data mining as an audit 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. IBMs 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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Table of Contents

1. Introduction 1

1.1. Background 1

1.2. Research Objective 2

1.3. Thesis Structure 2

2. Auditing 4

2.1. Objective and Structure 4

2.2. What Is Auditing? 4

2.3. Audit Engagement Processes 5

2.3.1. Client Acceptance or Client Continuance 5

2.3.2. Planning 6

2.3.2.1. Team Mobilization 6

2.3.2.2. Clients Information Gathering 7

2.3.2.3. Risk Assessment 7

2.3.2.4. Audit Program Preparation 9

2.3.3. Execution and Documentation 10

2.3.4. Completion 11

2.4. Audit Approaches 12

2.4.1. Tests of Controls 12

2.4.2. Substantive Tests 13

2.4.2.1. Analytical Procedures 13

2.4.2.2. Detailed Tests of Transactions 13

2.4.2.3. Detailed Tests of Balances 14

2.5. Summary 14

3. Computer Assisted Auditing Tools 17


3.2. Why Computer Assisted Auditing Tools? 17

3.3. Generalized Audit Software 18

3.4. Other Computerized Tools and Techniques 22

3.5. Summary 23


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4. Data mining 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 334.5.3. 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


5.2. Why Integrate Data Mining with Auditing? 43


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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


6.2. 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


7.2. Hypothesis 73

7.3. Research Processes 73

7.3.1. Business Understanding 73

7.3.2. Data Understanding 74

7.3.3. Data Preparation 75

7.3.3.1. Data Transformation 757.3.3.2. Attribute Selection 76

7.3.3.3. Choice of Tests 80

7.3.4. Software Deployment 82

7.3.4.1. IBMs DB2 Intelligent Miner for Data 82

7.3.4.2. ACL 91

7.4. Result Interpretations 94

7.4.1. IBMs DB2 Intelligent Miner for Data 94

7.4.2. ACL 95

7.5. Summary 99


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8. Conclusion 101


8.2. Research Perspective 101

8.3. Implications of the Results 102

8.4. Restrictions and Constraints 103

8.4.1. Data Limitation 103

8.4.1.1. Incomplete Data 103

8.4.1.2. Missing Information 103

8.4.1.3. Limited Understanding 104

8.4.2. Limited Knowledge of Software Packages 104

8.4.3. Time Constraint 105

8.5. Suggestions for Further Researches 105

8.6. Summary 105

List of Figures 105

List of Tables 105

References 105

a) Books and Journals 105

b) Web Pages 105

Appendix A: List of Columns of Data Available 109

Appendix B Results of IBMs 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


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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 clients 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 todays 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 companys 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 miningtechniques 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 professions 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 companys financial statements. Such written report is called auditors

opinion or auditors report.

Auditors 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, auditors 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 levelof 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 auditors objectivity and integrity. These

factors help to ensure a quality audit and to earn peoples 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, clients 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. Clients 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

clients needs and expectations as well as managements 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 clients 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 RiskPlanned 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 clients 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 clients

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 clients 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 auditors reliance on clients 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 clients

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 clients 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 accountfor 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 toperform 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 clients management

(adjusting their financial statements or adequately disclosing them in their financial

statement) or by auditors (disclosing them in the auditors 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 auditors report date that require recognition in the financial statements.

Based on accumulated audit evidences and audit findings, the auditors

opinion can be issued. Types of auditors 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 clients 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 auditors report

Arrange clearance meeting with client

Evaluate team performance

Mobilize

Gather information in details

Evaluate clientClient

Acceptance

Planning

Ex

ecution&

Documentation

Completion

High

LowCo

ntrol

Relia

nce

Tests of Controls

- Identify controls

- Assess control reliance

- Select samples

- Test controls

- Further investigate forunusual 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 auditors 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 clients system and then download

clients 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


- Sampling menu with the ability to

specify sampling size and selectioncriteria

- 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


- Evaluation menu

Table 3.1: ACL features used in assisting each step of audit processes


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Audit Processes ACL Features

Detailed Tests

- Sample selection

- Sample testing


Documentation

- Sampling menu with the ability to

specify sampling size and selection

criteria

- Filter menu

- Analyze menu including Count,


Verify and Search


- 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,


Verify and Search


- Filter menu

Table 3.1: ACL features used in assisting each step of audit processes (Continued)

With ACLs 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 ACLs

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 thatused to be performed by the EDP auditors has now become part of an auditors

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 clients accounting system, and

- Continuous auditing: embed audit program into clients 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 isdesigned 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, outlieranalysis 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 generalcharacteristics 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

analysts 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 tothe 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

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.


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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.


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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.


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(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 supportcount, 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.


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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

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

Transaction = 50x > 35 ?

Transaction = 15

y > 52 ?

Transaction = 35

y > 25 ?

Transaction = 9

Group E

Transaction = 6

Group D

Transaction = 25

x > 65 ?

Transaction = 10

Group C

Transaction = 15

Group A

Transaction = 10

Group B

No Yes

No Yes

No Yes

No Yes


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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 arti