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Showing 4 results for Fraud Detection
Seyed Javad Kazemitabar, Majid Shahbazzadeh,
Volume 17, Issue 1 (6-2020)
Volume 17, Issue 1 (6-2020)
Abstract
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.
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.
Seyed Morteza Seyed Rezaie, Ghorban Kheradmandian, Seyed Javad Kazemitabar Amirkolaie,
Volume 19, Issue 3 (12-2022)
Volume 19, Issue 3 (12-2022)
Abstract
With the advancement of technology, the use of ATM and credit cards are increased. Cyber fraud and theft are the kinds of threat which result in using these Technologies. It is therefore inevitable to use fraud detection algorithms to prevent fraudulent use of bank cards. Credit card fraud can be thought of as a form of identity theft that consists of an unauthorized access to another person's card information for the purpose of charging purchases to the account or removing funds from it. Credit card fraud schemes are divided into two categories: application fraud and account takeover. When a credit card account gets opened without someone’s permission is called application fraud. Account takeovers, on the other hand, is when an existing credit card account is hijacked, and the criminal obtains enough personal information to modify the account's information. The criminal then subsequently reports the card lost or stolen in order to obtain a new card and make unauthorized purchases with it. Data mining as a technique capable of identifying useful patterns among a great deal of data is an effective method in detecting fraud in this regard. The main purpose of this paper is to present a new method for detecting unattended outliers that require high accuracy and recall. The method presented in this study is based on a combination of NMF, hierarchical k-means, k-means and k-nearest neighbors’ techniques. To evaluate the proposed method of outlier detection, several experiments were performed using standard data, in terms of accuracy and recall with Isolation Forest, k-nearest neighbors, Median kNN, and Average kNN. The dataset used in this paper is one that was provided in a 2016 Kaggle competition and was provided by a European bank after anonymization. The results, corroborate that the proposed method has higher accuracy and recall than other algorithms.
Dr. Saeid Bakhtiari, Mrs. Zahra Nasiri, Mr. Seyed Mohammad Sadegh Hejazi,
Volume 19, Issue 4 (3-2023)
Volume 19, Issue 4 (3-2023)
Abstract
As we know, credit cards speed up and make life easier for all citizens and bank customers. They can use it anytime and anyplace according to their personal needs, instantly and quickly and without hassle, without worrying about carrying a lot of cash and more security than having liquidity. Together, these factors make credit cards one of the most popular forms of online banking. This has led to widespread and increasing use for easy payment for purchases made through mobile phones, the Internet, ATMs, and so on. Despite the popularity and ease of payment with credit cards, there are various security problems, increasing day by day. One of the most important and constant challenges in this field is credit card fraud all around the world. Due to the increasing security issues in credit cards, fraudsters are also updating themselves. In general, as a field grows in popularity, more fraudsters are attracted to it, and this is where credit card security comes into play. So naturally, this worries banks and their customers around the world. Meanwhile, financial information acts as the main factor in market financial transactions. For this reason, many researchers have tried to prioritize various solutions for detecting, predicting, and preventing credit card fraud in their research work and provide essential suggestions that have been associated with significant success. One of the practical and successful methods is data mining and machine learning. In these methods, one of the most critical parameters in fraud prediction and detection is the accuracy of fraud transaction detection. This research intends to examine the Gradient Boosting methods, which are a subset of Ensemble Learning and machine learning methods. By combining these methods, we can identify credit card fraud, reduce error rates, and improve the detection process, which in turn increases efficiency and accuracy. This study compared the two algorithms LightGBM and XGBoost, merged them using simple and weighted averaging techniques, and then evaluate the models using AUC, Recall, F1-score, Precision, and Accuracy. The proposed model provided 95.08, 90.57, 89.35, 88.28, and 99.27, respectively, after applying feature engineering and using the weighted average approach for the mentioned validation parameters. As a result, function engineering and weighted averaging significantly improved prediction and detection accuracy.
Zahra Shaeeri, Seyed Javad Kazemitabar, Soroush Haghverdi,
Volume 22, Issue 2 (9-2025)
Volume 22, Issue 2 (9-2025)
Abstract
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
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
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