Outlier Transactions Research Articles

Role of clustering based on density to detect patterns of stock trading deviation

The pattern of deviation patterns can be identified from the results of cluster transactions and transactions that are transaction irregularities, will be detected. DBSCAN as a density-based clustering algorithm forms clusters that agglomerate and make it easier to detect unclustered data, which is considered as data noise (data outlier). The nature of density in the data clamping process will make it easier to determine noise data objects.The DBSCAN has two parameters, Eps and MinPts. The values entered in both parameters play a role in forming clusters. Stock trading transactions are stated as data objects to be clustered. The noise from clustering with DBSCAN shows outlier transactions, which have diferrent pattern with ordinary transactions. In the results of this clustering, the stock transaction pattern which includes outliers is obtained, marking the close occurs. This result can help to detect stock price manipulation in outlier transactions carried out by securities brokers

부정 탐지를 위한 이상치 분석 활용방안 연구 : 농수산 상장예외품목 거래를 대상으로

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

Digital Business Environment and Audit Quality

The event of the global financial crisis is a stack reminder of the importance of audit quality. As various stakeholders grapple with the series of business failures and corporate scandals, the need to re-examine the veracity of audit reports as well as the quality of these audit reports has taking a center stage. Today’s business landscape is increasingly seeing the emergence of new digital technologies like analytics, big data, cloud, social media, mobile etc. Such digital technologies are reshaping existing products, creating new products and services as well as turning digital resources within organizations into a new revenue streams [1-2]. These digital technologies are creating new audit landscape requiring auditors to alter audit processes, controls and tests [3-4]. Navigating through the plethora of cross-functional technologies in a vastly digital world is requiring a new breed of auditors. For instance, cloud computing platforms allow enterprises to run business processes through a network connection while eliminating the conventional IT hardware and software infrastructure that supports these business processes. Auditing in a cloud based enterprise presents various unconventional audit-related risks that may impact on the internal control system and overall audit quality. Risk-based assessment for firms using cloud computing platforms is convoluted because the technology infrastructure and control mechanisms are house and managed outside the enterprise being audited. Cloud computing adds additional layers into the audit procedures as auditors may have to examine the cloud service provider, data center and in some cases third party vendors to establish inherent risk and potential control risk. Similarly, data analytics has become a critical part of the audit process thus, creating the need for auditors to acquire new skills and alter existing audit procedures. With the growing trend of big data and data analytics in audit practices, concerns have been raised about the effectiveness of conventional audit processes and procedures to meet the standards of an evolving audit landscape [1,5-6]. For instance, rather than perform the typical sampling of clients record, big data analytics enables auditors to look at the general ledger and examine outliers and exceptions. In fact, some studies have argued that the underlying assumptions and premises of many audit procedures are no longer applicable to the current auditing environment [6,3]. There is a need for audit firms to seek ways to leverage cutting-edge data analytical tool that will provide greater insights into corporate books and records. Data analytics tools can positively impact audit quality. First, analytics tools can be used to gather insight regarding the level of auditing and the type of procedures to be applied for a particular client. Moreover, analytics tools can also be used to identify potential frauds by picking out deviations and outlier transactions. Emerging digital technologies can provide value-adding benefits to auditors and can potentialy contribute to audit quality. However, auditors have to evolve from the traditional audit procedures and in order to conduct an audit in a post-digital environment. Without a concerted effort, auditors will grapple with clients that have unconventional business models and high digital intensity. As businesses move toward digitization, more concerns will be created for auditors in the areas of access controls and risk of material misstatements. However, auditors must crave new procedures that will leverage cutting-edge data analytical tools and technologies that are capable of providing greater insights into corporate books and records. Such insights will not only make auditors more effective but will also improve the overall audit quality.

Outlier detection in transactional data

Outlier detection is studied in the context of supervised (with class label) and unsupervised (without class label - e.g., clustering) data. To the best of our knowledge there has been no study on outlier detection in transactional database, e.g. market basket data where each transaction has a number of items and the number of items in each transaction is not constant. Following the definition of outlier by Barnett and Lewis, 1994, we define an outlier transaction in this paper as that which appears to deviate markedly from other transactions of the database in which it occurs. This problem is important, for instance a supermarket manager would like to know the outlier transactions so as to make proper decisions. This problem is not trivial particularly when the number of items is large which is often the case in market basket data. We propose a novel and efficient solution DETACH which is different from other work in this area. The proposed method uses a unique approach of creating a representative sample, and subsequently it determines the degree to which each transaction is an outlier considering the representative sample. The proposed method, unlike its predecessors, does not require any parameter that is hard to set. We test our proposed method using benchmark market basket data from QUEST project. Results show that DETACH is very efficient and accurate.

FP-outlier: Frequent pattern based outlier detection

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers.