraud in financial statements and new techniques used to discover it.

In today's competitive world, organizations face a huge amount of data, and one of the major issues that needs to be addressed, is the issue of financial statement fraud. Because  it cause lack of transparency about operations of the company and cause issues such as asset misappropriation, loss of company credit and so on.

Fraud detection approaches generally include Bayesian Process and Networking approaches for detecting commodity and securities fraud; Bayesian Networking approaches, Genetic Algorithm etc to detect financial statements fraud; machine learning approaches, decision tree etc to detect credit card fraud and logistic regression approach to detect insurance fraud. As a result, all groups involved in fraud detection need these tools and approaches to detect fraud in a timely manner and to inform managers to track and resolve them. In addition, accountants, auditors and managers need to be familiar with new approaches to fraud detection for metadata analysis, thereby analyzing all the risk-related signs of fraud detection and achieving a more rational and transparent result.