In order to have a fair market condition, it is crucial that regulators continuously monitor the stock market for possible Fraud and market manipulation. There are many types of Fraudulent activities defined in this context. In our paper we will be focusing on "front running". According to Association of Certified Fraud Examiners, front running is a form of insider information and thus is very difficult to detect. Front running is committed by brokerage firm employees when they are informed of a customer's large transaction request that could potentially change the price by a substantial amount. The Fraudster then places his own order before that of the customer to enjoy the low price. Once the customer's order is placed and the prices are increased he will sell his shares and makes profit. Detecting front running requires not only statistical analysis, but also domain knowledge and filtering. For example, the authors learned from Tehran's Over The Counter (OTC) stock exchange officials that Fraudsters may use cover-up accounts to hide their identity. Or they could delay selling their shares to avoid suspicion. Before being able to present the case to a prosecutor, the analyst needs to determine whether predication exists. Only then, can he start testing and interpreting the collected data. Due to large volume of daily trades, the analyst needs to rely on computer algorithms to reduce the suspicious list. One way to do this is by assigning a risk score to each transaction. In our work we build two filters that determine the risk of each transaction based on the amount of statistical abnormality. We use the Chebyshev inequality to determine anomalous transactions. In the first phase we focus on detecting a large transaction that changes market price significantly. We then look at transactions around it to find people who made profit as a consequence of that large transaction. We tested our method on two different stocks the data for which was kindly provided to us by Tehran Exchange Market. The officials confirmed we were able to detect the Fraudster.

Fraud is a global and expanding problem, so that the costs and resources that are lost due to it are very significant and its exact dimensions cannot be determined. this research is aimed at identifying the major causes of Fraud and developing a Fraud detection model for forensic accountants in Iran.A qualitative method based on multi-grounded theory is used. The statistical society of the research includes experienced experts of the Center of Official Justice Experts with at least 10 years of professional work experience and at least a master's degree. The theoretical method was used for sampling. Also, to collect data, 21 in-depth semi-structured interviews were conducted with professional experts, based on the rule of theoretical saturation.Based on the results of data analysis, causal factors affecting Fraud detection are knowledge of different sciences, relevant skills, and abilities, Fraud handling tools, and experience. It also requires strategies at different individual levels, the center of experts and the center of lawyers, the company and the government. To apply and implement strategies, it is necessary to provide a set of contextual conditions including education and research, culture and ethics. Also, the principles of professional ethics, the threat of court accountants, and the lack of supervision of the administrative procedures of cases have a negative impact on the implementation of strategies as intervening conditions. The results of this research can help legislators, regulatory institutions, judicial institutions to improve the quality components of court accounting in order to detect and prevent crimes.

In the last decade, high profile financial Frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement Fraud. The research objects are companies which experienced both Fraudulent and non-Fraudulent financial statements between the years 2011 and 2016. The results show, that artificial neural network perform well relative to a Bayesian network, Discriminant Analysis, logistic regression and Support vector machine. The results also reveal some diversity in predictors used across the classification algorithms. Out of 19 predictors examined, only nine are consistently selected and used by different classification algorithms: Employee Productivity, Accounts Receivable to Sales, Debt-toEquity, Inventory to Sales, Sales to Total Assets, Return On Equity, Return on Sales, Liabilities to Interest Expenses, and Assets to Liabilities. These findings extend financial statement Fraud research and can be used by practitioners and regulators to improve Fraud risk models.

Insurance industry experts believe that Fraud is a destructive disaster in the insurance industry. Over the years, many methods have been used in the literature for Fraud detection, one of which is expert systems. Fraud detection expert systems are based on the knowledge of experts in the field of insurance identifies Fraud. Judgment of experts is mostly based on evidence, documents, qualitative information which is often presented in verbal words to describe the Fraudulent behavior. In the presented model, 61 qualitative and quantitative criteria related to the detection of Fraud in car insurance were identified. Then, these criteria were prioritized according to expert opinion and 17 criteria with the highest priority classified into eight factors were selected. In the suggested system fuzzy inference was performed using Mamdani algorithm. Finally, the designed system was implemented to an Iranian private insurance company and the validity of the system assessed by a questionnaire and came up to 69.45%. The obtained results indicate that the proposed model is able to detect the Fraud quite significantly.