Data Mining As A Financial Auditing Tool
M.Sc. Thesis in Accounting
Swedish School of Economics and Business Administration
The Swedish School of Economics and Business Administration
Type of Document: Thesis
Title: Data Mining As A Financial Auditing Tool
Author: Supatcharee Sirikulvadhana
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
Search Words: Data Mining, Artificial Intelligent, Auditing, Computerized Audit
Assisted Tools, Generalized Audit Software
Table of Contents
1. Introduction 1
1.1. Background 1
1.2. Research Objective 2
1.3. Thesis Structure 2
2. Auditing 4
2.1. Objective and Structure 4
2.2. What Is Auditing? 4
2.3. Audit Engagement Processes 5
2.3.1. Client Acceptance or Client Continuance 5
2.3.2. Planning 6
188.8.131.52. Team Mobilization 6
184.108.40.206. Client’s Information Gathering 7
220.127.116.11. Risk Assessment 7
18.104.22.168. 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
22.214.171.124. Analytical Procedures 13
126.96.36.199. Detailed Tests of Transactions 13
188.8.131.52. Detailed Tests of Balances 14
2.5. Summary 14
3. Computer Assisted Auditing Tools 17
3.1. Objective and Structure 17
3.2. Why Computer Assisted Auditing Tools? 17
3.3. Generalized Audit Software 18
3.4. Other Computerized Tools and Techniques 22
3.5. Summary 23
4. Data mining 24
4.1. Objective and Structure 24
4.2. What Is Data Mining? 24
4.3. Data Mining process 25
4.3.1. Business Understanding 26
4.3.2. Data Understanding 27
4.3.3. Data Preparation 27
4.3.4. Modeling 27
4.3.5. Evaluation 28
4.3.6. Deployment 28
4.4. Data Mining Tools and Techniques 29
4.4.1. Database Algorithms 29
4.4.2. Statistical Algorithms 30
4.4.3. Artificial Intelligence 30
4.4.4. Visualization 30
4.5. Methods of Data Mining Algorithms 32
4.5.1. Data Description 32
4.5.2. Dependency Analysis 33
4.5.3. Classification and Prediction 33
4.5.4. Cluster Analysis 34
4.5.5. Outlier Analysis 34
4.5.6. Evolution Analysis 35
4.6. Examples of Data Mining Algorithms 36
4.6.1. Apriori Algorithms 36
4.6.2. Decision Trees 37
4.6.3. Neural Networks 39
4.7. Summary 40
5. Integration of Data Mining and Auditing 43
5.1. Objective and Structure 43
5.2. Why Integrate Data Mining with Auditing? 43
5.3. Comparison between Currently Used Generalized Auditing Software
and Data Mining Packages 44
5.3.1. Characteristics of Generalized Audit Software 45
5.3.2. Characteristics of Data Mining Packages 46
5.4. Possible Areas of Integration 48
5.5. Examples of Tests 58
5.6. Summary 66
6. Research Methodology 68
6.1. Objective and Structure 68
6.2. Research Period 68
6.3. Data Available 68
6.4. Research Methods 69
6.5. Software Selection 70
6.5.1. Data Mining Software 70
6.5.2. Generalized Audit Software 71
6.6. Analysis Methods 71
6.7. Summary 72
7. The Research 73
7.1. Objective and Structure 73
7.2. Hypothesis 73
7.3. Research Processes 73
7.3.1. Business Understanding 73
7.3.2. Data Understanding 74
7.3.3. Data Preparation 75
184.108.40.206. Data Transformation 75
220.127.116.11. Attribute Selection 76
18.104.22.168. Choice of Tests 80
7.3.4. Software Deployment 82
22.214.171.124. IBM’s DB2 Intelligent Miner for Data 82
126.96.36.199. ACL 91
7.4. Result Interpretations 94
7.4.1. IBM’s DB2 Intelligent Miner for Data 94
7.4.2. ACL 95
7.5. Summary 99
8. Conclusion 101
8.1. Objective and Structure 101
8.2. Research Perspective 101
8.3. Implications of the Results 102
8.4. Restrictions and Constraints 103
8.4.1. Data Limitation 103
188.8.131.52. Incomplete Data 103
184.108.40.206. Missing Information 103
220.127.116.11. 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
a) Books and Journals 105
b) Web Pages 105
Appendix A: List of Columns of Data Available 109
Appendix B Results of IBM’s Intelligent Miner for Data 105
a) Preliminary Neural Clustering (with Six Attributes) 105
b) Demographic Clustering: First Run 105
c) Demographic Clustering: Second Run 105
d) Neural Clustering: First Run 105
e) Neural Clustering: Second Run 105
f) Neural Clustering: Third Run 105
g) Tree Classification: First Run 105
h) Tree Classification: Second Run 105
i) Tree Classification: Third Run 105
Appendix C: Sample Selection Result of ACL 105
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
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,
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
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
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
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.
- 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.
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
18.104.22.168. 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.
22.214.171.124. 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
- 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.
126.96.36.199. 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.
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
found in Statement of Auditing Standard (SAS) No. 47: Audit Risk and Materiality in
Conducting an Audit (AICPA, 1983).
188.8.131.52. 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
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
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
- 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
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.
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
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
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
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:
184.108.40.206. 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
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.
220.127.116.11. 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.
18.104.22.168. 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
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.
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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Gather information in details
Perform preliminary analytical procedures
Tests of Controls
Assess risk and control
- Identify controls
Set materiality - Assess control reliance
- Select samples
Develop audit plan and detailed audit program - Test controls
- Further investigate for
Execution & Documentation
Perform Tests of Controls - Evaluate Results
Con ance Low
High Rel - Develop expectations
Perform Substantive Tests - Compare expectations
- Detailed Tests of Transactions with actual figures
- Analytical Procedures - Further investigate for
- Detailed Tests of Balances major differences
- Evaluate Results
Document testing results
- Select samples
Gather audit evidence and audit findings - Test samples
Review subsequent events - Further investigate for
Evaluate overall results - Evaluate results
Issue auditor’s report
Arrange clearance meeting with client
Evaluate team performance
Figure 2.1: Summary of audit engagement processes
Planning includes mobilization, information gathering, risk assessment and
audit program preparation. Two basic types of audit approaches the auditors can use
during execution phase are tests of controls and substantive tests. Substantive tests
include analytical procedures, detailed tests of transactions and detailed tests of
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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
3.1. Objective and Structure
The objective of this chapter is to provide information about technological
tools and techniques currently used by auditors. Section 3.2 discusses why computer
assisted auditing tools (CAATs) are more than requisite in auditing profession at
present. In section 3.3, general audit software (GAS) is reviewed in detail. The topic
focuses on the most popular software, Audit Command Language (ACL). Other
computerized tools and techniques are briefly identified in section 3.4. Finally, a brief
summary of some currently used CAATs is provided in section 3.5.
Before proceeding, it is worth noting that this chapter was mainly based on two
textbooks and one journal, which are “Accounting Information Systems” (Bonar &
Hopwood, 2001), “Core Concept of Accounting Information System” (Moscove,
Simkin & Bagranoff, 2000) and “Audit Tools” (Needleman, 2001).
3.2. Why Computer Assisted Auditing Tools?
It is accepted that advances in technology have affected the audit process.
With the ever increasing system complexity, especially the computer-based accounting
information systems, including enterprise resource planning (ERP), and the vast amount
of transactions, it is impractical for auditors to conduct the overall audit manually. It is
even more impossible in an e-commerce intensive environment because all accounting
data auditors need to access are computerized.
In the past ten years, auditors frequently outsource technical assistance in some
auditing areas from information system (IS) auditor, also called electronic data
processing (EDP) auditor. However, when the computer-based accounting information
systems become commonplace, such technical skill is even more important. The rate of
growth of the information system practices within the big audit firms (known as “the
Big Five”) was estimated at between 40 to 100 percent during 1990 and 2005
(Bagranoff & Vendrzyk, 2000, 35).
Nowadays, the term “auditing with the computer” is extensively used. It
describes the employment of the technologies by auditors to perform some audit work
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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
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
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
- Risk assessment - “Statistics” menu
- “Evaluation” menu
Execution and Documentation
Tests of Controls
- Sample selection - “Sampling” menu with the ability to
specify sampling size and selection
- “Filter” menu
- Controls Testing - “Analyze” menu including Count,
Total, Statistics, Age, Duplicate,
Verify and Search
- Expression builder
- Results evaluation - Evaluation menu
- Expectations development - “Statistics” menu
- Expected versus actual figures - “Merge” command
comparison - “Analyze” menu including Statistics,
Age, Verify and Search
- Expression builder
- Results evaluation - Evaluation menu
Table 3.1: ACL features used in assisting each step of audit processes
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Audit Processes ACL Features
- Sample selection - “Sampling” menu with the ability to
specify sampling size and selection
- “Filter” menu
- Sample testing - “Analyze” menu including Count,
Total, Statistics, Age, Duplicate,
Verify and Search
- Expression builder
- Results evaluation - Evaluation menu
Documentation - Document note
- Automatic command log
- File history
- Lesson learned record - “Document Notes” menu
- “Reports” menu
- 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
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
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
- System testing: test controls of the client’s accounting system, and
- Continuous auditing: embed audit program into client’s system.
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
4.1. Objective and Structure
The objective of this chapter is to describe the basic concept of data mining.
Section 4.2 provides some background on data mining and explains its basic element.
Section 4.3 describes data mining processes in greater detail. Data mining tools and
techniques are discussed in section 4.4 and methods of data mining algorithms are
discussed in section 4.5. Examples of most frequently used data mining algorithms are
provided in section 4.6. Finally, the brief summary of data mining is reviewed in
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 &
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
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
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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
Processes / Phases
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
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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
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
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.
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
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
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.
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
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
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
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.
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 &
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-
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
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
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
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
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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
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
(e) The support count of each candidate is accumulated.
(f) After pruning unsatisfactory item sets according to minimum support
count, the frequent 2-itemsets is created.
(g) The iteration of (d), (e) and (f) are executed until no more frequent k-
itemsets can be found or, in other words, the next frequent k-itemsets
contains empty frequent.
(h) At the terminated level, the Candidate with maximum support count
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,
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
Transaction = 50
x > 35 ?
Transaction = 15 Transaction = 35
y > 52 ? y > 25 ?
No Yes No Yes
Transaction = 9 Transaction = 6 Transaction = 25 Transaction = 10
Group E Group D x > 65 ? Group C
Transaction = 15 Transaction = 10
Group A Group B
model performance. Scalability of the model is the major concern in this phase.
Figure 4.3: A decision tree classifying transactions into five groups
The fundamental algorithms can be different in each model. Probably
the most popular ones are Classification and Regression Trees (CART) and Chi-Square
Automatic Interaction Detector (CHAID). For the sake of simplicity, I will not go into
the details of these algorithms and only perspectives of them are provided.
CART is an algorithm developed by Leo Breiman, Jerome Friedman,
Richard Olshen and Charles Stone. The advantage of CART is that it automates the
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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 artificial neural
networks (ANN), attract the most interest among all data mining algorithms. It is a
computer model based on the architecture of the brain. To put it simply, it first detects
the pattern from data sets. Then, it predicts the best classifiers. And finally, it learns
from the mistakes. It works best in classification and prediction as well as clustering
methods. The structure of neural network is shown in figure 4.4.
First hidden layer Second hidden layer
Input Layer Output layer
Figure 4.4: A neural network with two hidden layers
As noticed in figure 4.4, neural network is comprised of neurons in input
layer, one or more hidden layers and output layer. Each pair of neurons is connected
with a weight. In the cases where there are more than one input neurons, the input
weights are combined using a combination function such as summation (Berry &
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Linoff, 2000, 122). During training phase, the network learns by adjusting the weights
so as to be able to predict the correct output (Han & Kamber, 2000, 303).
The most well known neural network learning algorithm is
backpropagation. It is the method of updating the weights of the neurons. Unlike other
learning algorithms, backpropagation algorithm works, or learns and adjusts the
weights, backward which simply means that it predicts the weighted algorithms by
propagating the input from the output.
Neural networks are widely recognized for its robustness; however, the
weakness is its lack of self-explanation capability. Though the performance of the
model is satisfactory, some people do not feel comfortable or confident to rely
irrationally on the model.
It should note that some algorithms are good at discovering specific methods
where some others are appropriate for many types of methods. The choice of algorithm
or set of algorithms used depends solely on user’s judgement.
Data mining, which is also known as knowledge discovery in databases
(KDD), is the area of attention in recent years. It is a set of techniques that exhaustively
automated to uncover potentially interesting patterns from a large amount of data in any
kind of data repositories. Data mining goals can be roughly divided into two main
categories, verification (including explanation and confirmation) and discovery.
The first step of the data mining process is to map business problems to data
mining problems. Then, data to be mined is captured, studied, selected and
preprocessed respectively. The preprocessed activities are performed in order to
prepare final data sets to be fed into data mining model. Next, data mining model is
constructed, tested, and applied. The results of this step are evaluated subsequently. If
the result is satisfactory, then it will be deployed in the real business environment.
Lessons learned during data mining engagement should be recorded as guidelines for
As data mining is developed from and driven by multidisciplinary fields,
different tools and techniques can be applied in each step of data mining process. Those