Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Common Indicators of White-Collar Crime in Malaysia
M. Affendy Arip, Samuel Wei-Siew Liew and Chin-Hong Puah
This paper aims to examine the common indicators to detect the white-collar crime in Malaysia.
With the advancement of information, communication, and technology, chances of committing
white-collar crime among the employee who know the loophole of the company’s management and
operational system is expected to increase. By identifying the most common indicators of this
crime, it will narrow down the area of management and operation that needs greater attention to
curb white-collar crime problems. Our main intention of this study is to highlight the most common
indicators that can be used to detect, hence preventing the employee from committing this crime.
For this purpose, we look at two main areas which include behavioral indicators, and organizational
and managerial indicators. Empirical findings suggest that the perpetrators of white-collar crime
usually experience a change in lifestyle and behavior as they gain a windfall of money. The results
also reveal that the management failure to display appropriate attitude about internal control has
created opportunities for the employees to commit this crime. Even with the placement of good
internal controls, it may not be sufficient as perpetrators will always try to find ways around them for
this purpose.
JEL Codes: K20, K42 and G34
Field: Finance
1. Introduction
According to the statistics reveal by the Commercial Crimes Investigation Department (CCID), the number of
white-collar crime cases in Malaysia investigated in 2003 was 11,714 cases involving money worth about
RM579 million, and in 2004 the number of cases declined (9,899) but the amount of losses rose to about
RM836 million (Lim, 2005). Both number of cases and amount of money involve then increase to about
17,311 cases and RM846 million respectively in 2008 (“White-Collar Crimes,” 2009). The survey done by
KPMG (2009) indicates that the occurrence of this crime is expected to rise. This trend is very alarming and
detrimental to the companies in Malaysia especially to the foreign investors. It will deteriorate investors’
confidence towards the country’s business ethics.
The technological advancement was believed to have contributed to the drastic rise in white-collar crime.
Criminals have become more sophisticated and the post-1990s era witnessed a change in the modus
operandi of white-collar crime (Lim, 2005). They have ventured into highly specialized crimes like “high-tech
check scams” (cloning of checks), ATM/credit card fraud, share scams, “Flight by Night” scams, internet fraud,
and money laundering (Lim, 2005). Emergence of new crime methods have contributed to the rise in the
number of cases and it poses new challenges to related agencies to equip themselves with the necessary
skills to combat white-collar crime in the country.
It is, therefore, essential to provide a basic understanding on the common indicators of white-common crime in
Malaysia. Such an understanding will ease the detection of white-collar crime and thereby allows actions to be
taken against it at the earliest stage possible to prevent incurring enormous amount of losses. In short, the
rationale behind this study lies in educating and increase the knowledge among the stakeholders of a
company towards the red flags of white-collar crime in Malaysia.

Corresponding author: M. Affendy Arip, E-mail address: amaffendy@feb.unimas.my
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
2. Literature Review
It is the beliefs of some researchers that identifying the indicators or the warning signs of white-collar crime
may prove helpful in terms of early detection or even prevention of the occurrences of crime within
organizations. Smith, Omear, Idris, and Baharuddin (2005) found that management failure to display
appropriate attitude about internal control as the highest ranked individual red flag, followed by unusually high
dependence on debt in Malaysia. In another similar study in Malaysia, insufficient control is ranked the most
important individual red flag, followed by personal financial pressure and expensive lifestyle (Voon, Voon &
Puah, 2008).
The findings of the two research papers consistently state the importance of internal control within
organizations to detect white-collar crime in the early stage. KPMG (2009) also report that internal control is
among one of the most important factors that could prevent fraud to take place in an organization. However,
whether the identified white-collar crime indicators or red flags are indeed useful in the detection and eventual
reduction of white-collar crime are still remain as an empirical issue that requires further inspection among the
practitioners within organizations (Smith et al., 2005).
On a more individualistic note, Lux and Fitiani (2002) identified the general red flags for asset
misappropriation as changes in behavior, inability to look people in the eye, increased irritability, irregular work
history, character problems, consistent anger, tendency to blame others, and change in lifestyle. The last red
flag is perhaps the most noticeable (Singleton, Singleton, Bologna & Lindquist, 2006). According to Singleton
et al. (2006), the red flags for asset misappropriation are different from those for financial statement frauds.
The latter would include accounting anomalies, rapid growth, unusual profits, internal control weaknesses, and
aggressiveness for executive management, with the last one being “the most telling common red flag” (p.
129).
3. DATA AND METHODOLOGY
The respondents can be any potential or existing investor, who is at least 17 years of age, in Malaysia. The
minimum age of the potential or an existing investor is 17 so as to ensure that the respondents have at least a
minimal understanding about white-collar crime. 300 completed questionnaires are gathered via the nonprobability sampling procedure. The questionnaire is divided into two sections. In the first section, respondents
have to fill in their demographic information, and to rate their perception on a five-point numerical scale
towards the different red flags of white-collar crime in the second section. To increase the construct validity of
the questionnaire and the reliability of the findings, the construction of the questionnaire is based on published
surveys, researches, and textbooks (Smith et al., 2005). Some examples of the published works are Smith et
al. (2005), Singleton et al. (2006), and Voon et al. (2008).
Upon gathering 300 completed questionnaires, the demographic data is then translated into a frequency table.
One-way analysis of variance (ANOVA) is also conducted to test whether or not the difference in the
respondents’ demographics influence the outcome of their perception towards the common indicators of
white-collar crime listed in the questionnaire. The indicators are then ranked according to their mean in a
descending manner from the highest mean to the lowest mean.
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
4. RESULT DISCUSSION
Table 1 shows the profiles of 300 people who responded to questionnaire. Most of the respondents are male,
but the percentage difference between male and female is only 1.4%. Therefore, it can be deduced that the
distribution of respondents in terms of gender is balanced. The bulk of the respondents are in the age group of
20-29. They are mostly Chinese, followed by Malay and the other ethnic groups. Some examples of the ethnic
groups are Iban, Bidayuh and Kayan. The number of Indian respondents is the lowest. Concerning the
demographic information of marital status, the frequencies for both single and married respondents are almost
the same. The percentage difference is merely 4%. Almost half of the total respondents are degree holders.
The minority of respondents are master’s degree or higher, and certificate holders, constituting a combined
total of almost 18%. As for the respondents’ occupation, most of them are working as professionals. Their
professions include lecturers, stock analysts and engineers, to name few. They constitute 27% of the total
respondents. However, most of the respondents earn less than RM4,000 a month, constituting a combined
total of 70.7%.
Table 1: Respondents’ Profiles
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
20-29
124
41.3
30-39
61
20.3
40-49
58
19.3
50 or above
38
12.7
Male
152
50.7
Female
148
49.3
Single
154
51.3
Married
142
47.3
4
1.3
41
13.7
195
65.0
Indian
7
2.3
Others
57
19.0
Chinese
Education
Level
6.3
Occupation
19
Malay
Demographics
%
17-19
Divorced
Ethnicity
ƒ
Income per
Month
Marital
Status
Gender
Age
Demographics
SPM or below
Certificate
Diploma
Bachelor Degree
Master Degree or above
Professional
Management &
Administration
Clerical
Sales & Services
Technical
Self-Employed
Home Duties
Unemployed
Retired
Others
Below RM2,000
RM2,000-RM3,999
RM4000-RM5,999
RM6000-RM7,999
RM8,000 or above
ƒ
%
54
26
59
134
27
81
48
18.0
8.7
19.7
44.7
9.0
27.0
16.0
23
20
9
47
6
12
5
49
108
104
49
20
19
7.7
6.7
3.0
15.7
2.0
4.0
1.7
16.3
36.0
34.7
16.3
6.7
6.3
In addition, Table 2 depicts the one-way ANOVA statistical test results of whether or not demographics
differences influence the outcome of respondents’ perception towards the common indicators of white-collar
crime listed in the questionnaire. For this purpose the significance level of 5% is set for the statistical test.
Based on this criterion, the results suggest that crimes are greatly influenced demographics, with the
exceptions of gender, ethnicity and level of education. This therefore implies that individual experience,
working environment, social status, and the community in which he/she lives or grows up do influence
his/her perception towards the red flags of white-collar crime.
Table 2: One-Way ANOVA of Respondents’ Demographics towards the Indicators of White-Collar Crime in
Malaysia
Demographics
Age
Gender
Marital Status
Ethnicity
Education Level
Occupation
Income per Month
(p-value)
0.000**
0.067
0.008**
0.051
0.059
0.003**
0.001**
Note: Asterisks (**) denote significant at 5% level.
Table 3 indicates the white-collar crime risk according to the ranking of their commonness. The most
common and significant indicator of white-collar crime risk in Malaysia is the change of lifestyle. When a
lifestyle of an employee suddenly improves without any reasonable explanation, there is high probability of
that employee exercising illegal activities in carrying their task. The mean for this indicator is 3.73, followed
by changes in behavior at the mean of 3.47, and management failure to display appropriate attitude about
internal control, which has a mean of 3.41. The least common indicator is difficulty in determining
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
organizational control. In other words, the obscurity of an organization’s internal control is not a usual
indicator of white-collar crime risk in Malaysia.
Table 3: Ranking of the Indicators of White-Collar Crime Risk in Malaysia
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Indicators of White-Collar Crime Risk
Change in lifestyle
Changes in behavior
Management failure to display appropriate attitude about internal control
Character problems
Significant pressure to obtain capital
Negative operating cash flow but reported earnings
Unusually high dependence on debt
Threat of imminent bankruptcy
Inability to look people in the eye
Tendency to blame others
Poor/deteriorating financial position with management guarantee of firm's debt
High degree of competition/market saturation and declining margins
Unusually rapid growth/profitability relative to industry
High turnover of senior management
Increased irritability
Irregular work history
Overly complex organization
Consistent anger
Significant accounts based on estimates
Known history of securities law violations
Rapid changes in industry and vulnerability to changing technology
Company in declining industry
Presence of aggressive incentive programs
Strained management-auditor relationship
Difficulty in determining organizational control
Mean
3.73
3.47
3.41
3.37
3.35
3.30
3.28
3.21
3.20
3.20
3.15
3.12
3.12
3.05
3.03
3.00
2.92
2.92
2.92
2.89
2.89
2.86
2.84
2.84
2.83
As shown in Table 4, the white-collar crime risk can be divided into two groups under the headings of
behavioral indicators, and organizational and managerial indicators. The respondents believe that
behavioral indicators are more common than organizational and managerial indicators in Malaysia.
Apparent changes in lifestyle and behavior, for instance, strongly indicate the existence of white-collar
crime within an organization. Organizational and managerial indicators are generally perceived to be
weaker in terms of red-flagging white-collar crime risk.
These findings are in line with the study in investigating the most common indicator white-collar crime by
Singleton et al. (2006). A drastic change in the way the perpetrator lives and the sudden extravagant
spending, particularly on real assets and material goods, can easily arouse suspicion among colleagues
(Singleton et al., 2006). This can also be linked to the second highest ranked indicator – changes in
behavior. The perpetrator will appear to be much more generous than usual, especially towards family
members and friends. On the contrary, he/she may display attitude that results in poor relationships with
colleagues and supervisors. The stark changes in behavior seriously indicate that the perpetrator may have
engaged in some misdeeds.
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Table 4: Ranking of the Indicators of White-Collar Crime Risk by Group
Rank
1
2
Indicators of White-Collar Crime Risk
Individual
Mean
Behavioral indicators
1.1
Change in lifestyle
1.2
Changes in behavior
1.3
Character problems
1.4
Inability to look people in the eye
1.5
Tendency to blame others
1.6
Increased irritability
1.7
Irregular work history
1.8
Consistent anger
3.73
3.47
3.37
3.20
3.20
3.03
3.00
2.92
Organizational and managerial indicators
2.1
Management failure to display appropriate attitude about internal control
2.2
Significant pressure to obtain capital
2.3
Negative operating cash flow but reported earnings
2.4
Unusually high dependence on debt
2.5
Threat of imminent bankruptcy
2.6
Poor/deteriorating financial position with management guarantee of firm's debt
2.7
High degree of competition/market saturation and declining margins
2.8
Unusually rapid growth/profitability relative to industry
2.9
High turnover of senior management
2.10
Overly complex organization
2.11
Significant accounts based on estimates
2.12
Known history of securities law violations
2.13
Rapid changes in industry and vulnerability to changing technology
2.14
Company in declining industry
2.15
Presence of aggressive incentive programs
2.16
Strained management-auditor relationship
2.17
Difficulty in determining organizational control
3.41
3.35
3.30
3.28
3.21
3.15
3.12
3.12
3.05
2.92
2.92
2.89
2.89
2.86
2.84
2.84
2.83
Group
Mean
3.24
3.06
In relation to changes in organizational and managerial components, it is discovered that management
failure in displaying appropriate attitude about internal control is the most common indicator. It is also found
to be the top ranked indicator in Smith et al. (2005) followed by other indicators such as significant pressure
to obtain capital, negative operating cash flow but reported earnings, unusually high dependence on debt,
and threat of imminent bankruptcy.
One of the reasons for management failure to display appropriate attitude about internal control as the top
rank indictor is insufficient control. In the study conducted by Voon et al. (2008), insufficient control is found
to be the leading determinant of corporate crime. This, coupled with poor internal control as a common
causes, clearly demonstrate the importance of a company to streamline internal activities in order to avoid
the occurrences of white-collar crime.
Meanwhile, the findings that difficulty in determining organizational control as least indicator of white-collar
crime in Malaysia is similar to the results obtained by Smith et al. (2005). This can be linked to the inherent
nature of most businesses in Malaysia. Many companies regard the information of their internal control as
classified, fearing that competitors might take advantage of the information. Since it is fundamentally
difficult to determine organizational control, therefore, it does not appear as a common or an important
indicator.
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
5. CONCLUSION
Perpetrators of white-collar crime usually experience a change in lifestyle and behavior as they gain a
windfall of money. Although the placement of good internal controls is important, it may not be sufficient as
perpetrators can always find ways around them to commit white-collar crime. Supervisors and managers
must therefore be made aware of the common indicators of white-collar crime risk, for example, changes in
lifestyle and behavior of an employee. Equipped with the awareness of common indicators, detecting whitecollar crime becomes easier. Early detection is always desirable as it helps to keep the losses incurred from
white-collar crime at minimal.
ACKNOWLEDGEMENT
The authors acknowledge the financial support of the Universiti Malaysia Sarawak and Research
Acculturation Grant Scheme No. RAGS/SS07(1)/1035/2013(02).
References
KPMG. (2009). KPMG Malaysia Fraud Survey Report 2009. Kuala Lumpur: KPMG.
Lim,
H.S.
(2005).
White-collar
crime
in
http://rmpckl.rmp.gov.my/Journal/BI/whitecollarcrime.pdf
Malaysia.
Retrieved
from
Lux, A.G. & Fitiani, S. (2002). Fighting internal crime before it happens. Information Systems Control
Journal, III, 50-51.
Singleton, T.W., Singleton, A.J., Bologna, G.J. & Lindquist, R.J. (2006). Fraud Auditing and Forensic
Accounting (3rd ed.). New Jersey: John Wiley & Sons.
Smith, M., Omear, N.H., Idris, I.Z. & Baharuddin, I. (2005). Auditors’ perception of fraud indicators –
Malaysian evidence. Managerial Auditing Journal, 20(1), 73-82.
Voon, M.L., Voon, S.L. & Puah, C.H. (2008). An empirical analysis of the determinants of corporate crime in
Malaysia. International Applied Economics and Management Letters, 1(1), 13-17.
White-collar crimes accounted for RM788 mil losses last year – IGP. (2009, June 19). Bernama. Retrieved
from
http://www.insuranceonline.my/2009/06/white-collar-crimes-accounted-for-rm788-mil-losseslast-year-igp/
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