Uploaded by Shalini Velu


There are three stages in the money laundering which are placement (where the dirty
moneyintegrates into the financial system), layering (transfer funds between
variousoffshore/onshore banks) and then integration (purchase of luxury assets, financial
investmentetc to become legal money). Datuk Seri Najib has engaged into this world class
fraudactivity.1 Malaysia Development Berhad (1MDB) had become a hot topic discussed byeveryone
around the world. Malaysia has now become the biggest money laundering activityin the world.
1MDB is a state venture fund which Datuk Seri Najib propelled in 2009 soonafter getting to be prime
minister. It is a joint venture program to bring in foreign investmentdirect into Malaysia.Here we will
be discussing the banking transactions that involving the money launderingactivity. First of all,
during investigation RM2.973bil were transmitted to a person account
What happen to 1MDB
1Malaysia Development Berhad, or 1MDB, was founded in 2009 just four months after Najib
Razakbecame Prime Minister of Malaysia. Ensnared in the scandal, he later lost reelection and was
charged with abuse of power and criminal breach of trust in relation to SRC International, a former
1MDB unit. Najib pleaded not guilty charges and has consistently denied any wrongdoing in relation
to 1MDB.The fund was originally set up to finance infrastructure and other economy-linked deals in
Malaysia.But the fund veered into lavish spending, producing films such as “The Wolf of Wall Street”
and buying casinos, champagne and “Dustheads,” a painting by US artist Jean-Michel BasquiatAn
estimated $4.5 billion was misappropriated from 1MDB by high-level officials and their associates
between 2009 and 2014, the US Department of Justice has alleged. Razak has consistently denied
wrongdoing. The scandal spreads across a number of companies and financial institutions with eyewatering sums involvedIn 2012, officials from 1MDB met with Goldman Sachs in Hong Kong to
discuss a bond deal which would eventually lead to mega fees for the bank (and now, potentially,
mega fines). Goldman raised $6.5 billion for the fund. Malaysia’s government said it will seek “well in
excess” of the $2.7 billion it says was misappropriated, as well as the $600 million Goldman earned
in feesBetween 2012 and 2013, Goldman arranged three bonds worth $6.5 billion for 1MDB with
fees totalling $593 million, or 9% of the total, higher than the average fees paid on such deals,
according to critics.Malaysian prosecutors say that Goldman Sachs made untrue statements and
omitted key facts in offering circulars for the bonds it sold for Malaysian state fund 1MDb
Current situation for 1Malaysia Development Berhad1Malaysia Development Berhad (1MDB) is a
government’s ambitiousinvestment experiment which established by the Malaysia Government at
year 2009.As a strategic development company, 1MDB was established to drive strategicinitiatives
for long-term economic development for the country by forging globalpartnerships and promoting
foreign direct investment.However, the performance of 1MDB disappointed the Malaysia citizen. In
justover six years, 1MDB has declared borrowings of over RM 46 billion on the back ofan asset base
of about RM 51 billion, made up largely of power-generation assets andpotentially lucrative parcels
of real estate acquired from the government at steepdiscounts to the market value. In fact, Datuk
Seri Abdul Wahid admitted that 1MDB isa burden because it failure to generate cash flow against its
huge debts when 1MDBhad expanded by taking loans from banks and the capital market. It’s causing
theMalaysia department debt has increased to RM42 billion as of March 2014
How would you detect IMDB fraud?
The 1Malaysia Development Berhad scandal or 1MDB scandal is an ongoing political scandal
occurring in Malaysia. In 2015, Malaysia's then-Prime Minister Najib Razak was accused of
channelling over RM 2.67 billion (≈ US$700 million) from 1Malaysia Development Berhad (1MDB), a
government-run strategic development company, to his personal bank accounts.[1] The event
triggered widespread criticism among Malaysians, with many calling for Najib Razak's resignation –
including Dr. Mahathir Mohamad, one of Najib's predecessors as Prime Minister, who eventually
defeated Najib to return to power after the 2018 general election.
According to the records held by the companies commission, the company "has no business address
and no appointed auditor and according to its publicly filed accounts, 1MDB has nearly RM 42 billion
(US$11.73 billion) in debt. Some of this debt resulted from a $3 billion state-guaranteed 2013 bond
issue led by Goldman Sachs, who is believed to have made as much as $300 million in fees from that
deal alone, although it disputes this figure.[4] Conference of Rulers in Malaysia has called for the
investigations by the government to be completed as soon as possible, saying that the issue is
causing a crisis of confidence in Malaysia.
1Malaysia Development Berhad (1MDB), the Malaysian sovereign wealth fund described by thenU.S. Attorney General Jeff Sessions in December 2017, as “kleptocracy at its worst,” started off as a
“Malaysian strategic development company,” according to its website, which has since been shut
down. At the very core of the 1MDB scandal was the “less than satisfactory corporate governance
and internal controls,” as reported by the former Malaysian Auditor General Ambrin Buang in the
1MDB audit report.
How would you detect IMDB fraud?
In my view I would propose to detect 1MDB Fraud based on following step.
First using benford Law
Benford's law, also called the Newcomb–Benford law, the law of anomalous numbers, or the
first-digit law, is an observation about the frequency distribution of leading digits in many
real-life sets of numerical data. The law states that in many naturally occurring collections of
numbers, the leading significant digit is likely to be small. For example, in sets that obey the
law, the number 1 appears as the leading significant digit about 30% of the time, while 9
appears as the leading significant digit less than 5% of the time. If the digits were distributed
uniformly, they would each occur about 11.1% of the time. Benford's law also makes
predictions about the distribution of second digits, third digits, digit combinations, and so on.
In the case of 1MBD I will propose use Benford law where the financial statement of
1MBD where there first step is to extract the first digit of each
This statistical method is used as an “anti-doping test” inside the process of fraud detection
and the analysis of accounting data. Indeed, we need to use analytical procedures to identify
the presence of transactions, events or unusual trends, and to make assumptions based on a
first-digit distribution of data. Benford’s law provides expected schemes of the digits in
numerical data and is recommended as a test for the authenticity and the trustworthiness of
transaction’s accounting data in 1MBD, besides having described how Benford’s model
works, provided a second-order test that determines the digit frequencies of the differences
between the ranked values in a data set. A further, second-order test has been applied to 4
groups of transactional data, and it detected errors in data downloads, rounded data, data
generated by statistical procedures, and the inaccurate ordering of data. In order to detect
fraud such as 1MDB I would suggested that the law could be used to detect possible fraud in
lists of socio-economic data submitted in support of public planning decisions. Based on the
plausible assumption that people who fabricate figures tend to distribute their digits fairly
uniformly, a simple comparison of first-digit frequency distribution from the data with the
expected distribution according to Benford's Law ought to show up any anomalous results.
Benford's Law could be used in forensic accounting and auditing as an indicator of
accounting and expenses fraud. In practice, applications of Benford's Law for fraud
detection routinely use more than the first digit.In the 2016 movie The Accountant, Ben
Affleck's character uses Benford's Law to expose the theft of funds from a robotics company.
Besides that Altman’s z-score model Another statistical method commonly applied to
accounting data, and in fraud detection which is the result of a company’s solvency test
measured on its probability of failure. It uses profitability, leverage, liquidity, solvency, and
activity to predict whether a company has a high probability of becoming insolvent.
Altman’s indicator is built on five financial indicators that are obtainable from 1MBD’s
financial statement:
X1 = Working Capital/Total Asset;
X2 = Retained Earnings/Total Asset;
X3 = EBIT/Total Asset;
X4 = Market Value of Equity/Total Liabilities;
X5 = Sales/Total Asset.
z-score model is then calculated by the formula:
Z-SCORE = 1.2X1+ 1.4X2 + 3.3X3 + 0.6X4 + 0.999X5.
Depending on results, it could be established if a company is subject, more or less, to insolvency risk
and then to bankruptcy risk, or not. When zscore is above 2.99, the company is in a “safe zone”;
rather, when the result is under 1.81, the company is in a “distress zone”, that is considered as a
dangerous area. Finally, when z-score gives a value between 2.99 and 1.81 that is a grey zone where
results are not certain, and they should be vetted with more analysis.
Beneish’s M-score
M-score model, along with Altman’s z-score and Benford’s law, is a valid tool in fraud
detection. This mathematical method, consists in eight variables that identify financial fraud
events or the tendency of a company to manipulate accounting data. The variables are
represented by actual data contained inside the balance sheet, and once put together they
prove the earning management rate.
The M-score variables are: SGI (Sales Growth Index), GMI (Gross Margin Index), AQI
(Assets Quality Index), DSRI (Days' Sales in Receivables Index), SGAI (Sales, General &
Administrative Expenses Index), DEPI (Depreciation Index), LVGI (Leverage Index) and
TATA (Total Accrual to Total Assets). Once the financial indicators are calculated, the next
step is using them inside the linear regression:
M-SCORE = -4.84 + 0.92 DSRI + 0.528 GMI + 0.404 AQI + 0.892 SGI + 0.115 DEPI –
0.172 SGAI + 4.679 =TATA – 0.327 LVGI
For values under -2.22, the company do not have manipulated accounting data; whereas,
when the m-score is over -2.22 it highlights that, likely, the company could have used earning
management practices. Beneish’s indicator is very useful to identify who manipulated data
and the areas where they operated earning management policies. Therefore, statistical
methods such as Benford’s law, Altman’s z-score and Beneish’s Mscore revealed themselves
to be useful and valid inside the process of fraud detection of 1MBD. Professional figures
involved in fraud detection, use other tools beyond the analytical ones.
The term “whistleblowing” has been coined by Ralph Nader in the 1970s during his
investigative activities. The whistleblower existed, in the corporate world, long before Nader
give him the name, however, more negative terms were used, for example, “snitch” or
“informant”. Cambridge Dictionary defines whistleblower as “a person who tells someone in
authority about something illegal that is happening, especially in a government department or
a company”. Furthermore, usually in most circumstances, the Hermanson capability plays a
fundamental role in the fraud, because it allows the fraudster the opportunity to commit fraud
without the risk of getting caught. Among the elements that characterise most capability, we
have intelligence, ego, top position inside a company, ability to lie and to manage stress.
Fraud triangle
The last method used to detect accounting fraud, and fraud in general, is the fraud triangle.
Fraud triangle theory has been hypothesized for the first time by Cressey (1953), and then it
has been resumed by Albrecht (1984). This theory sees fraud as a combination of three
elements: opportunity, pressure, and rationalizati on.
The first component is pressure. Even though we are not all predisposed to commit crimes,
once we face a need or the right pressure (financial, environmental or personal) we could be
able to perpetrate a fraud. Along with pressure, there is an opportunity, linked with personal
capabilities, and rationalization too, that is the most important because it allows the fraudster
to blame the system or someone else for his actions. With the use of the fraud triangle inside
the process of detection, it is possible to identify those red flags that, once put together, could
show fraudulent behaviours
Fraud analytics
Sampling is mandatory for certain processes of fraud detection. Sampling will be
more effective where there a lot of data population involved.
Repetitive or Continuous Analysis
Repetitive or Competitive Analysis means creating and setting up scripts to run
against big volume of data to identify the frauds as they occur over a period of time.
Run the script every day to go through all the transactions and get periodic
notification regarding the frauds. This method can help in improving the overall
efficiency and consistency of fraud detection processes.
Analytics Techniques
Analytic techniques help you to find out frauds that are not normal
Calculate Statistical parameters to find out values that exceed averages of standard
deviation. Look at high and low values and find out the anomalies there. Such
anomalies are often the indicators of fraud
Fraud Detection Predictive Analytics for big data
Predictive analytics uses text analytics and sentiment analysis to look at big data for
fraud detection. Predictive analysis has been widely used by a lot of organizations as
it helps in proactively detecting frauds. In the beginning Predictive analysis was used
to analyze statistical information stored in the structured databases but now it is
extended to the big data realm.
Audit Command Language (ACL)
Powerful program for data analysis
Most widely used by auditors worldwide
CaseWare’s IDEA
Powerful program for data analysis with more Windows-like user interface
Accounting anomalies
Internal control weaknesses
Analytical anomalies
Extravagant lifestyle
Unusual behavior
Tips and complaints