Data Mining As A Financial Auditing Tool

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Data Mining As A Financial Auditing Tool

M.Sc. Thesis in Accounting

Swedish School of Economics and Business Administration 2002

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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

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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

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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,

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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.

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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.

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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.

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- 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.

2.3.2.1. 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.

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2.3.2.2. 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.

2.3.2.3. 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.

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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

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found in Statement of Auditing Standard (SAS) No. 47: Audit Risk and Materiality in Conducting an Audit (AICPA, 1983).

2.3.2.4. 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.

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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

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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.

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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.

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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:

2.4.2.1. 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.

2.4.2.2. 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

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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.

2.4.2.3. 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.

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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

Gather Information

Perform preliminary analytical procedures Assess risk and control

Set materiality

Develop audit plan and detailed audit program

Perform Tests of Controls

Perform Substantive Tests - Detailed Tests of Transactions

- Analytical Procedures - Detailed Tests of Balances

Gather audit evidence and audit findings Review subsequent events

Evaluate overall results Issue auditor’s report

Arrange clearance meeting with client Evaluate team performance

Mobilize

Gather information in details Evaluate client C li en t A cc ep ta n ce P la n n in g E x ec u ti o n & D o cu m en ta ti o n C o m p le ti o n High Low Contr ol Relia nce Tests of Controls - Identify controls - Assess control reliance - Select samples

- Test controls

- Further investigate for unusual items

- Evaluate Results

Analytical Review

- Develop expectations - Compare expectations with actual figures - Further investigate for major differences - Evaluate Results

Detailed Tests

- Select samples - Test samples

- Further investigate for unusual items

- Evaluate results Document testing results

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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.

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3. Current Auditing Computerized Tools

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

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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.

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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. (www.acl.com). 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.

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Audit Processes ACL Features

Planning

- Risk assessment - “Statistics” menu

- “Evaluation” menu Execution and Documentation

Tests of Controls - Sample selection - Controls Testing - Results evaluation Analytical Review - Expectations development - Expected versus actual figures

comparison

- Results evaluation

- “Sampling” menu with the ability to specify sampling size and selection criteria

- “Filter” menu

- “Analyze” menu including Count, Total, Statistics, Age, Duplicate, Verify and Search

- Expression builder - Evaluation menu

- “Statistics” menu - “Merge” command

- “Analyze” menu including Statistics, Age, Verify and Search

- Expression builder - Evaluation menu

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Audit Processes ACL Features Detailed Tests - Sample selection - Sample testing - Results evaluation Documentation

- “Sampling” menu with the ability to specify sampling size and selection criteria

- “Filter” menu

- “Analyze” menu including Count, Total, Statistics, Age, Duplicate, Verify and Search

- Expression builder - Evaluation menu - 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

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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,

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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.

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4. Data mining

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.

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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,

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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

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.

Processes / Phases

Process Instances Special Tasks Generic Tasks

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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.

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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

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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.

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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,

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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

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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.

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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

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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

Data Mining As A Financial Auditing Tool