Experience, instruction and knowledge acquisition: a study in taxation

This study examines the effects of direct work experience, gained through internships and cooperative educational experiences, on subsequent knowledge acquisition. In particular, theoretical and empirical research in education, cognitive psychology and accounting suggests that experienced individuals develop organizational knowledge structures which allow them to better assimilate new information into memory than inexperienced individuals. Consistent with this notion, the research investigates whether the benefits of prior direct work experience on knowledge acquisition is a function of the nature of the learning tasks (structured versus unstructured) used in subsequent educational experiences.As hypothesized, the results indicate that inexperienced subjects were assisted to a greater extent in the knowledge acquisition process by structure oriented tax return preparation tasks than by relatively unstructured tax research assignments. Also consistent with expectations, the opposite was true for experienced subjects. When experienced subjects were provided with instruction supplemented by unstructured tax research assignments, they demonstrated a greater level of tax knowledge than when assigned structured tax return problems. Further, the knowledge level of experienced subjects was found to be greater than that of inexperienced subjects only when unstructured tax research assignments were provided. These findings suggest that all benefits of direct work experience on the knowledge acquisition process cannot be realized without the specific consideration of such experiences on the design of educational and firm training materials. Finally, the study also indicates that the benefits of direct work experience on subsequent knowledge acquisition are generally greater for individuals with low versus high learning aptitudes.     

An ontological artifact for classifying social media: Text mining analysis for financial data

In this paper we utilize a structured natural language processing implementation of the Financial Industry Business Ontology (FIBO) to extract financial information from the unstructured textual data of the social media platform Twitter regarding financial and budget information in the public sector, namely the two public-private agencies of the Port Authority of NY and NJ (PANYNJ), and the NY Metropolitan Transportation Agency (MTA). This research initiative uses the Design Science Research (DSR) perspective to develop an artifact to classify tweets as being either relevant to financial bonds or not. We apply a frame and slot approach from the artificial intelligence and natural language processing literature to operationalize this artifact. FIBO provides standards for defining the facts, terms, and relationships associated with financial concepts. We show that FIBO grammar can be used to mine semantic meaning from unstructured textual data and that it provides a nuanced representation of structured financial data. With this artifact, social media such as Twitter may be accessed for the knowledge that its text contains about financial concepts using the FIBO ontology. This process is anticipated to be of interest to bond issuers, regulators, analysts, investors, and academics. It may also be extended towards other financial domains such as securities, derivatives, commodities, and banking that relate to FIBO ontologies, as well as more generally to develop a structured knowledge representation of unstructured data through the application of an ontology.     

The ‘SKADE LITorSET’ Expert System for Corporate ‘Litigate or Settle’ Decisions

The ‘SKADE LITotSET’ system is a blackboard-based expert system that makes ‘litigate or settle’ decisions in the product liability area. It has three knowledge sources: Legal, Manager and Insurance Adjuster. The combined expertise from each of these is required to solve the ‘litigate or settle’ problem. The control component co-ordinates the interaction between the various knowledge sources on the blackboard. Based on the latest changes to the data on the blackboard, it selects and executes the next knowledge source. The model reproduces the decision makers' opportunistic reasoning processes by the interaction between the various knowledge sources through the blackboard. The results of analyses of a hypothetical case through a series of experiments with the ‘SKADE LITorSET’ system indicate that the blackboard is an appropriate model for development of expert systems in the ‘litigate or settle’ decision domain. The initial success with the blackboard approach suggests that further work needs to be done to see whether more complex models can be built to incorporate a broader range of determinants of settlement decisions.     

A Case-Based Reasoning System for Indirect Bank Lending

By implementing case-based reasoning (CBR) systems, business organizations can utilize past cases—a key data resource—for future decision making. CBR is particularly suitable for business domains that have available a large amount of historical data. One such domain is indirect bank lending. In this paper, we present a case-based system that operates in the bank lending domain. The system recommends whether an indirect loan application should be approved or denied, based on past experiences. We describe how the system was developed and explain how the system functions. The system was empirically evaluated using actual loan cases. The positive results of the evaluation confirm our hypothesis that CBR is an attractive decision-making methodology for the bank lending domain.     

Constructing a knowledge‐based system to aid scenario‐based strategic planning: an application to the European airline industry

This paper explores the use of scenario planning and the design of a knowledge‐based system in strategic decision making, in the context of the European airline industry. Several innovative strategies were derived, as well as other key recommendations based on sound strategic reasoning, and participants testified to the effectiveness of the approach in stretching their thinking. The requirement to draft strategies as expert system rules, with reasons, was useful in clarifying thinking and achieving group consensus. This methodology, therefore, aids effectiveness of the scenario planning process itself, while providing a dynamic, accessible means of storing the resulting strategic thinking. Copyright © 2005 John Wiley & Sons, Ltd.     

Development of a case‐based decision support system for process modeling in BPR

This paper presents a knowledge‐based methodology for business process reengineering that uses a case‐based reasoning paradigm to provide decision support to its users in the modeling of a current problem and a redesign of critical business processes. As a process modeling tool for representing the business process, the event process chain (EPC) modeling method is used in this paper. We developed a CAPMOSS (CAse‐based Process MOdeling Supporting System) to support our proposed methodology. To reengineer a new business process problem, CAPMOSS retrieves from its case base the case that is most similar to the current problem. CAPMOSS uses a retrieved case to guide the structuring of AS‐IS models and TO‐BE models of a target business process. Using the transformational knowledge of a retrieved case, CAPMOSS helps the user to transform an AS‐IS model into a TO‐BE model for the target process with ease and the purchasing process in a government R&D institute is explained as an application of this approach. Copyright © 2000 John Wiley & Sons, Ltd.     

A methodology for modeling influence diagrams: a case-based reasoning approach

In this paper, a case-based reasoning approach to build an influence diagram is described. Building an influence diagram in decision analysis is known to be a most complicated and burdensome process. To overcome such a difficulty, decision class analysis is suggested, which treats a set of decisions having some degree of similarity as a single unit. This research suggests a case-based reasoning approach as a methodology to analyze a class of decisions. The candidate influence diagrams are retrieved from a set of similar influence diagrams, a case base. They are combined and modified by the node classification tree and DM’s preference for the given decision problem. For such a purpose, the case representation and retrieval process is explained with the adaptation process. We suggest using two measure, the fitness and garbage ratio for the case retrieval process. The basic concept of decision class analysis and case-based reasoning is very similar so the case-based reasoning approach is believed to be a better methodology to implement a decision class analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

Case-based Reasoning and Multiple-agent Systems for Accounting Regulation Systems with Extensions

One of the areas of judgment research in accounting and financial applications is that of accounting regulation. Previously, artificial intelligence efforts at modeling human judgment in accounting regulation systems have concentrated on rule-based expert systems. In those systems, general heuristic knowledge was captured using ‘if … then …’ rules in order to model particular decision processes. Recent developments in artificial intelligence have focused on case-based reasoning (CBR) and multiple-agent intelligent systems (MAIS). The ideas behind CBR are that ‘if it worked once then remember to use it again’ and ‘if it did not work before, then remember to not use it again‘. MAIS assumes that many organizational systems can be treated as computational models of multiple-interacting intelligent agents. Typically, solutions may be derived using some form of negotiations between agents to accomplish single global or separate individual interacting goals. This paper argues that many accounting regulation judgment processes can be modeled using CBR and MAIS. As a result, it summarizes some examples of both CBR and MAIS useful in accounting regulation and extends those to other accounting applications. In addition, it describes the results of some previously developed systems that employ CBR or MAIS.     

Automated Induction of Rule-based Neural Networks from Databases

This paper describes our approach to the problem of automated knowledge acquisition from large databases of examples using an information-theoretic approach. Our previous research has resulted in practical algorithms (ITRULE) for the automatic induction of rules from large example databases. Utilizing these algorithms, the raw data can be transformed into a set of human readable IF THEN rules, thus giving insight into the knowledge hidden within the data. These rules can then be automatically loaded into an expert system shell. Alternatively, they can be used to build a new type of parallel inference system—a rule-based neural network. This process enables a prototype expert system to be automatically generated and up and running in a matter of minutes, compared with months using a manual knowledge-acquisition approach. The resulting expert system can then be used as a sophisticated search and analysis tool to query the original database capable of reasoning with uncertain and incomplete data.     

Decision‐making capabilities of a hybrid system applied to the auditor's going‐concern assessment

The purpose of this study is to evaluate a hybrid system as a decision support model to assist with the auditor's going‐concern assessment. The going‐concern assessment is often an unstructured decision that involves the use of both qualitative and quantitative information. An expert system that predicts the going‐concern decision has been developed in consultation with partners at three of the Big Five accounting firms. This system is combined with a statistical model that predicts bankruptcy, as a component of the auditor's decision, to form a hybrid system. The hybrid system, because it combines the use of quantitative and qualitative information, has the potential for better prediction accuracy than either the expert system or statistical model predicting separately. In addition, testing of the system provides some insight into the characteristics of firms that experience problems, but do not necessarily receive a going‐concern modification. Further investigation into those firms that have problems could reveal factors that may be incorporated into decision support systems for auditors, in order to improve accuracy and reliability of these decision tools. © 2001 John Wiley & Sons, Ltd.     

Toward a better expert system for auditor going concern opinions using Bayesian network inflation factors

We develop an analytical model intended as the first stage in the development of expert systems to improve auditor knowledge in, and assist in the decision process of, Going Concern Opinions (“GCOs”). Our approach is consistent with a design science approach to developing information systems, resulting in an initial artifact, the mathematical model, which can, through iterative design science and behavioral research, inform a technology-based expert system. Based on Bayesian networks, our model provides insights about auditors’ revision, or inflation, of the probability to issue a GCO based on the interrelationship that forms with the incremental existence of one, two, or three publicly observable financial statement risk factors – net operating loss, negative cash flows from operations, and negative working capital. We calculate the revised probabilities using empirical data of GCOs from 2004 to 2015. Results reveal that the incremental relationship (one, two, or three factors present) effectively models expert auditors’ decisions to issue a GCO, and suggests the existence of these measurable inflation factors that represent situational and auditor-specific factors. We also find that Non-Big Four auditors inflate these factors differently than Big Four auditors to arrive at a decision to issue a GCO.     

Case-Based Reasoning: Application Techniques for Decision Support

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.     

Assessing inherent risk during audit planning: The development of a knowledge based model

Within the academic and professional auditing communities there has been growing concern with accurately assessing the various risks associated with the performance of an audit. One approach to developing sophisticated risk assessment models is to study how experienced expert auditors use industry and firm specific factors in making audit judgements. This study presents a model of inherent risk assessment based on literature reviews and a field study that involved structured and unstructured interviews and observations of experts in audit planning meetings. Analysis of the data gathered led to the specification of a conceptual model of inherent risk assessment which has been implemented as a computer program (a computational model). Auditors were asked to assess the behavior and performance of the computational model as a first step in evaluating the expert model.     

A Multi-Stage Model of Decision Bias: Implications for Expert Systems

The study of the bias processes that affect decision making is crucial in designing expert systems. This study proposes a multi-stage model for decision biases which reconceptualizes cognitive styles and decision heuristics within a framework that borrows heavily from research by Posner and McLeod (1982), Tversky and Kahneman (1973) and Ramaprasad (1987). The framework is tested within the context-bound area of loan making so that the biasing effects of prior experience on decision making can be examined. The results are analyzed with a path-modeling technique (i.e. covariance structural modeling) that allows testing for indirect as well as direct effects. The results are discussed in terms of the implications for expert systems development     

The development of an expert system for managerial evaluation of internal controls

Both practitioners and researchers have devoted signi?cant effort to the study of decision aids, especially expert systems, to assist auditors in internal control evaluations. In addition to being used as a decision aid, researchers have long contended that expert systems could be used to train non‐expert users. Even though the professional accounting literature makes it clear that responsibility for maintaining an effective internal control system rests with management rather than auditors, the focus to date has been on expert systems aimed at assisting/training auditors, not an organization's management. In contrast, this study focuses on management as users of an expert system for internal control evaluation. We describe the development process, explain how the resulting system was evaluated, and discuss results of that evaluation. These results suggest that such a system gives a new way to help managers increase effectiveness and ef?ciency of a critical organizational process: the evaluation of internal controls. Copyright © 2004 John Wiley & Sons, Ltd.     

Assessing Systemic Importance With a Fuzzy Logic Inference System

Three metrics are designed to assess Colombian financial institutions' size, connectedness and non-substitutability as the main drivers of systemic importance: (i) centrality as net borrower in the money market network; (ii) centrality as payments originator in the large-value payment system network; and (iii) asset value of core financial services. An aggregated systemic importance index is calculated based on expert knowledge by using a fuzzy logic inference system. We use principal component analysis to calculate a benchmark index for comparison purposes. Overall similarities between both indexes put forward that expert knowledge aggregation is consistent with that based on a purely quantitative standard approach. Specific non-negligible differences concur with the nonlinear features of an approach whose intention is to replicate human reasoning. Both indexes are complementary and provide a comprehensive relative assessment of each financial institution's systemic importance in the Colombian case, in which the choice of metrics pursues the macroprudential perspective of financial stability. Copyright © 2015 John Wiley & Sons, Ltd.     

Shamrock Shirt Co.'s non-interest bearing note

Student knowledge engineering is a new instructional technique which places students in the role of expert systems developers. This paper examines the feasibility, costs and benefits of this technique within the context of the accounting curriculum. A knowledge engineering project in the area of taxation is presented and evaluated by monitoring students' problem solving behaviour before, during and after developing their expert systems. Evidence suggests that knowledge engineering provides and environment in which students learn to (1) search, (2) read with a problem solving frame of mind, (3) communicate logically, (4) organize and structure accounting knowledge, and (5) logically problem solve, while learning accounting content material. In addition, knowledge engineering gives students a hands-on introduction to expert systems technology. Student knowledge engineering was implemented and evaluated in an Accounting Information Systems course. Student experiences were generally positive and confirmed expectations. Times required for completion of the projects were similar to those of other major course assignments.     

Using Goals,Rules and Methods to Support Reasoning in Business Process Reengineering

One step towards a more systematic approach to the design of business processes is to develop models that provide appropriate representations of the knowledge that is needed for understanding and for reasoning about business processes. We present a modelling framework which uses goals, rules and methods to support the systematic analysis and design of business processes. The frame-work consists of two main components—a Strategic Dependency model that describes a process organization in terms of intentional dependencies among actors, and a Strategic Rationale model that supports reasoning during process redesign. Formal representation of these models allows computer-based tools to be developed as extensions to, and eventually integrated with, other tools for supporting information systems development.