Management Information Systems | Open Access Articles

Online Payment Gateways Used To Facilitate E-Commerce Transactions And Improve Risk Management, Paul Benjamin Lowry, Taylor Michael Wells, Greg Moody, Sean Lamarc Humpherys, Degan Kettles

Faculty Publications

As online transactions continue to increase and become a significant part of the global economy, the ability to accept payments online becomes more important for businesses. This paper evaluates the literature and provides current information for IS researchers and instructors focusing on electronic commerce. In this paper, we explore the components of e-credit providers (conventional, person-to-person, and third-party) and explain how each system processes a single transaction. We then analyze several market leaders in each segment and summarize the strengths and weaknesses of each company. We provide guidelines for selecting an e-credit provider and highlight the options that apply best …

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A Semantic Web Data Retrieval Implementation With An Adaptive Model For Supporting Agent Decision Structures, James V. Hansen, James B. Mcdonald, Conan C. Albrecht, Douglas L. Dean, Bonnie B. Anderson

Faculty Publications

This paper proposes an adaptive learning approach that yields decision models that can be applied by a transactions agent. This model can learn effectively with a variety of data distributions. This research used the Semantic Web as a data access approach. The Semantic Web is a method that sellers can use to publish semantically meaningful information on Websites so that automated applications can reliably access that information. We implemented a Semantic Web composed of 30 vendors’ Web pages and a spider to search those pages to obtain product and vendor information. This information was used to train a learning agent, …

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Deception Detection Through Automatic, Unobtrusive Analysis Of Nonverbal Behavior, Thomas O. Meservy, Matthew L. Jensen, John Kruse, Judee K. Burgoon, Jay F. Nunamaker Jr., Douglas P. Twitchell, Gabriel Tsechpenakis, Dimitris N. Metaxas

Faculty Publications

Every day, hundreds of thousands of people pass through airport security checkpoints, border crossing stations, or other security screening measures. Security professionals must sift through countless interactions and ferret out high-risk individuals who represent a danger to other citizens. During each interaction, the security professional must decide whether the individual is being forthright or deceptive. This task is difficult because of the limits of human vigilance and perception and the small percentage of individuals who actually harbor hostile intent. Our research initiative is based on a behavioral approach to deception detection. We attempted to build an automated system that can …

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Dependability Auditing With Model Checking, Bonnie Anderson, James Hansen, Paul Benjamin Lowry

Faculty Publications

Model checking offers a methodology for determining whether a model satisfies a list of correctness requirements. We propose a theory of dependability auditing with model checking based on four principles: (1) The modeling process should be partitioned into computational components and behavioral components as an aid to system understanding; (2) The complex system will be abstracted to create a model; (3) A language must be available that can represent and evaluate states and processes that evolve over time; (4) Given an adequate model and temporal specifications, a model checker can verify whether or not the input model is a model …

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Data Mining Of Forensic Association Rules, James V. Hansen, Paul Benjamin Lowry, Rayman D. Meservy

Faculty Publications

Data mining offers a potentially powerful method for analyzing the large data sets that are typically found in forensic computing (FC) investigations to discover useful and previously unknown patterns within the data. The contribution of this paper is an innovative and rigorous data mining methodology that enables effective search of large volumes of complex data to discover offender profiles. These profiles are based on association rules, which are computationally sound, flexible, easily interpreted, and provide a ready set of data for refinement via predictive models. Methodology incorporates link analysis and creation of predictive models based on association rule input.

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An Adaptive Learning Model Which Accommodates Asymmetric Error Costs And Choice-Based Samples, James V. Hansen, James B. Mcdonald, Rayman D. Meservy

Faculty Publications

This paper introduces an adaptive-learning model, EGB2, which optimizes over a parameter space to fit data to a family of models based on maximum-likelihood criteria. We also show how EGB2 can be modified to handle asymmetric costs of Type I and Type II errors, thereby minimizing misclassification costs. It has been shown that standard methods of computing maximum-likelihood estimators of qualitative-response models are generally inconsistent when applied to sample data with different proportions than found in the universe from which the sample is drawn. We investigate how a choice estimator, based on weighting each observation's contribution to the log-likelihood function, …

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Case-Based Reasoning: Application Techniques For Decision Support, James V. Hansen, Rayman D. Meservy, Larry E. Wood

Faculty Publications

Decision-support systems can be improved by enabling them to use past decisions to assist in making present ones. Reasoning from relevant past cases is appealing because it corresponds to some of the processes an expert uses to solve problems quickly and accurately. All this depends on an effective method of organizing cases for retrieval. This paper investigates the use of inductive networks as a means for case organization and outlines an approach to determining the desired number of cases-or assessing the reliability of a given number. Our method is demonstrated by application to decision making on corporate tax audits.

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Learn Audit Selection Rules From Data: A Genetic Algorithms Approach, David P. Greene, Rayman D. Meservy, Stephen F. Smith

Faculty Publications

The construction of expert systems typically require the availability of expertise that can be modeled. However, there are many important problems where no expertise exists, yet there is a wealth of data indicating results in different situations. Machine learning algorithms attempt to discover rules which capture the regularities that exists in such data.

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Case-Based Reasoning And Risk Assessment In Audit Judgment, Eric L. Denna, James V. Hansen, Rayman D. Meservy, Larry E. Wood

Faculty Publications

The purpose of this paper is to describe the results of an effort to utilize Case Based Reasoning (CBR) to model a specific audit judgment task. To date most efforts to develop computational models of audit judgment have used strictly rule-based representation methods. Some researchers have recently adopted more robust structures to model the auditor domain knowledge. Although these recent efforts to extend the representation methods appear to be more accurate descriptions of auditor reasoning and memory, they still lack a comprehensive. theory to guide the development of the model. A commonly encountered phenomenon in audit judgment is for an …

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Investigating Expertise In Auditing, Paul E. Johnson, Andrew D. Bailey Jr, Rayman D. Meservy

Faculty Publications

Research on human expertise in auditing contexts is an important area of study. Our objective in this paper is to present an approach to conducting research on human expertise. We begin with some issues of terminology and then propose a strategy for inquiry into the phenomena of expertise. Finally, we attempt to illustrate this strategy with three examples of recently completed work. The examples include: two from the auditing literature that involve the analysis of internal control and a medical diagnosis case which illustrates the "garden path" problem that is of concern in any decision making context, including auditing.

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Auditing, Artificial Intelligence And Expert Systems, Andrew D. Bailey, Rayman D. Meservy, Gordon L. Duke, Paul E. Johnson, William Thompson

Faculty Publications

This paper will provide the reader with an introduction to the field of financial auditing and the applicability of Decision Support Systems (DSS), Artificial Intelligence (AI), and Expert Systems (ES) to that field of endeavor. The paper will also discuss a continuing research project concerning the application of DSS/AI/ES techniques to the evaluation of internal accounting controls. The reader will find that the evaluation of internal accounting controls is a critical step in every financial audit and that it is an area in which the auditor exhibits substantial expertise. It is thus an area of work particularly suited to the …

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Kass: A Knowledge-Based Auditor Support System, Ramayya Krishnan, Rayman D. Meservy, Vandana Gadh

Faculty Publications

This paper describes the design of a knowledge-based system to assist auditors in the evaluatation of internal accounting controls and focusses on the logic-based language AL that has been developed as a knowledge representation formalism. Interesting features of AL include a declarative approach to modeling accounting systems and the means to explicitly describe authority structures typically used to enforce internal controls.

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Internal Control Evaluation: A Computational Model Of The Review Process, Rayman D. Meservy, Andrew D. Bailey Jr, Paul E. Johnson

Faculty Publications

This study investigated the strategies by which experienced auditors evaluate systems of internal accounting controls. The research method included: (1) observations, using concurrent protocols, of a small sample of practicing auditors performing the internal control evaluation task; (2) extensive interviews with one of the practicing auditors; (3) formalization of auditor processes as a computational model; and (4) validation of the model. The simulation model was Im· plemented as an expert system and tuned to one auditor. The model output consists of a trace of the model processing including: (1) rec• ommendatlons for specific controls to be compliance tested; and (2) …

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A Mathematical Contraction And Automated Analysis Of Internal Controls, Andrew D. Bailey Jr, Gordon Leon Duke, James Gerlach, Chen-En Ko, Rayman D. Meservy, Andrew B. Whinton

Faculty Publications

This paper presents a precedent oriented computer assisted method of designing, analyzing and evaluating systems of internal controls. The ..!..nternal Control _!iodel, TICOM III, is fundamentally a design and analysis tool useful in the context of Automated Office Information Systems (AOIS's). Traditional analysis and evaluation tools, such as flowcharting, written narratives and questionnaires, are inefficient, if not insufficient, to support the design and control analysis of these new systems. The advantages of TICOM III over traditional methods such as narrative description, questionnaires and flowcharts are: (1) the evaluation can be more rigorous and exhaustive because of the speed, accuracy and …

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Full-Text Articles in Management Information Systems

Online Payment Gateways Used To Facilitate E-Commerce Transactions And Improve Risk Management, Paul Benjamin Lowry, Taylor Michael Wells, Greg Moody, Sean Lamarc Humpherys, Degan Kettles

Faculty Publications

A Semantic Web Data Retrieval Implementation With An Adaptive Model For Supporting Agent Decision Structures, James V. Hansen, James B. Mcdonald, Conan C. Albrecht, Douglas L. Dean, Bonnie B. Anderson

Faculty Publications

Deception Detection Through Automatic, Unobtrusive Analysis Of Nonverbal Behavior, Thomas O. Meservy, Matthew L. Jensen, John Kruse, Judee K. Burgoon, Jay F. Nunamaker Jr., Douglas P. Twitchell, Gabriel Tsechpenakis, Dimitris N. Metaxas

Faculty Publications

Dependability Auditing With Model Checking, Bonnie Anderson, James Hansen, Paul Benjamin Lowry

Faculty Publications

Data Mining Of Forensic Association Rules, James V. Hansen, Paul Benjamin Lowry, Rayman D. Meservy

Faculty Publications

An Adaptive Learning Model Which Accommodates Asymmetric Error Costs And Choice-Based Samples, James V. Hansen, James B. Mcdonald, Rayman D. Meservy

Faculty Publications

Case-Based Reasoning: Application Techniques For Decision Support, James V. Hansen, Rayman D. Meservy, Larry E. Wood

Faculty Publications

Learn Audit Selection Rules From Data: A Genetic Algorithms Approach, David P. Greene, Rayman D. Meservy, Stephen F. Smith

Faculty Publications

Case-Based Reasoning And Risk Assessment In Audit Judgment, Eric L. Denna, James V. Hansen, Rayman D. Meservy, Larry E. Wood

Faculty Publications

Investigating Expertise In Auditing, Paul E. Johnson, Andrew D. Bailey Jr, Rayman D. Meservy

Faculty Publications

Auditing, Artificial Intelligence And Expert Systems, Andrew D. Bailey, Rayman D. Meservy, Gordon L. Duke, Paul E. Johnson, William Thompson

Faculty Publications

Kass: A Knowledge-Based Auditor Support System, Ramayya Krishnan, Rayman D. Meservy, Vandana Gadh

Faculty Publications

Internal Control Evaluation: A Computational Model Of The Review Process, Rayman D. Meservy, Andrew D. Bailey Jr, Paul E. Johnson

Faculty Publications

A Mathematical Contraction And Automated Analysis Of Internal Controls, Andrew D. Bailey Jr, Gordon Leon Duke, James Gerlach, Chen-En Ko, Rayman D. Meservy, Andrew B. Whinton

Faculty Publications