Course: FSCT 7910Instructor: Joe IlseverStudents: Andrew Bakanauskas

Derek SoJan Whyte

Date: November 21, 2014

The transportation industry, by its modes - road, rail, marine, air, pipeline - has answered the demand for global freight and people movement on an exponential scale.

With the increase in business volumes, the transportation industry is also experiencing an increase in the magnitude of dollar losses due to fraud.

Globalization has had an interesting effect on the ability of multi-national companies to prevent, detect and recover from fraud losses.

The data analyzed is from various editions of the Association of Fraud Examiners “Report to Nations”.

The first edition of this report was in 1996. The next edition was not produced until 2002.

We requested the raw data from ACFE, were referred to another organization who is the gatekeeper of the ‘scrubbed’ data (names removed) and were denied access.

Andrew recreated the database from the ACFE reports into Excel - 2400 individual variables.

We also accessed data from the Central Intelligence Agency and United Nations websites.

The combined datasets contained variables from the following factor groups:

◦ A. Industry Organization

◦ B. Crime Scheme

◦ C. Geographical Region

◦ D. Behavioural Red Flags

◦ E. Trade Statistics

◦ F. Transportation Modal Infrastructure Statistics

The complexity of this data called for three hypotheses to be tested in order to answer the question “What factors drive global transportation fraud?”.

Hypothesis 1 H0: The trend in the number of cases of fraud did not have a relationship with time

(years);

HA: The trend in the number of cases of fraud does have a relationship with time (years).

Hypothesis 2 H0: The number of cases of global fraud in 2014 were not significantly

influenced by the sophistication of the transportation and economic factors in the region;

HA: The number of cases of global fraud in 2014 were significantly influenced by one or more of the transportation and economic factors in the region.

Hypothesis 3 H0: The rate of convictions in global fraud prosecutions has not increased over time;

HA: The rate of convictions in global fraud prosecutions has increased over time.

The analysis considered both Global and Geographical regions.

There was a limitation with respect to global data – the Certified Fraud Examiners Association just recently started tracking global fraud – only years 2008 to 2014 contained global data.

A longitudinal analysis with trendline was created from the ACFE reports.

Decline of fraud cases, over the span of 4 years.

2010-2012 report observes 25% fraud case decline.

2012-2014 report observes 1% fraud case decline.

This data does not imply that the total # of fraud cases is decreasing.

Graphical trend has been applied to geographical regions, to study case # vs year

United States region has the highest # of fraud cases reported over the years

Southern Asia region has the lowest # of fraud cases reported over the years

The overall trends in the regions have shown a decrease over time

Therefore, the null hypothesis was disproven; the number of fraud cases does have a relationship with time.

We proceeded to explore other variables to attempt to answer our question – what are the predictors of global transportation fraud?

◦ H0: The number of cases of global fraud in 2014 were not significantly influenced by the sophistication of the transportation and economic factors in the region;

HA: The number of cases of global fraud in 2014 were significantly influenced by one or more of the transportation and economic factors in the region.

Dependant Variable – Number of Fraud Cases Reported

Independent Variables

◦ Value of Exports

◦ Value of Imports

◦ Number of Airports

◦ Length of Rail

◦ Number of Ships

◦ Length of Road

We classified our countries under 3 groups

◦ 1 Developed, 2 Emerging, 3 Developing

Missing values replaced with 0

Removed all countries with 0 cases

Data did not appear normal so we applied a Log10 transformation

There was a deficiency in the data as approximately 50% of the countries in the global analysis did not contain data.

This appears to be because the ACFE is not tracking global fraud, but could also mean there is no legal infrastructure available to identify the elements of fraud as crimes in that region.

Discriminate Analysis showed that our groups were different.

Regression Analysis did not reveal the dependant variables were significant in explaining the number of fraud cases reported in emerging or undeveloped countries.

◦ Group 1 (Developed Countries) r^2 = .921

◦ Group 2 (Emerging Countries) r^2 = .671

◦ Group 3 (Undeveloped Countries) r^2 = 0.429

There are two options businesses have available to them in order to remedy the fraud losses: (1) prevention; or (2) detection and prosecution.

In exploring option #2, we hypothesized as follows:

◦ H0: The rate of convictions in global fraud prosecutions has not increased over time;

HA: The rate of convictions in global fraud prosecutions has increased over time.

0

10

20

30

40

50

60

70

80

2008 2010 2012 2014

Legal Trend Analysis

Plead Guilty/No Contest Convicted at Trial Private Settlement Log. (Convicted at Trial)

There is a very slight upward trend in the number of cases which resulted in a conviction at trial, however the population sample was flawed because there is not a strong representation of undeveloped or emerging countries included in the data for this variable.

The models created in this analysis are not reliable for the following reasons:

◦ The number of cases may be largely influenced by fraud examiners working in developed countries, resulting in under-representation of the population in a global context

◦ The country-specific transportation industry factors which are relevant to the volume of transportation did not explain the trends in number of fraud cases.

The longitudinal trend analysis confirmed the need for further exploration in three regions.

The literature confirmed the need for further exploration into fraud reduction methods in transportation.

We have the following recommendations:

Transportation companies should stop investing funds in prosecution/recovery through the criminal justice system.

Transportation companies should adopt or refer to a more robust, multi cultural information database.

The data base referred to (or created) must use country specific fraud indicators.