| چکیده انگلیسی مقاله |
Stock market fraud, particularly front-running, is a deceptive practice in which traders exploit prior knowledge of significant orders placed by others to profit from stock price movements. Front-running is considered illegal because it involves using confidential or non-public information to manipulate the market for personal gain. This paper tries ti propose a novel, unsupervised, and real-time anomaly detection method based on behavioral analysis, specifically designed to identify front-running fraud within stock market transactions. The method focuses on building individual behavioral profiles for each trader, capturing their specific traits and patterns in stock buying and selling. These profiles serve as baselines for what is considered as 'normal' trading behavior for each trader.
To detect anomalies, we introduce a statistical framework where the risk of each transaction will be calculated by evaluating the deviation from the expected behavior based on the trader's historical actions. This deviation is a measure of how unusual the current transaction is in comparison to the trader’s typical actions. The risk calculation involves the use of the log-likelihood ratio, a concept derived from detection theory, which compares the likelihood of a transaction being normal or fraudulent. The conditional probability of a transaction being either fraudulent or non-fraudulent is computed, and the ratio of these probabilities has been taken on a logarithmic scale to define the transaction risk. This risk metric is then utilized to flag potentially suspicious behavior for further investigations.
Bayesian probability theory underpins the model, specifically employing Bayes' rule to update the likelihood of fraud as more data will be accumulated over time. The model assumes the independence of risk components, which simplifies the complexity of the system and improves computational efficiency. Despite the potential limitation of assuming independence, empirical studies have shown that this assumption often yields reliable results for detecting anomalous behavior, making the approach both practical and effective.
Behavioral profiling plays a key role in this method. By observing the individual’s trading history—such as the frequency, timing, and amounts of trades—the system learns a trader’s typical behavior. This behavioral information is critical because it accounts for the natural variance in a trader's actions over time, allowing the model to distinguish between normal fluctuations and abnormal activities that might indicate fraud. Key behavioral indicators include the timing of trades, the volume of trades, the frequency of transactions with specific counterparties, and the trader’s overall market engagement. Traders whose actions significantly deviate from their established patterns—such as purchasing large quantities of stocks at unusual times or interacting with the same trader excessively—are flagged as high-risk.
The simulation section of the paper uses 16 months of stock market transaction data, where features such as transaction amounts, time of trade, urgency, and consistency in trading with particular traders are analyzed to calculate the risk profile. The system ranks traders based on the risk scores of their transactions, enabling the detection of front-running activities in near real-time.
The results from the simulation indicate that the proposed method is highly effective in identifying front-running fraud. The use of behavioral profiling ensures that the system is adaptive to individual trading patterns, making it resistant to the evolving nature of fraud in financial markets. The methodology also provides a significant advantage over traditional rule-based systems, which often struggle to adapt to new fraud techniques as they emerge. Furthermore, this approach can be applied in live trading environments, making it a practical tool for regulatory bodies and market surveillance.
This paper contributes to the growing field of financial fraud detection by introducing an innovative approach that combines behavioral analysis with advanced statistical techniques. The findings underline the importance of real-time monitoring and adaptive fraud detection systems in maintaining market integrity. In the simulation section, stock market data of 16 months is used. Features related to amounts, hours, urgency, and trading with one trader in buying/selling have been used to obtain the ranking. Results show that the proposed method is effective in detecting front running cases |
| کلیدواژههای انگلیسی مقاله |
Stock market fraud detection, behavioral profiling, data analytics, Front running, log-likelihood, Bayes Rule, anomaly detection. |
| نویسندگان مقاله |
زهرا شعیری | Zahra Shaeeri
PHD, Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
دانش آموخته دکترای مهندسی برق، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران
جواد کاظمی تبار | Javad Kazemitabar
Associate Professor, Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
دانشیار گروه مخابرات، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران
سروش حق وردی | Soroush Haghverdi
PHD, Faculty of Management, University of Tehran, Tehran, Iran
دانش آموخته دکتری رشته مدیریت دانشگاه تهران، تهران، ایران
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