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Data Mining As A Financial Auditing Tool M.Sc. Thesis in Accounting Swedish School of Economics and Business Administration 2002 The Swedish School of Economics and Business Administration Department: Accounting Type of Document: Thesis Title: Data Mining As A Financial Auditing Tool Author: Supatcharee Sirikulvadhana Abstract In recent years, the volume and complexity of accounting transactions in major organizations have increased dramatically. To audit such organizations, auditors frequently must deal with voluminous data with rather complicated data structure. Consequently, auditors no longer can rely only on reporting or summarizing tools in the audit process. Rather, additional tools such as data mining techniques that can automatically extract information from a large amount of data might be very useful. Although adopting data mining techniques in the audit processes is a relatively new field, data mining has been shown to be cost effective in many business applications related to auditing such as fraud detection, forensics accounting and security evaluation. The objective of this thesis is to determine if data mining tools can directly improve audit performance. The selected test area was the sample selection step of the test of control process. The research data was based on accounting transactions provided by AVH PricewaterhouseCoopers Oy. Various samples were extracted from the test data set using data mining software and generalized audit software and the results evaluated. IBM’s DB2 Intelligent Miner for Data Version 6 was selected to represent the data mining software and ACL for Windows Workbook Version 5 was chosen for generalized audit software. Based on the results of the test and the opinions solicited from experienced auditors, the conclusion is that, within the scope of this research, the results of data mining software are more interesting than the results of generalized audit software. However, there is no evidence that the data mining technique brings out material matters or present significant enhancement over the generalized audit software. Further study in a different audit area or with a more complete data set might yield a different conclusion. Search Words: Data Mining, Artificial Intelligent, Auditing, Computerized Audit Assisted Tools, Generalized Audit Software Table of Contents 1. Introduction 1 1.1. Background 1 1.2. Research Objective 2 1.3. Thesis Structure 2 2. Auditing 4 2.1. Objective and Structure 4 2.2. What Is Auditing? 4 2.3. Audit Engagement Processes 5 2.3.1. Client Acceptance or Client Continuance 5 2.3.2. Planning 6 2.3.2.1. Team Mobilization 6 2.3.2.2. Client’s Information Gathering 7 2.3.2.3. Risk Assessment 7 2.3.2.4. Audit Program Preparation 9 2.3.3. Execution and Documentation 10 2.3.4. Completion 11 2.4. Audit Approaches 12 2.4.1. Tests of Controls 12 2.4.2. Substantive Tests 13 2.4.2.1. Analytical Procedures 13 2.4.2.2. Detailed Tests of Transactions 13 2.4.2.3. Detailed Tests of Balances 14 2.5. Summary 14 3. Computer Assisted Auditing Tools 17 3.1. Objective and Structure 17 3.2. Why Computer Assisted Auditing Tools? 17 3.3. Generalized Audit Software 18 3.4. Other Computerized Tools and Techniques 22 3.5. Summary 23 4. Data mining 24 4.1. Objective and Structure 24 4.2. What Is Data Mining? 24 4.3. Data Mining process 25 4.3.1. Business Understanding 26 4.3.2. Data Understanding 27 4.3.3. Data Preparation 27 4.3.4. Modeling 27 4.3.5. Evaluation 28 4.3.6. Deployment 28 4.4. Data Mining Tools and Techniques 29 4.4.1. Database Algorithms 29 4.4.2. Statistical Algorithms 30 4.4.3. Artificial Intelligence 30 4.4.4. Visualization 30 4.5. Methods of Data Mining Algorithms 32 4.5.1. Data Description 32 4.5.2. Dependency Analysis 33 4.5.3. Classification and Prediction 33 4.5.4. Cluster Analysis 34 4.5.5. Outlier Analysis 34 4.5.6. Evolution Analysis 35 4.6. Examples of Data Mining Algorithms 36 4.6.1. Apriori Algorithms 36 4.6.2. Decision Trees 37 4.6.3. Neural Networks 39 4.7. Summary 40 5. Integration of Data Mining and Auditing 43 5.1. Objective and Structure 43 5.2. Why Integrate Data Mining with Auditing? 43 5.3. Comparison between Currently Used Generalized Auditing Software and Data Mining Packages 44 5.3.1. Characteristics of Generalized Audit Software 45 5.3.2. Characteristics of Data Mining Packages 46 5.4. Possible Areas of Integration 48 5.5. Examples of Tests 58 5.6. Summary 66 6. Research Methodology 68 6.1. Objective and Structure 68 6.2. Research Period 68 6.3. Data Available 68 6.4. Research Methods 69 6.5. Software Selection 70 6.5.1. Data Mining Software 70 6.5.2. Generalized Audit Software 71 6.6. Analysis Methods 71 6.7. Summary 72 7. The Research 73 7.1. Objective and Structure 73 7.2. Hypothesis 73 7.3. Research Processes 73 7.3.1. Business Understanding 73 7.3.2. Data Understanding 74 7.3.3. Data Preparation 75 7.3.3.1. Data Transformation 75 7.3.3.2. Attribute Selection 76 7.3.3.3. Choice of Tests 80 7.3.4. Software Deployment 82 7.3.4.1. IBM’s DB2 Intelligent Miner for Data 82 7.3.4.2. ACL 91 7.4. Result Interpretations 94 7.4.1. IBM’s DB2 Intelligent Miner for Data 94 7.4.2. ACL 95 7.5. Summary 99 8. Conclusion 101 8.1. Objective and Structure 101 8.2. Research Perspective 101 8.3. Implications of the Results 102 8.4. Restrictions and Constraints 103 8.4.1. Data Limitation 103 8.4.1.1. Incomplete Data 103 8.4.1.2. Missing Information 103 8.4.1.3. Limited Understanding 104 8.4.2. Limited Knowledge of Software Packages 104 8.4.3. Time Constraint 105 8.5. Suggestions for Further Researches 105 8.6. Summary 105 List of Figures 105 List of Tables 105 References 105 a) Books and Journals 105 b) Web Pages 105 Appendix A: List of Columns of Data Available 109 Appendix B Results of IBM’s Intelligent Miner for Data 105 a) Preliminary Neural Clustering (with Six Attributes) 105 b) Demographic Clustering: First Run 105 c) Demographic Clustering: Second Run 105 d) Neural Clustering: First Run 105 e) Neural Clustering: Second Run 105 f) Neural Clustering: Third Run 105 g) Tree Classification: First Run 105 h) Tree Classification: Second Run 105 i) Tree Classification: Third Run 105 Appendix C: Sample Selection Result of ACL 105 - 1 - 1. Introduction 1.1. Background Auditing is a relatively archaic field and the auditors are frequently viewed as stuffily fussy people. That is no longer true. In recent years, auditors have recognized the dramatic increase in the transaction volume and complexity of their clients’ accounting and non-accounting records. Consequently, computerized tools such as general-purpose and generalized audit software (GAS) have increasingly been used to supplement the traditional manual audit process. The emergence of enterprise resource planning (ERP) system, with the concept of integrating all operating functions together in order to increase the profitability of an organization as a whole, makes accounting system no longer a simple debit-and-credit system. Instead, it is the central registrar of all operating activities. Though it can be argued which is, or which is not, accounting transaction, still, it contains valuable information. It is auditors’ responsibility to audit sufficient amount of transactions recorded in the client’s databases in order to gain enough evidence on which an audit opinion may be based and to ensure that there is no risk left unaddressed. The amount and complexity of the accounting transactions have increased tremendously due to the innovation of electronic commerce, online payment and other high-technology devices. Electronic records have become more common; therefore, online auditing is increasingly challenging let alone manual access. Despite those complicated accounting transactions can now be presented in the more comprehensive format using today’s improved generalized audit software (GAS), they still require auditors to make assumptions, perform analysis and interpret the results. The GAS or other computerized tools currently used only allows auditors to examine a company’s data in certain predefined formats by running varied query commands but not to extract any information from that data especially when such information is unknown and hidden. Auditors need something more than presentation tools to enhance their investigation of fact, or simply, material matters. On the other side, data mining techniques have improved with the advancement of database technology. In the past two decades, database has become commonplace in - 2 - business. However, the database itself does not directly benefit the company; in order to reap the benefit of database, the abundance of data has to be turned into useful information. Thus, Data mining tools that facilitate data extraction and data analysis have received greater attention. There seems to be opportunities for auditing and data mining to converge. Auditing needs a mean to uncover unusual transaction patterns and data mining can fulfill that need. This thesis attempts to explore the opportunities of using data mining as a tool to improve audit performance. The effectiveness of various data mining tools in reaching that goal will also be evaluated. 1.2. Research Objective The research objective of this thesis is to preliminarily evaluate the usefulness of data mining techniques in supporting auditing by applying selected techniques with available data sets. However, it is worth nothing that the data sets available are still in question whether it could be induced as generalization. According to the data available, the focus of this research is sample selection step of the test of control process. The relationship patterns discovered by data mining techniques will be used as a basis of sample selection and the sample selected will be compared with the sample drawn by generalized audit software. 1.3. Thesis Structure The remainder of this thesis is structured as follows: Chapter 2 is a brief introduction to auditing. It introduces some essential auditing terms as a basic background. The audit objectives, audit engagement processes and audit approaches are also described here. Chapter 3 discusses some computer assisted auditing tools and techniques currently used in assisting auditors in their audit work. The main focus will be on the generalized audit software (GAS), particularly in Audit Command Language (ACL) -- the most popular software in recent years. Chapter 4 provides an introduction to data mining. Data mining process, tools and techniques are reviewed. Also, the discussions will attempt to explore the concept, - 3 - methods and appropriate techniques of each type of data mining patterns in greater detail. Additionally, some examples of the most frequently used data mining algorithms will be demonstrated as well. Chapter 5 explores many areas where data mining techniques may be utilized to support the auditors’ performance. It also compares GAS packages and data mining packages from the auditing profession’s perspective. The characteristics of these techniques and their roles as a substitution of manual processes are also briefly discussed. For each of those areas, audit steps, potential mining methods, and required data sets are identified. Chapter 6 describes the selected research methodology, the reasons for selection, and relevant material to be used. The research method and the analysis technique of the results are identified as well. Chapter 7 illustrates the actual study. The hypothesis, relevant facts of the research processes and the study results are presented. Finally, the interpretation of study results will be attempted. Finally, chapter 8 provides a summary of the entire study. The assumptions, restrictions and constraints of the research will be reviewed, followed by suggestions for further research. - 4 - 2. Auditing 2.1. Objective and Structure The objective of this chapter is to introduce the background information on auditing. In section 2.2, definitions of essential terms as well as main objectives and tasks of auditing profession are covered. Four principal audit procedures are discussed in section 2.3. Audit approaches including test of controls and substantive tests are discussed in greater details in section 2.4. Finally, section 2.5 provides a brief summary of auditing perspective. Notice that dominant content covered in this chapter are based on the notable textbook “Auditing: An Integrated Approach” (Arens & Loebbecke, 2000) and my own experiences. 2.2. What Is Auditing? Auditing is the accumulation and evaluation of evidence about information to determine and report on the degree of correspondence between the information and established criteria (Arens & Loebbecke, 2000, 16). Normally, independent auditors, also known as certified public accountants (CPAs), conduct audit work to ascertain whether the overall financial statements of a company are, in all material respects, in conformity with the generally accepted accounting principles (GAAP). Financial statements include Balance Sheets, Profit and Loss Statements, Statements of Cash Flow and Statements of Retained Earning. Generally speaking, what auditors do is to apply relevant audit procedures, in accordance with GAAP, in the examination of the underlying records of a business, in order to provide a basis for issuing a report as an attestation of that company’s financial statements. Such written report is called auditor’s opinion or auditor’s report. Auditor’s report expresses the opinion of an independent expert regarding the degree of reliability upon of the information presented in the financial statements. In other words, auditor’s report assures the financial statements users, which normally are external parities such as shareholders, investors, creditors and financial institutions, of the reliability of financial statements, which are prepared by the management of the company.