Transaction Profile Research Articles

Measuring Parent-Child Transactions for Early Identification of Young Autistic Children.

This study explored the salient characteristics of transactions within parent-child engagement and investigated relationships between transactional characteristics and future identification of autism. The main aims of the study were to (1) examine if parents/children and their initial behaviors impact the length of transaction; (2) determine miscue differences among parents and children; and (3) determine if transactional characteristics are predictive of autism at preschool age.The study sample was drawn from extant data of a parent-mediated intervention for young children showing early sings of autism. Thirty parent-child dyad videos were randomly selected and coded for transactions. Statistical analyses were applied to examine the study aims and to perform post-hoc analyses.The length of transaction increased when children initiated with a look cue. Parents displayed a higher proportion of miscues and greater variance in their miscue behavior than their children. Neither the length of transaction nor the proportion of child miscues at 1-year of age predicted an autism diagnosis at preschool age. Post-hoc analyses revealed that girls with high variance of transaction length at 1-year of age, had a lower likelihood of showing autism traits at preschool age.Sustained transactions were more likely when children initiated engagement by looking. Early transactional characteristics were associated with later autism identification among girls, namely longer median transaction length with lower variance of transaction length. This transaction profile is believed to represent high fixation on topics with less ability to explore varied topics.

Human behavior scoring in credit card fraud detection

<span lang="EN-US">Now days, the analysis of the behavior of cardholders is one of the important fields in electronic payment. This kind of analysis helps to extract behavioral and transaction profile patterns that can help financial systems to better protect their customers. In this paper, we propose an intelligent machine learning (ML) system for rules generation. It is based on a hybrid approach using rough set theory for feature selection, fuzzy logic and association rules for rules generation. A score function is defined and computed for each transaction based on the number of rules, that make this transaction suspicious. This score is kind of risk factor used to measure the level of awareness of the transaction and to improve a card fraud detection system in general. The behavior analysis level is a part of a whole financial fraud detection system where it is combined to intelligent classification to improve the fraud detection. In this work, we also propose an implementation of this system integrating the behavioral layer. The system results obtained are very convincing and the consumed time by our system, per transaction was 6 ms, which prove that our system is able to handle real time process.</span>

Transaction Cost Economics Identification in Malaysian Housing Development: A Preliminary Review

Shelter, albeit being one of the crucial physiological needs in Maslow’s Hierarchy of Needs, is still considered as luxury needs as many people still struggle to afford a house especially in Malaysia. The issue stemmed from the mismatch between the affordability level and the housing prices. Many research delved from the demand and supply perspective to find the root cause, however, the problem still prevails. Hence, this research aimed to study the aforementioned issue from the transaction cost economics (TCE)’s perspective. TCE provides a platform to allow parties to deduce the invisible costs from transaction of activities. This can be done by determining the effectiveness of each transaction profile by enhancing the value between the two transactions, hence, lower the costs. This paper outlined a preliminary finding on TCE’s identification in typical housing development process based on RIBA Plan of Work 2013 process protocol. RIBA Plan of Work 2013 was chosen to provide a standard platform of measurement to ensure consistency in activities listed in the pre-contract and post-contract in typical housing development process. Interviews were conducted with purposive sampling with individuals working directly with housing development. The respondents were asked to verify the list of pre-determined TCEs by indicating whether those activities constitute under the TCEs components and whether activities identified as redundant or inefficient can be removed or enhanced. Analysis conducted using the frequency analysis managed to determine the most important and also the most redundant transaction cost components in a typical housing development. The findings revealed an interesting fact where the number of transaction cost components at both pre and post-contract were higher than the anticipated number attained from literature review. Higher number of identified transaction cost components indicates higher transaction cost in housing development. This further suggested that certain activities which were found redundant or inefficient can be either removed or enhanced to lower the transaction costs. The research findings provide a useful platform to further verify the TCE in housing development. The components will be quantified to ultimately develop a framework for a best practice transaction cost economics model to apprehend unaffordable housing issues. Hence, it is noteworthy that combining TCE and Lean Management theory will enhance this on-going research on identification and quantification of the transaction costs within the housing development to address matters that cause high transaction costs that subsequently increases the housing prices.

Cooperative Operation Framework for a Wind-Solar-CCHP Multi-Energy System Based on Nash Bargaining Solution

A multi-energy system can supply both electric and thermal energy simultaneously to the end-users to achieve a high energy use-efficiency. However conventional operational strategies for multi-energy systems, in the existing works, do not consider renewable energy generation plants and CCHP plants as belonging to different interests. To cope with this problem, this article studies a cooperative operation framework model for a Wind-Solar-CCHP Multi-energy system. Instead of the conventional non-cooperative solution-based method, the optimal operation scheduling problem of a multi-energy system is described as an optimization problem of Nash Bargaining. Then the optimization problem of Nash Bargaining for the multi-energy system decomposed into two subproblems: social welfare maximization problem (P1) and energy trading payment problem (P2). The optimal energy transaction profiles can be obtained by solving the P1 problem and the problem of payment of energy trading between PV, WT plant agents and the CCHP plant agent can be obtained by solving the P2 problem. Both problems P1 and P2 can be formulated into mixed-integer linear programming problems and solved by the alternating directional multiplier method. Numerical studies demonstrate the correctness and effectiveness of the proposed cooperative operation framework and distributed algorithm for solving the two subproblems. Moreover, the bargaining-based cooperative operation method can further induce the multi-energy system to improve the operating profit with the reduced feed-in tariff of distributed generation.

Peran Perbankan Dalam Pencegahan Dan Pemberantasan Tindak Pidana Pencucian Uang Yang Dilakukan Oleh Nasabah

Money laundering is rampant in Indonesia with banks as a means. Weaknesses in the banking system provide a loophole for money launderers to carry out their actions so that the proceeds of crime obtained are safely stored in banks. This research is a normative legal research that is descriptive in nature, with a case appoach. The purpose of this paper is to analyze the role of banks in effors to prevent and eradicate money laundering crimes committed by customers in the perspective of banking laws and the mode of action committed by money laundering criminals in banks. The results of this study indicate that themode of conduct of money laundering in conducting money laundering in banks is through capital cooperation though credit collateral, overseas transfers, disguising domestic businessses, engineering foreign loans and the role of banks in preventing and eradicating money laundering and banking laws by recognizing prospective customers who will open accounts in banks and monitoring customer profiles and transactions carried out on an on going basis, including conformity between transaction profile, examining the similarity or similarity of names with names suspects/defendants published in the mass media or by the competent authority.

A Framework to Score the Risk Associated with Suspicious Money Laundering Activity and Social Media Profile

Money laundering has immense entailments. The criminal who possesses black money and wants to mask it as legitimate must fabricate the source to look genuine. It makes the crime organized and more systematic to break the financial system. The existing AML (Anti Money Laundering) solutions and its design based on the creation of a transaction profile. Most of the leading AML software focuses on financial transactions and rarely focuses on linked suspicious individual’s social media profiles. Social networking is one of the most popular platforms to interact with others and millions of users use these platforms to communicate with each other from around the world. At the same time, the web has plenty of social and demographic information to create an accurate profile that aims to construct a legitimate profile. This paper consolidates the fragmented discussion from several articles and provides a detailed view of fraud profile identification. Practical insights are identified from various AML solutions and summarized from an extensive literature review. The risk scoring framework and definitions of filters can be widened to include more parameters for effective alert generation. In this paper, we propose an approach and risk scoring framework to assess customer profiles that drive the suspicious profile or transactions based on social media attributes.

Fuzzy ARM and cluster analysis for database intrusion detection and prevention

Designing and implementation of an intrusion detection system in any database environment has emerged as an absolute necessity in the recent years. Detection of both, the outsider attack and privilege abuse from within the organisation, has become a fundamental need for maintenance of dynamic, scalable and reinforced databases. Proposed advanced approach, malicious query detection using fuzzy and cluster analysis (MQDFCA) operates in a seamless manner and efficaciously performs detection and prevention of transactions that are intrusive in nature, within a database environment, thus shielding the vital data stored in a database from any unauthorised/malicious access or modifications. The method utilises concepts of machine learning like fuzzy logic, association rule mining and clustering algorithms at various stages to validate a newly generated transaction at role segment, profile segment and the rule validation segment. The degree of adherence of user supplied queries within a transaction to the previously generated user roles, transaction profiles and extracted rules is used to categorise the transaction as non-malicious or malicious. The efficaciousness of proposed methodology in detection of intrusions is exemplified from the results of the experiments conducted on the synthetic dataset yielding recall and precision values of 93% and 98% respectively.

The Relationship with Vocational Type and Vocational Classification based on the Ego-State of Transactional Analysis Profile

The Relationship with Vocational Type and Vocational Classification based on the Ego-State of Transactional Analysis Profile

Predicate Offences of Money Laundering and Anti Money Laundering Practices in Bangladesh Among South Asian Countries

Abstract The purpose of the study is to identify the main reasons of money laundering in Bangladesh among the twenty seven predicate offences of money laundering prescribed by Bangladesh Bank and position of Bangladesh among South Asian Countries regarding anti-money laundering practices. Besides, an anti-money laundering model has been developed to combat against money laundering as 14 percent bankers think that only existing know your customer form and transaction profile of banking sector are not enough to detect money laundering. To conduct the study, 91 bankers have been surveyed to take response through structured questionnaire regarding their opinion about the predicate offences of money laundering and sufficiency of existing KYC form of banking sector to detect money laundering. From the responses, factor analysis, test of hypothesis, correlation and regression analysis have been conducted using SPSS software. The study identifies that predicate offences of money laundering can be minimized mainly through scrutinizing the activities of local criminals with foreign network and strict anti-corruption measures through automation in National Board of Revenue, strict policy adoption of criminal detection and support from foreign experts. Besides, regression model shows that only six predicate offences of money laundering explains 87.2 percent of money laundering that should get more emphasize to combat against money laundering. From the comparative analysis, it has been found that Bangladesh in holding better position just after India among six South Asian Countries according to Basel AML Index score. This study provides a complete understanding of the position of Bangladesh in case of money laundering and anti-money laundering practices. The integration of four domains, i.e. AML model development, factor analysis, econometric analysis and comparative analysis of AML index will provide insights to managers and policy makers about the money laundering scenario in Bangladesh.

Integrating frequent pattern clustering and branch-and-bound approaches for data partitioning

In this paper, we propose an approach integrating frequent pattern clustering and branch-and-bound algorithms for finding an optimal database partition. First, the Apriori algorithm and cosine similarity are used to determine weighted frequent patterns according to a transaction profile. On the basis of the weighted frequent patterns, we developed two methods for partitioning a database: the candidate method and the optimal method. The optimal method involves using a branch-and-bound algorithm and considering costs in each step of combining attributes until an optimal solution is reached. Furthermore, we refined the optimal method for expediting the execution by reducing the search space. Finally, the experimental results show that the proposed optimal method performs the highest among all examined methods, and the refined method is considerably more efficient than the original method.

Anomaly detection in performance regression testing by transaction profile estimation

SummaryAs part of the process to test a new release of an application, the performance testing team need to confirm that the existing functionalities do not perform worse than those in the previous release, a problem known as performance regression anomaly. Most existing approaches to analyse performance regression testing data vary according to the applied workload, which usually leads to the need for an extra performance testing run. To ease such lengthy tasks, we propose a new workload‐independent, automated technique to detect anomalies in performance regression testing data using the concept known as transaction profile (TP). The TP is inferred from the performance regression testing data along with the queueing network model of the testing system. Based on a case study conducted against two web applications, one open source and one industrial, we have been able to automatically generate the ‘TP run report’ and verify that it can be used to uncover performance regression anomalies caused by software updates. In particular, the report helped us to isolate the real anomaly issues from those caused by workload changes with an average F1 measure of 85% for the open source application and 90% for the industrial application. Such results support our proposal to use the TP as a more efficient technique in identifying performance regression anomalies than the state of the art industry and research techniques. Copyright © 2015 John Wiley & Sons, Ltd.

Detecting Fraud Using Transaction Frequency Data

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. In this paper, we present a fraud detection method which detects irregular frequency of transaction usage in an Enterprise Resource Planning (ERP) system. We discuss the design, development and empirical evaluation of outlier detection and distance measuring techniques to detect frequency-based anomalies within an individual user’s profile, relative to other similar users. Primarily, we propose three automated techniques: a univariate method, called Boxplot which is based on the sample’s median; and two multivariate methods which use Euclidean distance, for detecting transaction frequency anomalies within each transaction profile. The two multivariate approaches detect potentially fraudulent activities by identifying: (1) users where the Euclidean distance between their transaction-type set is above a certain threshold and (2) users/data points that lie far apart from other users/clusters or represent a small cluster size, using k-means clustering. The proposed methodology allows an auditor to investigate the transaction frequency anomalies and adjust the different parameters, such as the outlier threshold and the Euclidean distance threshold values to tune the number of alerts. The novelty of the proposed technique lies in its ability to automatically trigger alerts from transaction profiles, based on transaction usage performed over a period of time. Experiments were conducted using a real dataset obtained from the production client of a large organization using SAP R/3 (presently the most predominant ERP system), to run its business. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Hybrid segmentation of internet banking users

PurposeThe objective of this paper is to present and discuss the development of a transaction‐based model for segmenting users of internet banking. It aims to employ a random sample of clients of a large Canadian bank in generating the hybrid segments.Design/methodology/approachThe basic transactional profile of the bank's clients was merged with Mosaic's financial segments contained in the Generation5 database. A random sample of 3 percent of a large Canadian chartered bank's clients was drawn from its transaction database. The transaction database employed contains clients from Quebec and the Maritime provinces. The sampling frame consisted of close to one million clients. Two‐step cluster analysis was employed to generate the transaction segment and later merged with the Mosaic financial segment to produce hybrid segments.FindingsTwo‐step cluster analysis identified four generic transaction segments which, when cross‐tabulated with the Mosaic financial segments, produced highly stable and interpretable segments. These hybrid segments are clearly superior to conventional life style or psychographic segments produced by classical segmentation methodologies.Practical implicationsThe results of this study clearly demonstrate the functional and analytical superiority of hybrid customer segments. Hybrid segmentation, by cross‐tabulating transaction and Mosaic's financial segments, provides banks and financial institutions with superior strategic insights in customer understanding, customer segmentation, customer communication, customer prospecting and targeting.Originality/valueThis paper is the first to present, explain and to demonstrate the nature and the operational procedure to develop hybrid customer/client segments. More importantly, it is the first that goes beyond conventional approaches to segmenting banks' clients who engage in internet banking by integrating clients' transaction profiles and Mosaic financial segments. The resulting hybrid segments are radically different than the conventional, one‐dimensional segments produced by conventional cluster‐based segmentation.

Transaction Mining for Fraud Detection in ERP Systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Whodunit

This paper is concerned with performance debugging of multi-tier applications, such as commonly found in servers and dynamic-content web sites. Existing tools and techniques for profiling such applications are not general enough to track and profile transactions in a generic multi-tier application. We propose transactional profiling that provides a general solution to this problem. We provide novel algorithms and techniques to track and profile transactions that flow through shared memory, events, stages or via interprocess communication using messages. We also measure interference among concurrent transactions. We describe the design and implementation of Whodunit, our prototype transactional profiler. We demonstrate the correctness of our proposed algorithm for tracking transaction flow through shared memory using Apache and MySQL. Using Whodunit we are able to track and profile transactions that flow through shared memory, events, stages or via message passing, and measure the interference among concurrent transactions. We illustrate the use of Whodunit in obtaining the transactional profile of web servers, a web proxy cache and a bookstore application.

Horizontal and vertical takeover and sell-off announcements: Abnormal returns differ by industry

We begin with the hypothesis that shareholder-wealth effects of corporate transactions differ depending on (a) the specific industry, (b) whether they are horizontal or vertical, and (c) whether they are integrations (takeovers) or disintegrations (partial sell-offs). A standard event study analysis for cumulative abnormal returns based on the market model is conducted for 309 data-points from 227 transaction announcements. We find that abnormal returns indeed do significantly depend on the transaction profile (horizontal vs. vertical and integration vs. disintegration) and industry. One main result is the observation that the capital market shows distinctive preferences for either an integration or disintegration strategy for vertical as well as for horizontal transactions. The specific pattern of this preference differs according to industry

Real-time data attack isolation for commercial database applications

Traditional database security mechanisms are very limited in defending against data attacks. Authorized but malicious transactions can make a database useless by impairing its integrity and availability. This paper presents the design of a real-time data attack isolation system, denoted DAIS. DAIS isolates likely suspicious actions before a definite determination of intrusion is reported. In this way, the database can be immunized from many malicious transactions. DAIS is a COTS-DBMS-specific implementation of a general isolation algorithm that we developed [Liu P, Jajodia S, McCollum CD. Intrusion confinement by isolation in information systems. Journal of Computer Security, 2000;8(4):243–79]. In this paper, the design of the first DAIS prototype, which is for Oracle Server 9.2, is discussed. DAIS uses triggers and transaction profiles to keep track of the items read and written by transactions, isolates attacks by rewriting user SQL statements, and is transparent to end users. The DAIS design is very general. In addition to Oracle, it can be easily adapted to support many other database application platforms such as Microsoft SQL Server, Sybase, and Informix.

Performance evaluation of a distributed concurrency control algorithm

Concurrency control (CC) algorithms are important in distributed database systems to ensure consistency of the database. A number of such algorithms are available in the literature. The issue of performance evaluation of these algorithms has been recognized to be important. However, only a few studies have been carried out towards this. This paper deals with the performance evaluation of a CC algorithm proposed by Rosenkrantz et al. through a detailed simulation study. In doing so, the algorithm has been modified so that it can, within itself, take care of the redundancy in the database. The influences of various system parameters and the transaction profile on the response time and on the degree of conflict are considered. The entire study has been carried out using the programming language SIMULA on a DEC-1090 system.