Data Mining As A Financial Auditing Tool


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Data Mining As A Financial Auditing Tool

  1. 1. Data Mining As A Financial Auditing Tool M.Sc. Thesis in Accounting Swedish School of Economics and Business Administration 2002
  2. 2. 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
  3. 3. 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 Team Mobilization 6 Client’s Information Gathering 7 Risk Assessment 7 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 Analytical Procedures 13 Detailed Tests of Transactions 13 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. 4. 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. 5. 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 Data Transformation 75 Attribute Selection 76 Choice of Tests 80 7.3.4. Software Deployment 82 IBM’s DB2 Intelligent Miner for Data 82 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
  6. 6. 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 Incomplete Data 103 Missing Information 103 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
  7. 7. -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, on- line 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
  8. 8. -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,
  9. 9. -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.
  10. 10. -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.
  11. 11. -5- Due to the time and cost constraints, auditors cannot examine every detail records behind the financial statements. The concept of materiality and fairly stated financial statements were introduced to solve this problem. Materiality is the magnitude of an omission or misstatement of information that misleads the financial statement users. The materiality standard applied to each account balance is varied and is depended on auditors’ judgement. It is the responsibility of the auditors to ensure that all material misstatements are indicated in the auditors’ opinion. In business practice, it is more common to find an auditor as a staff of an auditing firm. Generally, several CPAs join together to practice as partners of the auditing firm, offering auditing and other related services including auditing and other reviews to interested parties. The partners normally hire professional staffs and form an audit team to assist them in the audit engagement. In this thesis, auditors, auditing firm and audit team are synonyms. 2.3. Audit Engagement Processes The audit engagement processes of each auditing firm may be different. However, they generally involve the four major steps: client acceptance or client continuance, planning, execution and documentation, and completion. 2.3.1. Client Acceptance or Client Continuance Client acceptance, or client continuance in case of a continued engagement, is a process through which the auditing firm decides whether or not the firm should be engaged by this client. Major considerations are: - Assessment of engagement risks: Each client presents different level of risk to the firm. The important risk that an auditing firm must evaluate carefully in accepting an audit client are: accepting a company with a bad reputation or questionable ethics that involves in illegal business activities or material misrepresentation of business and accounting records. Some auditing firms have basic requirements of favorable clients. On the other hand, some have a list of criteria to identify the unfavorable ones. Unfavorable clients, for example, are in dubious businesses or have too complex a financial structure.
  12. 12. -6- - Relationship conflicts: Independence is a key requirement of the audit profession, of equal importance is the auditor’s objectivity and integrity. These factors help to ensure a quality audit and to earn people’s trust in the audit report. - Requirements of the clients: The requirements include, for example, the qualification of the auditor, time constraint, extra reports and estimated budget. - Sufficient competent personnel available - Cost-Benefit Analysis: It is to compare the potential costs of the engagement with the audit fee offered from the client. The major portion of the cost of audit engagement is professional staff charge. If the client is accepted, a written confirmation, generally on an annual basis, of the terms of engagement is established between the client and the firm. 2.3.2. Planning The objective of the planning step is to develop an audit plan. It includes team mobilization, client’s information gathering, risk assessment and audit program preparation. Team Mobilization This step is to form the engagement team and to communicate among team members. First, key team members have to be identified. Team members include engagement partner or partners who will sign the audit report, staff auditors who will conduct most of the necessary audit work and any specialists that are deemed necessary for the engagement. The mobilization meeting, or pre-planning meeting, should be conducted to communicate all engagement matters including client requirements and deliverables, level of involvement, tentative roles and responsibilities of each team member and other relevant substances. The meeting should also cover the determination of the most efficient and effective process of information gathering. In case of client continuance, a review of the prior year audit to assess scope for improving efficiency or effectiveness should be identified.
  13. 13. -7- Client’s Information Gathering In order to perform this step, the most important thing is the cooperation between the client and the audit team. A meeting is arranged to update the client’s needs and expectations as well as management’s perception of their business and the control environment. Next, the audit team members need to perform the preliminary analytical procedures which could involve the following tasks: - Obtaining background information: It includes the understanding of client’s business and industry, the business objectives, legal obligations and related risks. - Understanding system structures: System structures include the system and computer environments, operating procedures and the controls embedded in those procedures. - Control assessment: Based upon information about controls identified from the meeting with the client and the understanding of system structures and processes, all internal controls are updated, assessed and documented. The subjects include control environment, general computerized (or system) controls, monitoring controls and application controls. More details about internal control, such as definitions, nature, purpose and means of achieving effective internal control, can be found in “Internal Control – Integrated Framework” (COSO, 1992). Audit team members’ knowledge, expertise and experiences are considered as the most valuable tools in performing this step. Risk Assessment Risk, in this case, is some level of uncertainty in performing audit work. Risks identified in the first two steps are gathered and assessed. The level of risks assessed in this step is directly lead to the audit strategy to be used. In short, the level of task is based on the level of risks. Therefore, the auditor must be careful not to understate or overstate the level of these risks.
  14. 14. -8- Level of risks is different from one auditing area to another. In planning the extent of audit evidences of each auditing area, auditors primarily use an audit risk model such as the one shown below: Acceptable Audit Risk Planned Detection Risk = Inherent Risk * Control Risk - Planned detection risk: Planned detection risk is the highest level of misstatement risk that the audit evidence cannot detect in each audit area. The auditors need to accumulate audit evidences until the level of misstatement risk is reduced to planned detection risk level. For example, if the planned detection risk is 0.05, then audit testing needs to be expanded until audit evidence obtained supports the assessment that there is only five percent misstatement risk left. - Acceptable audit risk: Audit risk is the probability that auditor will unintentionally render inappropriate opinion on client’s financial statements. Acceptable audit risk, therefore, is a measure of how willing the auditor is to accept that the financial statements may be materially misstated after the audit is completed (Arens & Loebbecke, 2000, 261). - Inherent risk: Inherent risk is the probability that there are material misstatements in financial statements. There are many risk factors that affect inherent risk including errors, fraud, business risk, industry risk, and change risk. The first two are preventable and detectable but others are not. Auditors have to ensure that all risks are taken into account when considering the probability of inherent risk. - Control risk: Control risk is the probability that a client’s control system cannot prevent or detect errors. Normally, after defining inherent risks, controls that are able to detect or prevent such risks are identified. Then, auditors will assess whether the client’s system has such controls and, if it has, how much they can rely on those controls. The more reliable controls, the lower the control risk. In other words, control risk represents auditor’s reliance on client’s control structure. It is the responsibility of the auditors to ensure that no risk factors of each audit area are left unaddressed and the evidence obtained is sufficient to reduce all risks to an acceptable audit risk level. More information about audit risk can be
  15. 15. -9- found in Statement of Auditing Standard (SAS) No. 47: Audit Risk and Materiality in Conducting an Audit (AICPA, 1983). Audit Program Preparation The purpose of this step is to determine the most appropriate audit strategy and tasks for each audit objective within each audit area based on client’s background information about related audit risks and controls identified from the previous steps. Firstly, the audit objectives, both transaction-related and balance- related, of each audit area have to be identified. These two types of objectives share one thing in common -- that they must be met before auditors can conclude that the information presented in the financial statements are fairly stated. The difference is that while transaction-related audit objectives are to ensure the correctness of the total transactions for any given class, balance-related audit objectives are to ensure the correctness of any given account balance. A primary purpose of audit strategy and task is to ensure that those objectives are materially met. Such objectives include the following. Transaction-Related and Balance-Related Audit Objectives - Existence or occurrence: To ensure that all balances in the balance sheet have really existed and the transactions in the income statement have really occurred. - Completeness: To ensure that all balances and transactions are included in the financial statements. - Accuracy: To ensure that the balances and transactions are recorded accurately. - Classification: To ensure that all transactions are classified in the suitable categories. - Cut-off (timing): To ensure that the transactions are recorded in the proper period.
  16. 16. - 10 - Others Balance-Related Audit Objectives - Valuation: To ensure that the balances and transactions are stated at the appropriate value. - Right and obligation: To ensure that the assets are belonged to and the liabilities are the obligation of the company. - Presentation and disclosure: To ensure that the presentation of the financial statements does not mislead the users and the disclosures are enough for users to understand the financial statements clearly. After addressing audit objectives, it is time to develop an overall audit plan. The audit plan should cover audit strategy of each area and all details related to the engagement including the client’s needs and expectations, reporting requirements, timetable. Then, the planning at the detail level has to be performed. This detailed plan is known as a tailored audit program. It should cover tasks identification and schedule, types of tests to be used, materiality thresholds, acceptable audit risk and person responsible. Notice that related risks and controls of each area are taken into account for prescribing audit strategy and tasks. The finalized general plan should be communicated to the client in order to agree upon significant matters such as deliverables and timetable. Both overall audit plan and detailed audit programs need to be clarified to the team as well. 2.3.3. Execution and Documentation In short, this step is to perform the audit examinations by following the audit program. It includes audit tests execution, which will be described in more detail in the next subsection, and documentation. Documentation includes summarizing the results of audit tests, level of satisfaction, matters found during the tests and recommendations. If there is an involvement of specialists, the process performed and the outcome have to be documented as well. Communication practices are considered as the most important skill to perform this step. Not only with the client or the staff working for the client, it is also
  17. 17. - 11 - crucial to communicate among the team. Normally, it is a responsibility of the more senior auditor to coach the less senior ones. Techniques used are briefing, coaching, discussing, and reviewing. A meeting with client in order to discuss the issues found during the execution process and the recommendations of those findings can be arranged either formally or informally. It is a good idea to inform and resolve those issues with the responsible client personnel such as the accounting manager before the completion step and leave only the critical matters to the top management. 2.3.4. Completion This step is similar to the final step of every other kind of projects. The results of aforementioned steps are summarized, recorded, assessed and reported. Normally, the assistant auditors report their work results to the senior, or in-charge, auditors. The auditor-in-charge should perform the final review to ensure that all necessary tasks are performed and that the audit evidence gathered for each audit area is sufficient. Also, the critical matters left from the execution process have to be resolved. The resolution of those matters might be either solved by client’s management (adjusting their financial statements or adequately disclosing them in their financial statement) or by auditors (disclosing them in the auditor’s opinion). The last field work for auditors is review of subsequent events. Subsequent events are events occurred subsequent to the balance sheet date but before the auditor’s report date that require recognition in the financial statements. Based on accumulated audit evidences and audit findings, the auditor’s opinion can be issued. Types of auditor’s opinion are unqualified, unqualified with explanatory paragraph or modified wording, qualified, adverse and disclaimer. After everything is done, it is time to arrange the clearance meeting with the client. Generally, auditors are required to report results and all conditions to the audit committee or senior management. Although not required, auditors often make suggestions to management to improve their business performance through the Management Letter. On the other hand, auditors can get feedback from the client according to their needs and expectations as well.
  18. 18. - 12 - Also, auditors should consider evaluating their own performances in order to improve their efficiency and effectiveness. The evaluation includes summarizing client’s comments, bottom-up evaluation (more senior auditors evaluate the work of assistant auditors) and top-down evaluation (get feedback from field work auditors). 2.4. Audit Approaches In order to determine whether financial statements are fairly stated, auditors have to perform audit tests to obtain competent evidence. The audit approaches used in each audit area as well as the level of test depended on auditors’ professional judgement. Generally, audit approaches fall into one of these two categories: 2.4.1. Tests of Controls There are as many control objectives as many textbooks about system security nowadays. However, generally, control objectives can be categorized into four broad categories -- validity, completeness, accuracy and restricted access. With these objectives in mind, auditors can distinguish control activities from the normal operating ones. When assessing controls during planning phase, auditors are able to identify the level of control reliance -- the level of controls that help reducing risks. The effectiveness of such controls during the period can be assessed by performing testing of controls. However, only key controls will be tested and the level of tests depends solely on the control reliance level. The higher control reliance is, the more tests are performed. The scope of tests should be sufficiently thorough to allow the auditor to draw a conclusion as to whether controls have operated effectively in a consistent manner and by the proper authorized person. In other words, the level of test should be adequate enough to bring assurance of the relevant control objectives. The assurance evidence can be obtained from observation, inquiry, inspection of supporting documents, re-performance or the combination of these.
  19. 19. - 13 - 2.4.2. Substantive Tests Substantive test is an approach designed to test for monetary misstatements or irregularities directly affecting the correctness of the financial statement balances. Normally, the level of tests depends on the level of assurance from the tests of controls. When the tests of controls could not be performed either because there is no or low control reliance or because the amount and extensiveness of the evidence obtained is not sufficient, substantive tests are performed. Substantive tests include analytical procedures, detailed tests of transactions as well as detailed tests of balances. Details of each test are as follows: Analytical Procedures The objective of this approach is to ensure that overall audit results, account balances or other data presented in the financial statements are stated reasonably. Statement of Auditing Standard (SAS) No. 56 also requires auditors to use analytical procedures during planning and final reporting phases of audit engagement (AICPA, 1988). Analytical procedures can be performed in many different ways. Generally, the most accepted one is to develop the expectation of each account balance and the acceptable variation or threshold. Then, this threshold is compared with the actual figure. Further investigation is required only when the difference between actual and expectation balances falls out of the acceptable variation range prescribed. Further investigation includes extending analytical procedures, detail examination of supporting documents, conducting additional inquiries and performing other substantive tests. Notice that the reliabilities of data, the predictive method and the size of the balance or transactions can strongly affect the reliability of assurance. Moreover, this type of test requires significant professional judgement and experience. Detailed Tests of Transactions The purpose of detailed tests of transactions (also known as substantive testing of transactions) is to ensure that the transaction-related audit objectives are met in each accounting transaction. The confidence on transactions will
  20. 20. - 14 - lead to the confidence on the account total in the general ledger. Testing techniques include examination of relevant documents and re-performance. The extent of tests remains a matter of professional judgement. It can be varied from a sufficient amount of samples to all transactions depending on the level of assurance that auditors want to obtain. Generally, samples are drawn either from the items with particular characteristics or randomly sampled or a combination of both. Examples of the particular characteristics are size (materiality consideration) and unusualness (risk consideration). This approach is time-consuming. Therefore, it is a good idea to reduce the sampling size by considering whether analytical procedures or tests of controls can be performed to obtain assurance in relation to the items not tested. Detailed Tests of Balances Detailed tests of balances (also called substantive tests of balances) focuses on the ending balances of each general ledger account. They are performed after the balance sheet date to gather sufficient competent evidence as a reasonable basis for expressing an opinion on fair presentation of financial statements (Rezaee, Elam & Sharbatoghlie, 2001, 155). The extent of tests depends on the results of tests of control, analytical procedures and detailed tests of transactions relating to each account. Like detailed tests of transactions, the sample size can be varied and remains a matter of professional judgement. Techniques to be applied for this kind of tests include account reconciliation, third party confirmation, observation of the items comprising an account balance and agreement of account details to supporting documents. 2.5. Summary 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. As seen in figure 2.1, the main audit engagement processes are client acceptance, planning, execution and completion.
  21. 21. - 15 - Acceptance Gather Information Client Evaluate client Mobilize Gather information in details Planning Perform preliminary analytical procedures Tests of Controls Assess risk and control - Identify controls Set materiality - Assess control reliance - Select samples Develop audit plan and detailed audit program - Test controls - Further investigate for unusual items Execution & Documentation Perform Tests of Controls - Evaluate Results trol Con ance Low Analytical Review i High Rel - Develop expectations Perform Substantive Tests - Compare expectations - Detailed Tests of Transactions with actual figures - Analytical Procedures - Further investigate for - Detailed Tests of Balances major differences - Evaluate Results Document testing results Detailed Tests - Select samples Gather audit evidence and audit findings - Test samples Review subsequent events - Further investigate for Completion unusual items Evaluate overall results - Evaluate results Issue auditor’s report Arrange clearance meeting with client Evaluate team performance Figure 2.1: Summary of audit engagement processes Planning includes mobilization, information gathering, risk assessment and audit program preparation. Two basic types of audit approaches the auditors can use during execution phase are tests of controls and substantive tests. Substantive tests include analytical procedures, detailed tests of transactions and detailed tests of
  22. 22. - 16 - balances. The extent of test is based on the professional judgement of auditors. However, materiality, control reliance and risks are also major concerns. The final output of audit work is auditor’s report. The type of audit report -- unqualified, unqualified with explanatory paragraph or modified wording, qualified, adverse or disclaimer -- depends on the combination of evidences obtained from the field works and the audit findings. At the end of each working period, the accumulated evidence and performance evaluation should be reviewed to assess scope for improving efficiency or effectiveness for the next auditing period. It is accepted that auditing business is not a profitable area of auditing firms. Instead, the value-added services, also known as assurance services, such as consulting and legal service are more profitable. The reason is that while cost of all services are relatively the same, clients are willing to pay a limited amount for auditing service comparing to other services. However, auditing has to be trustworthy and standardized and all above-mentioned auditing tasks are, more or less, time-consuming and require professional staff involvement. Thus, the main cost of auditing engagement is the salary of professional staffs and it is considerably high. This cost pressure is a major problem the auditing profession is facing nowadays. To improve profitability of auditing business, the efficient utilization of professional staff seems to be the only practical method. The question is how. Some computerized tools and techniques are introduced into auditing profession in order to assist and enhance auditing tasks. However, the level of automation is still questionable. As long as they still require professional staff involvement, auditing cost is unavoidable high.
  23. 23. - 17 - 3. Current Auditing Computerized Tools 3.1. Objective and Structure The objective of this chapter is to provide information about technological tools and techniques currently used by auditors. Section 3.2 discusses why computer assisted auditing tools (CAATs) are more than requisite in auditing profession at present. In section 3.3, general audit software (GAS) is reviewed in detail. The topic focuses on the most popular software, Audit Command Language (ACL). Other computerized tools and techniques are briefly identified in section 3.4. Finally, a brief summary of some currently used CAATs is provided in section 3.5. Before proceeding, it is worth noting that this chapter was mainly based on two textbooks and one journal, which are “Accounting Information Systems” (Bonar & Hopwood, 2001), “Core Concept of Accounting Information System” (Moscove, Simkin & Bagranoff, 2000) and “Audit Tools” (Needleman, 2001). 3.2. Why Computer Assisted Auditing Tools? It is accepted that advances in technology have affected the audit process. With the ever increasing system complexity, especially the computer-based accounting information systems, including enterprise resource planning (ERP), and the vast amount of transactions, it is impractical for auditors to conduct the overall audit manually. It is even more impossible in an e-commerce intensive environment because all accounting data auditors need to access are computerized. In the past ten years, auditors frequently outsource technical assistance in some auditing areas from information system (IS) auditor, also called electronic data processing (EDP) auditor. However, when the computer-based accounting information systems become commonplace, such technical skill is even more important. The rate of growth of the information system practices within the big audit firms (known as “the Big Five”) was estimated at between 40 to 100 percent during 1990 and 2005 (Bagranoff & Vendrzyk, 2000, 35). Nowadays, the term “auditing with the computer” is extensively used. It describes the employment of the technologies by auditors to perform some audit work
  24. 24. - 18 - that otherwise would be done manually or outsource. Such technologies are extensively referred to as computer assisted auditing tools (CAATs) and they are now play an important role in audit work. In auditing with the computer, auditors employ CAATs with other auditing techniques to perform their work. As its name suggests, CAAT is a tool to assist auditors in performing their work faster, better, and at lower cost. As CAATs become more common, this technical skill is as important to auditing profession as auditing knowledge, experience and professional judgement. There are a variety of software available to assist the auditors. Some are general-purpose software and some are specially designed that are customized to be used to support the entire audit engagement processes. Many auditors consider simple general ledger, automated working paper software or even spreadsheet as audit software. In this thesis, however, the term audit software refers to software that allows the auditors to perform overall auditing process that generally known as the generalized audit software. 3.3. Generalized Audit Software Generalized audit software (GAS) is an automated package originally developed in-house by professional auditing firms. It facilitates auditor in performing necessary tasks during most audit procedures but mostly in the execution and documentation phase. Basic features of a GAS are data manipulation (including importing, querying and sorting), mathematical computation, cross-footing, stratifying, summarizing and file merging. It also involves extracting data according to specification, statistical sampling for detailed tests, generating confirmations, identifying exceptions and unusual transactions and generating reports. In short, they provide auditors the ability to access, manipulate, manage, analyze and report data in a variety of formats. Some packages also provide the more special features such as risk assessment, high-risk transaction and unusual items continuous monitoring, fraud detection, key performance indicators tracking and standardized audit program generation. With the standardized audit program, these packages help the users to adopt some of the profession's best practices.
  25. 25. - 19 - Most auditing firms, nowadays, have either developed their own GASs or purchased some commercially available ones. Among a number of the commercial packages, the most popular one is the Audit Command Language (ACL). ACL is widely accepted as the leading software for data-access, analysis and reporting. Some in-house GAS systems of those large auditing firms even allow their systems to interface with ACL for data extraction and analysis. Figure 3.1: ACL software screenshot (version 5.0 Workbook) ACL software (figure 3.1) is developed by ACL Services Ltd. ( It allows auditors to connect personal laptops to the client’s system and then download client’s data into their laptops for further processing. It is capable of working on large data set that makes testing at hundred-percent coverage possible. Moreover, it provides a comprehensive audit trail by allowing auditors to view their files, steps and results at any time. The popularity of the ACL is resulted from its convenience, its flexibility and its reliability. Table 3.1 illustrates the features of ACL and how are they used in each step of audit process.
  26. 26. - 20 - Audit Processes ACL Features Planning - Risk assessment - “Statistics” menu - “Evaluation” menu Execution and Documentation Tests of Controls - Sample selection - “Sampling” menu with the ability to specify sampling size and selection criteria - “Filter” menu - Controls Testing - “Analyze” menu including Count, Total, Statistics, Age, Duplicate, Verify and Search - Expression builder - Results evaluation - Evaluation menu Analytical Review - Expectations development - “Statistics” menu - Expected versus actual figures - “Merge” command comparison - “Analyze” menu including Statistics, Age, Verify and Search - Expression builder - Results evaluation - Evaluation menu Table 3.1: ACL features used in assisting each step of audit processes
  27. 27. - 21 - Audit Processes ACL Features Detailed Tests - Sample selection - “Sampling” menu with the ability to specify sampling size and selection criteria - “Filter” menu - Sample testing - “Analyze” menu including Count, Total, Statistics, Age, Duplicate, Verify and Search - Expression builder - Results evaluation - Evaluation menu Documentation - Document note - Automatic command log - File history Completion - Lesson learned record - “Document Notes” menu - “Reports” menu Other Possibilities - Fraud detection - “Analyze” menu including Count, Total, Statistics, Age, Duplicate, Verify and Search - Expression builder - “Filter” menu Table 3.1: ACL features used in assisting each step of audit processes (Continued) With ACL’s capacity and speed, auditors can shorten the audit cycle with more thorough investigation. There are three beneficial features that make ACL a promising tool for auditors. First, the interactive capability allows auditors to test, investigate, analyze and get the results at the appropriate time. Second, the audit trail capability
  28. 28. - 22 - records history of the files, commands used by auditors and the results of such commands. This includes command log files that are, in a way, considered as record of work done. Finally, the reporting capability produces various kinds of report including both predefined and customized ones. However, there are some shortcomings. The most critical one is that, like other GAS, it is not able to deal with files that have complex data structure. Although ACL’s Open Data Base Connectivity (ODBC) interface is introduced to reduce this problem, some intricate files still require flattening. Thus, it presents control and security problems. 3.4. Other Computerized Tools and Techniques As mentioned above, there are many other computerized tools other than audit software that are capable of assisting some part of the audit processes. Those tools include the following: - Planning tools: project management software, personal information manager, and audit best practice database, etc. - Analysis tools: database management software, and artificial intelligence. - Calculation tools: spreadsheet software, database management software, and automated working paper software, etc. - Sample selection tools: spreadsheet software. - Data manipulation tools: database management software. - Documents preparation tools: word processing software and automated working paper software. In stead of using these tools as a substitution of GAS, auditors can incorporate some of these tools with GAS to improve the efficiency of the audit process. Planning tools is a good example. Together with the computerized tools, computerized auditing technique that used to be performed by the EDP auditors has now become part of an auditor’s repertoire. At least, financial auditors are required to understand what technique to use,
  29. 29. - 23 - how to apply those techniques, and how to interpret the result to support their audit findings. Such techniques should be employed appropriately to accomplish the audit objectives. Some examples are as follows: - Test data: test how the system detect invalid data, - Integrated test facility: observe how fictitious transactions are processed, - Parallel simulation: simulate the original transactions and compare the results, - System testing: test controls of the client’s accounting system, and - Continuous auditing: embed audit program into client’s system. 3.5. Summary In these days, technology impacts the ways auditors perform their work. To conduct the audit, auditors can no longer rely solely on their traditional auditing techniques. Instead, they have to combine such knowledge and experience with technical skills. In short, the boundary between the financial auditor and the information system auditor has becomes blurred. Therefore, it is important for the auditors to keep pace with the technological development so that they can decide what tools and techniques to be used and how to use them effectively. Computer assisted auditing tools (CAATs) are used to compliment the manual audit procedures. There are many CAATs available in the market. The challenge to the auditors is to choose the most appropriate ones for their work. Both the generalized audit software (GAS), that integrates overall audit functions, and other similar software are available to support their work. However, GAS packages tend to be more widely used due to its low cost, high capabilities and high reliability.
  30. 30. - 24 - 4. Data mining 4.1. Objective and Structure The objective of this chapter is to describe the basic concept of data mining. Section 4.2 provides some background on data mining and explains its basic element. Section 4.3 describes data mining processes in greater detail. Data mining tools and techniques are discussed in section 4.4 and methods of data mining algorithms are discussed in section 4.5. Examples of most frequently used data mining algorithms are provided in section 4.6. Finally, the brief summary of data mining is reviewed in section 4.7. Notice that the major contents in this chapter are based on “CRISP-DM 1.0 Step-by-Step Data Mining Guide” (CRISP-DM, 2000), “Data Mining: Concepts and Techniques” (Han & Kamber, 2000) and “Principles of Data Mining” (Hand, Heikki & Smyth 2001). 4.2. What Is Data Mining? Data mining is a set of computer-assisted techniques designed to automatically mine large volumes of integrated data for new, hidden or unexpected information, or patterns. Data mining is sometimes known as knowledge discovery in databases (KDD). In recent years, database technology has advanced in stride. Vast amounts of data have been stored in the databases and business people have realized the wealth of information hidden in those data sets. Data mining then become the focus of attention as it promises to turn those raw data into valuable information that businesses can use to increase their profitability. Data mining can be used in different kinds of databases (e.g. relational database, transactional database, object-oriented database and data warehouse) or other kinds of information repositories (e.g. spatial database, time-series database, text or multimedia database, legacy database and the World Wide Web) (Han, 2000, 33). Therefore, data to be mined can be numerical data, textual data or even graphics and audio.
  31. 31. - 25 - The capability to deal with voluminous data sets does not mean data mining requires huge amount of data as input. In fact, the quality of data to be mined is more important. Aside from being a good representative of the whole population, the data sets should contain the least amount of noise -- errors that might affect mining results. There are many data mining goals have been recognized; these goals may be grouped into two categories -- verification and discovery. Both of the goals share one thing in common -- the final products of mining process are discovered patterns that may be used to predict the future trends. In the verification category, data mining is being used to confirm or disapprove identified hypotheses or to explain events or conditions observed. However, the limitation is that such hypotheses, events or conditions are restricted by the knowledge and understanding of the analyst. This category is also called top-down approach. Another category, the discovery, is also known as bottom-up approach. This approach is simply the automated exploration of hitherto unknown patterns. Since data mining is not limited by the inadequacy of the human brain and it does not require a stated objective, inordinate patterns might be recognized. However, analysts are still required to interpret the mining results to determine if they are interesting. In recent years, data mining has been studied extensively especially on supporting customer relationship management (CRM) and fraud detection. Moreover, many areas have begun to realize the usefulness of data mining. Those areas include biomedicine, DNA analysis, financial industry and e-commerce. However, there are also some criticisms on data mining shortcomings such as its complexity, the required technical expertise, the lower degree of automation, its lack of user friendliness, the lack of flexibility and presentation limitations. Data mining software developers are now trying to mitigate those criticisms by deploying an interactive developing approach. It is expected that with the advancement in this new approach, data mining will continue to improve and attract more attention from other application areas as well. 4.3. Data Mining Process According to CRISP-DM, a consortium that attempted to standardize data mining process, data mining methodology is described in terms of a hierarchical process that includes four levels as shown in Figure 4.1. The first level is data mining phases,
  32. 32. - 26 - or processes of how to deploy data mining to solve business problems. Each phase consists of several generic tasks or, in other words, all possible data mining situations. The next level contains specialized tasks or actions to be taken in order to carry out in certain situations. To make it unambiguous, the generic tasks of the second phase have to be enumerated in greater details. The questions of how, when, where and by whom have to be answered in order to develop a detailed execution plan. Finally, the fourth level, process instances, is a record of the actions, decisions and results of an Processes / Phases Generic Tasks Special Tasks Process Instances actual data mining engagement or, in short, the final output of each phase. Figure 4.1: Four level breakdown of the CRISP-DM data mining methodology (CRISP-DM, 2000, 9) The top level, data mining process, consists of six phases which are business understanding, data understanding, data preparation, modeling, evaluation and deployment. Details of each phase are better described as follows. 4.3.1. Business Understanding The first step is to map business issues to data mining problems. Generic tasks of this step include business objective determination, situation assessment, data mining feasibility evaluation and project plan preparation. At the end of the phase, project plan will be produced as a guideline to the whole project. Such plan should include business background, business objectives and deliverables, data mining goals and requirements, resources and capabilities availability and demand, assumptions and constraints identification as well as risks and contingencies assessment.
  33. 33. - 27 - This project plan should be dynamic. This means that at the end of each phase or at each prescribed review point, the plan should be reviewed and updated in order to keep up with the situation of the project. 4.3.2. Data Understanding The objective of this phase is to gain insight into the data set to be mined. It includes capturing and understanding the data. The nature of data should be reviewed in order to identify appropriate techniques to be used and the expected patterns. Generic tasks of this phase include data organization, data collection, data description, data analysis, data exploration and data quality verification. At the end of the phase, the results of all above-mentioned tasks have to be reported. 4.3.3. Data Preparation As mentioned above, one of the major concerns in using data mining technique is the quality of data. The objective of this phase is to ensure that data sets are ready to be mined. The process includes data selection (deciding on which data is relevant), data cleaning (removing all, or most, incompleteness, noises and inconsistency), data scrubbing (cleaning data by abrasive action), data integration (combining data from multiple sources into standardized format), data transformation (converting standardized data into ready-to-be-mined and standardized format) and data reduction (removing redundancies and merging data into aggregated format). The end product of this phase includes the prepared data sets and the reports describing the whole processes. The characteristics of data sets could be different from the prescribed ones. Therefore, the review of project plan has to be performed. 4.3.4. Modeling Though, the terms “models” and “patterns” are used interchangeably, there are some differences between them. A model is a global summary of data sets that can describe the population from which the data were drawn while a pattern describes a structure relating to relatively small local part of the data (Hand, Heikki & Smyth, 2001, 165). To make it simplistic, a model can be viewed as a set of patterns.
  34. 34. - 28 - In this phase, a set of data mining techniques is applied to the preprocessed data set. The objective is to build a model that most satisfactorily describes the global data set. Steps include data mining technique selection, model design, model construction, model testing, model validation and model assessment. Notice that, typically, several techniques can be used in parallel to the same data mining problem. The model can be focused on either the most promising technique or using many techniques simultaneously. However, the latter technique requires cross-validated capabilities and evaluation criteria. 4.3.5. Evaluation After applying data mining techniques in a model with data sets, the result of the model will be interpreted. However, it does not mean data mining engagement is over once the results are obtained. Such results have to be evaluated in conjunction with business objectives and context. If the results are satisfactory, the engagement can move on to the next phase. Otherwise, another iteration or moving back to the previous phase has to be done. The expertise of analysts is required in this phase. Besides the result of the model, some evaluation criteria should be taken into account. Such criteria include benefits the business would get from the model, accuracy and speed of the model, the actual costs, degree of automation, and scalability. Generic tasks of this phase include evaluating mining result, reviewing processes and determining the next steps. At the end of the phase, the satisfactory model is approved and the list of further actions is identified. 4.3.6. Deployment Data mining results are deployed into business process in this phase. This phase begins with deployment plan preparation. Besides, the plan for monitoring and maintenance has to be developed. Finally, the success of data mining engagement should be evaluated including area to be improved and explored. Another important thing is that the possibility of failure has to be accepted. No matter how well the model is designed and tested, it is just a model that
  35. 35. - 29 - was built from a set of sample data sets. Therefore, the ability to adapt to business change and prompt management decision to correct it are required. Moreover, the performance of the model needs to be evaluated on a regular basis. The sequence of those phases is not rigid so moving back and forth between phases is allowed. Besides, the relationship could exist between any phases. At each review point, the next step has to be specified -- a step that can be either forward or backward. The lesson learned during and at the end of each phase should be documented as a guideline for the next phase. Besides, the documentation of all phases as well as the result of deployment should be documented for the next engagement. Details should include results of each phase, matters arising, problem solving options and method selected. Besides CRISP-DM guideline, there are other textbooks dedicating for integrating data mining into business problems. For the sake of simplicity, I would not go into too much detail than mentioned above. However, more information may be found in “Building Data Mining Applications for CRM” (Berson, Smith & Kurt, 2000) and “Data Mining Cookbook” (Rud, 2001). 4.4. Data Mining Tools and Techniques Data mining is developed from many fields including database technology, artificial intelligence, traditional statistics, high-performance computing, computer graphics and data visualization. Hence, there are abundance of data mining tools and techniques available. However, those tools and techniques can be classified into four broad categories, which are database algorithms, statistical algorithms, artificial intelligence and visualization. Details of each category are as follows: 4.4.1. Database algorithms Although data mining does not require large volume of data as input, it is more practical to deploy data mining techniques on large data sets. Data mining is most useful with the information that human brains could not capture. Therefore, it can be said that the objective of data mining is to mine databases for useful information.
  36. 36. - 30 - Thus, many database algorithms can be employed in order to assist mining processes especially in the data understanding and preparation phase. The examples of those algorithms are data generalization, data normalization, missing data detection and correction, data aggregation, data transformation, attribute-oriented induction, and fractal and online analytical processing (OLAP). 4.4.2. Statistical algorithms The distinction between statistics and data mining is indistinct as almost all data mining techniques are derived from statistics field. It means statistics can be used in almost all data mining processes including data selection, problem solving, result presentation and result evaluation. Statistical techniques that can be deployed in data mining processes include mean, median, variance, standard deviation, probability, confident interval, correlation coefficient, non-linear regression, chi-square, Bayesian theorem and Fourier transforms. 4.4.3. Artificial Intelligence Artificial intelligence (AI) is the scientific field seeking for the way to locate intelligent behavior in a machine. It can be said that artificial intelligence techniques are the most widely used in mining process. Some statisticians even think of data mining tool as an artificial statistical intelligence. Capability of learning is the greatest benefit of artificial intelligence that is most appreciated in the data mining field. Artificial intelligence techniques used in data mining processes include neural network, pattern recognition, rule discovery, machine learning, case-based reasoning, intelligent agents, decision tree induction, fuzzy logic, genetic algorithm, brute force algorithm and expert system. 4.4.4. Visualization Visualization techniques are commonly used to visualize multidimensional data sets in various formats for analysis purpose. It can be viewed as higher presentation techniques that allow users to explore complex multi-dimensional data in a simpler way. Generally, it requires the integration of human effort to analyze and assess the results from its interactive displays. Techniques include audio, tabular,
  37. 37. - 31 - scatter-plot matrices, clustered and stacked chart, 3-D charts, hierarchical projection, graph-based techniques and dynamic presentation. To separate data mining from data warehouse, online analytical processing (OLAP) or statistics is intricate. One thing to be sure of is that data mining is not any of them. The difference between data warehouse and data mining is quite clear. Though there are some textbooks about data warehouse that devoted a few pages to data mining topic, it does not mean that they took data mining as a part of data warehousing. Instead, they all agreed that while data warehouse is a place to store data, data mining is a tool to distil the value of such data. The examples of those textbooks are “Data Management” (McFadden, Hoffer & Prescott, 1999) and “Database Systems : A Practical Approach to Design, Implementation, and Management” (Connolly, Begg & Strachan, 1999). One might argue that the value of data could be realized by using OLAP as claimed in many data warehouse textbooks. OLAP, however, can be thought of as another presentation tool that reform and recompile the same set of data in order to help users find such value easier. It requires human interference in both stating presenting requirements as well as interpreting the results. On the other hand, data mining uses automated techniques to do those jobs. As mentioned above, the differentiation between data mining and statistics is much more complicated. It is accepted that the algorithms underlying data mining tools and techniques are, more or less, derived from statistics. In general, however, statistical tools are not designed for dealing with enormous amount of data but data mining tools are. Moreover, the target users of statistical tools are statisticians while data mining is designed for business people. This simply means that data mining tools are enhancement of statistical tools that blend many statistical algorithms together and possess a capability of handling more data in an automated manner as well as a user- friendly interface. The choice of an appropriate technique and timing depend on the nature of the data to be analyzed, the size of data sets and the type of methods to be mined. A range of techniques can be applied to the problems either alone or in combination. However, when deploying sophisticated blend of data mining techniques, there are at least two
  38. 38. - 32 - requirements that need to be met -- the ability to cross validate results and the measurement criteria. 4.5. Methods of Data Mining Algorithms Though nowadays data mining software packages are claimed to be more automated, they still require some directions from users. Expected method of data mining algorithm is one of those requirements. Therefore, in employing data mining tools, users should have a basic knowledge of these methods. The types of data mining methods can be categorized differently. However, in general, they fall into six broad categories which are data description, dependency analysis, classification and prediction, cluster analysis, classification and prediction, cluster analysis, outlier analysis and evolution analysis. Details of each method are as follows: 4.5.1. Data Description The objective of data description is to provide an overall description of data, either in itself or in each class or concept, typically in summarized, concise and precise form. There are two main approaches in obtaining data description -- data characterization and data discrimination. Data characterization is summarizing general characteristics of data and data discrimination, also called data comparison, is comparing characters of data between contrasting groups or classes. Normally, these two approaches are used in aggregated manner. Though data description is one among many types of data mining algorithm methods, usually it is not the real finding target. Often the data description is analyst’s first requirement, as it helps to gain insight into the nature of the data and to find potential hypotheses, or the last one, in order to present data mining results. The example of using data description as a presentation tool is the description of the characteristics of each cluster that could not be identified by neural network algorithm. Appropriate data mining techniques for this method are attribute-oriented induction, data generalization and aggregation, relevance analysis, distance analysis, rule induction and conceptual clustering.
  39. 39. - 33 - 4.5.2. Dependency Analysis The purpose of dependency analysis, also called association analysis, is to search for the most significant relationship across large number of variables or attributes. Sometimes, association is viewed as one type of dependencies where affinities of data items are described (e.g., describing data items or events that frequently occur together or in sequence). This type of methods is very common in marketing research field. The most prevalent one is market-basket analysis. It analyzes what products customers always buy together and presents in “[Support, Confident]” association rules. The support measurement states the percentage of events occurring together comparing to the whole population. The confident measurement affirms the percentage of the occurrence of the following events comparing to the leading one. For example, the association rule in figure 4.2 means milk and bread were bought together at 6% of all transactions under analysis and 75% of customers who bought milk also bought bread. Milk => bread [support = 6%, confident = 75%] Figure 4.2: Example of association rule Some techniques for dependency analysis are nonlinear regression, rule induction, statistic sampling, data normalization, Apriori algorithm, Bayesian networks and data visualization. 4.5.3. Classification and Prediction Classification is the process of finding models, also known as classifiers, or functions that map records into one of several discrete prescribed classes. It is mostly used for predictive purpose. Typically, the model construction begins with two types of data sets -- training and testing. The training data sets, with prescribed class labels, are fed into the model so that the model is able to find parameters or characters that distinguish one class from the other. This step is called learning process. Then, the testing data sets, without pre-classified labels, are fed into the model. The model will, ideally, automatically assign the precise class labels for those testing items. If the results of
  40. 40. - 34 - testing are unsatisfactory, then more training iterations are required. On the other hand, if the results are satisfactory, the model can be used to predict the classes of target items whose class labels are unknown. This method is most effective when the underlying reasons of labeling are subtle. The advantage of this method is that the pre-classified labels can be used as the performance measurement of the model. It gives the confidence to the model developer of how well the model performs. Appropriate techniques include neural network, relevance analysis, discriminant analysis, rule induction, decision tree, case-based reasoning, genetic algorithms, linear and non-linear regression, and Bayesian classification. 4.5.4. Cluster analysis Cluster analysis addresses segmentation problems. The objective of this analysis is to separate data with similar characteristics from the dissimilar ones. The difference between clustering and classification is that while clustering does not require pre-identified class labels, classification does. That is why classification is also called supervised learning while clustering is called unsupervised learning. As mentioned above, sometimes it is more convenient to analyze data in the aggregated form and allow breaking down into details if needed. For data management purpose, cluster analysis is frequently the first required task of the mining process. Then, the most interesting cluster can be focused for further investigation. Besides, description techniques may be integrated in order to identify the character providing best clustering. Examples of appropriate techniques for cluster analysis are neural networks, data partitioning, discriminant analysis and data visualization. 4.5.5. Outlier Analysis Some data items that are distinctly dissimilar to others, or outliers, can be viewed as noises or errors which ordinarily need to be drained before inputting data sets into data mining model. However, such noises can be useful in some cases, where unusual items or exceptions are major concerns. Examples are fraud detection, unusual usage patterns and remarkable response patterns.
  41. 41. - 35 - The challenge is to distinguish the outliers from the errors. When performing data understanding phase, data cleaning and scrubbing is required. This step includes finding erroneous data and trying to fix them. Thus, the possibility to detect interesting differentiation might be diminished. On the other hand, if the incorrect data remained in the data sets, the accuracy of the model would be compromised. Appropriate techniques for outlier analysis include data cube, discriminant analysis, rule induction, deviation analysis and non-linear regression. 4.5.6. Evolution Analysis This method is the newest one. The creation of evolution analysis is to support the promising capability of data warehouses which is data or event collection over a period of time. Now that business people came to realize the value of trend capture that can be applied to the time-related data in the data warehouse, it attracts increasing attention in this method. Objective of evolution analysis is to determine the most significant changes in data sets over time. In other words, it is other types of algorithm methods (i.e., data description, dependency analysis, classification or clustering) plus time- related and sequence-related characteristics. Therefore, tools or techniques available for this type of methods include all possible tools and techniques of other types as well as time-related and sequential data analysis tools. The examples of evolution analysis are sequential pattern discovery and time-dependent analysis. Sequential pattern discovery detects patterns between events such that the presence of one set of items is followed by another (Connolly, 1999, 965). Time-dependent analysis determines the relationship between events that correlate in a definite of time. Different types of methods can be mined in parallel to discover hidden or unexpected patterns, but not all patterns found are interesting. A pattern is interesting if it is easily understood, valid, potentially useful and novel (Han & Kamber, 2000, 27). Therefore, analysts are still needed in order to evaluate whether the mining results are interesting.
  42. 42. - 36 - To distinguish interesting patterns, users of data mining tools have to solve at least three problems. First, the correctness of patterns has to be measured. For example, the measurement of dependency analysis is “[Confident, Support]” value. It is easier for the methods that have historical or training data sets to compare the correctness of the patterns with the real ones; i.e., classification and prediction method. For those methods that training data sets are not available, then the professional judgement of the users of data mining tools is required. Second, the optimization model of patterns found has to be created. For example, the significance of “Confident” versus “Support” has to be formulated. To put it in simpler terms, it is how to tell which is better between higher “Confident” with lower “Support” or lower “Confident” with higher “Support”. Finally, the right point to stop finding patterns has to be specified. This is probably the most challenging problem. This leads to two other problems -- how to tell the current optimized pattern is the most satisfactory one and how to know it can be used as a generalized pattern on other data sets. In short, while trying to optimize the patterns, the over-fitting problem has to be taken into account as well. 4.6. Examples of Data Mining Algorithms As mentioned above, there are plenty of algorithms used to mine the data. Due to the limited of space, this section is focused on the most frequently used and widespread recognized algorithms that can be indisputable thought of as data mining algorithms; neither pure statistical, nor database algorithms. The examples include Apriori algorithms, decision trees and neural networks. Details of each algorithms are as follows: 4.6.1. Apriori Algorithms Apriori algorithm is the most frequently used in the dependency analysis method. It attempts to discover frequent item sets using candidate generation for Boolean association rules. Boolean association rule is a rule that concerns associations between the presence or absence of items (Han & Kamber, 2000, 229). The steps of Apriori algorithms are as follows: (a) The analysis data is first partitioned according to the item sets.
  43. 43. - 37 - (b) The support count of each item set (1-itemsets), also called Candidate, is performed. (c) The item sets that could not satisfy the required minimum support count are pruned. Thus creating the frequent 1-itemsets (a list of item sets that have at least minimum support count). (d) Item sets are joined together (2-itemsets) to create the second-level candidates. (e) The support count of each candidate is accumulated. (f) After pruning unsatisfactory item sets according to minimum support count, the frequent 2-itemsets is created. (g) The iteration of (d), (e) and (f) are executed until no more frequent k- itemsets can be found or, in other words, the next frequent k-itemsets contains empty frequent. (h) At the terminated level, the Candidate with maximum support count wins. By using Apriori algorithms, the group of item sets that most frequently come together is identified. However, dealing with large amount of transactions means the candidate generation, counting and pruning steps needed to be repeated numerous times. Thus, to make the process more efficient, some techniques such as hashing (reducing the candidate size) and transaction reduction can be used (Han & Kamber, 2000, 237). 4.6.2. Decision Trees Decision tree is a predictive model with tree or hierarchical structure. It is used most in classification and prediction methods. It consists of nodes, which contained classification questions, and branches, or the results of the questions. At the lowest level of the tree -- leave nodes -- the label of each classification is identified. The structure of decision tree is illustrated in figure 4.3. Typically, like other classification and prediction techniques, the decision tree begins with exploratory phase. It requires training data sets with labels to be fed.
  44. 44. - 38 - The underlying algorithm will try to find the best-fit criteria to distinguish one class from another. This is also called tree growing. The major concerns are the quality of the classification problems as well as the appropriate number of levels of the tree. Some leaves and branches need to be removed in order to improve the performance of the decision tree. This step is also called tree pruning. On the higher level, the predetermined model can be used as a prediction tool. Before that, the testing data sets should be fed into the model to evaluate the Transaction = 50 x > 35 ? No Yes Transaction = 15 Transaction = 35 y > 52 ? y > 25 ? No Yes No Yes Transaction = 9 Transaction = 6 Transaction = 25 Transaction = 10 Group E Group D x > 65 ? Group C No Yes Transaction = 15 Transaction = 10 Group A Group B model performance. Scalability of the model is the major concern in this phase. Figure 4.3: A decision tree classifying transactions into five groups The fundamental algorithms can be different in each model. Probably the most popular ones are Classification and Regression Trees (CART) and Chi-Square Automatic Interaction Detector (CHAID). For the sake of simplicity, I will not go into the details of these algorithms and only perspectives of them are provided. CART is an algorithm developed by Leo Breiman, Jerome Friedman, Richard Olshen and Charles Stone. The advantage of CART is that it automates the
  45. 45. - 39 - pruning process by cross validation and other optimizers. It is capable of handling missing data and it sets the unqualified records apart from the training data sets. CHAID is another decision tree algorithm that uses contingency tables and the chi-square test to create the tree. The disadvantage of CHAID comparing to CART is that it requires more data preparation process. 4.6.3. Neural Networks Nowadays, neural networks, or more correctly the artificial neural networks (ANN), attract the most interest among all data mining algorithms. It is a computer model based on the architecture of the brain. To put it simply, it first detects the pattern from data sets. Then, it predicts the best classifiers. And finally, it learns from the mistakes. It works best in classification and prediction as well as clustering methods. The structure of neural network is shown in figure 4.4. First hidden layer Second hidden layer Input Layer Output layer Figure 4.4: A neural network with two hidden layers As noticed in figure 4.4, neural network is comprised of neurons in input layer, one or more hidden layers and output layer. Each pair of neurons is connected with a weight. In the cases where there are more than one input neurons, the input weights are combined using a combination function such as summation (Berry &
  46. 46. - 40 - Linoff, 2000, 122). During training phase, the network learns by adjusting the weights so as to be able to predict the correct output (Han & Kamber, 2000, 303). The most well known neural network learning algorithm is backpropagation. It is the method of updating the weights of the neurons. Unlike other learning algorithms, backpropagation algorithm works, or learns and adjusts the weights, backward which simply means that it predicts the weighted algorithms by propagating the input from the output. Neural networks are widely recognized for its robustness; however, the weakness is its lack of self-explanation capability. Though the performance of the model is satisfactory, some people do not feel comfortable or confident to rely irrationally on the model. It should note that some algorithms are good at discovering specific methods where some others are appropriate for many types of methods. The choice of algorithm or set of algorithms used depends solely on user’s judgement. 4.7. Summary Data mining, which is also known as knowledge discovery in databases (KDD), is the area of attention in recent years. It is a set of techniques that exhaustively automated to uncover potentially interesting patterns from a large amount of data in any kind of data repositories. Data mining goals can be roughly divided into two main categories, verification (including explanation and confirmation) and discovery. The first step of the data mining process is to map business problems to data mining problems. Then, data to be mined is captured, studied, selected and preprocessed respectively. The preprocessed activities are performed in order to prepare final data sets to be fed into data mining model. Next, data mining model is constructed, tested, and applied. The results of this step are evaluated subsequently. If the result is satisfactory, then it will be deployed in the real business environment. Lessons learned during data mining engagement should be recorded as guidelines for future project. As data mining is developed from and driven by multidisciplinary fields, different tools and techniques can be applied in each step of data mining process. Those