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The B.E. Journal of EconomicAnalysis & Policy
AdvancesVolume 13, Issue 3 2012 Article 3
FORENSIC ECONOMICS
Identifying Terrorists using Banking Data
Steven D. Levitt∗
∗University of Chicago Department of Economics, [email protected]
Recommended CitationSteven D. Levitt (2012) “Identifying Terrorists using Banking Data,” The B.E. Journal of Eco-nomic Analysis & Policy: Vol. 13: Iss. 3 (Advances), Article 3.DOI: 10.1515/1935-1682.3282
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Identifying Terrorists using Banking Data∗
Steven D. Levitt
Abstract
The fight against terrorism requires identifying potential terrorists before they have the op-portunity to act. In this paper, we investigate the extent to which retail banking data – which asfar as we know are not currently used by anti-terror intelligence agencies in any systematic man-ner – are a useful tool in identifying terrorists. Using detailed administrative records of a largeBritish bank, we demonstrate that a number of variables in the data are strongly correlated withterrorism-related activities. Having both an Islamic given name and surname, not surprisingly, areamong the strongest of these predictors, but a wide range of other demographic characteristics andbehaviors observed in the data are also correlated strongly with terrorist involvement. The real keyto our method, however, rests on the identification of one particular pattern of banking behavior(what we call “Variable Z”) which dramatically improves our ability to identify terrorists. Ourmodel is demonstrated to have substantial power to identify terrorists both within sample and outof sample.
KEYWORDS: terrorism, forensic economics
∗We would like to thank Gary Becker, Stephen Dubner, John List, Kevin Murphy, and Chad Syver-son for helpful discussions on this topic, as well as numerous individuals who are employed in theanti-terror intelligence effort and by the bank which provide the data for the analysis. Lint Bar-rage, Adam Castor, Dana Chandler, Steve Cicala, Marina Niessner, and Dhiren Patki providedoutstanding research assistance. Correspondence should be addressed to Steven Levitt, Depart-ment of Economics, University of Chicago, 1126 E. 59th street, Chicago, IL 60637. The secondauthor, an employee of the bank, writes under a pseudonym.
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SECTION I. INTRODUCTION
Nearly 3,000 people died as a result of terrorist acts carried out on September 11,
2001. The full costs of terrorism, however, stretch far beyond the pain and
suffering of the direct victims of a particular attack. Terrorism induces fear and
disutility among the broader population (Becker, 2004). One manifestation of this
fear is behavioral distortions that are far more extreme than might appear to be
warranted based upon the actual risk of being a victim of a terror attack. These
behavioral distortions are accompanied by additional costs. The substitution away
from air travel, which is much safer than traveling by automobile, contributed to a
spike in motor vehicle fatalities after the September 11th
terrorism (Blalock,
2009). On a much grander scale, terrorist activities were one of the primary
motivations for initiating the wars in Afghanistan and Iraq, during which it is
estimated as many as 873,000 civilians1 and 6,598 American soldiers
2 have died.
Even terrorist attempts that fail can impose large costs. Eight years after Richard
Reid’s bungled attempt to detonate a shoe bomb on a transatlantic flight, airline
travelers continue to be required to remove their shoes during security screening.3
Preventing terrorism is a difficult task because there is almost no limit to
the variety of potential terrorist attacks. While the September 11th
terrorism was a
large, well-coordinated, and well-funded effort, much simpler schemes have also
proven effective. During the three week period when the “Washington snipers”
were shooting innocent victims largely at random, economic and other activities
in the area were sharply curtailed as a consequence of the actions of a man, a
child, and a rifle. Because there are so many potential targets, focusing efforts on
safeguarding these targets is a difficult and costly endeavor.
An alternative approach to terrorism focuses on identifying individuals
likely to engage in terror acts. Anti-terror efforts of this kind are built on three
sources of information: human informants, surveillance of communications via
phone, email, or internet, and following the international money trail. In this
paper, we explore a fourth potential source of information that, up until now, has
not been used extensively in the fight against terrorism:4 the data generated by
daily retail banking transactions. Specifically, we combine depersonalized
1 Estimates of civilian casualties come from the Casualties in Afghanistan & Iraq project of
www.unknownnews.org as of August 10th
2010. 2 Estimates of U.S. service member deaths since the beginning of the Afghanistan and Iraqi
military operations come from the Faces of the Fallen project of the Washington Post as of
October 31, 2012 (http://apps.washingtonpost.com/national/fallen/). 3 Over 500 million travelers pass through security in American airports each year. If each of them
spent just one minute removing their shoes and putting them back on since the policy was
instituted, this additional step has absorbed roughly 8,000 person-years of traveler time. 4 This assertion is based on private conversations with numerous leading figures in the intelligence
community engaged in fighting terrorism.
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administrative data from a large British bank with a host of other publicly
available data sources to investigate whether personal banking data can be used as
a tool for identifying terrorists. These data include demographic characteristics of
the customers, account characteristics, and banking transactions.
Over the period from October 2007 until February 2009, more than 100 of
the bank’s millions of customers were arrested or investigated by law
enforcement for suspected terrorist activity.5 We denote these individuals
“positives.” In the statistical analysis, we attempt to identify similarities in traits
and patterns of behavior among these positives that distinguish these individuals
from the rest of the sample. Perhaps unsurprisingly, the single best demographic
predictors in the data set are having Islamic first and last names. This
characteristic alone increases the likelihood of being a positive fifty-fold.
Positives also tend to be young and male. A number of behavioral characteristics
also prove useful in identifying positives: living in close proximity to mosques,
types of financial products used, whether the person owns their place of residence,
and the fraction of transactions made during traditional Muslim prayer hours on
Fridays. In addition to these variables, there is one further behavioral indicator
which proves to be extremely powerful in predicting positives – so powerful that
the cooperating bank has asked us not to disclose the precise nature of the
measure in the interest of national security. We refer to this variable henceforth as
“Variable Z.”6 Without revealing too much about Variable Z, we can say that the
idea for it came out of economic theory, and the select individuals who have been
told the nature of Variable Z immediately recognized why it would be such an
effective predictor. On the other hand, we have had many people attempt to guess
the identity of Variable Z, and none of those guesses has been remotely close to
accurate.
The model estimated in this paper has substantial power to identify
positives in the data.7 The overall prevalence of positives in the bank’s customer
pool is .00073 percent, or roughly 1 in 140,000. Among the actual set of positives,
the estimated predicted likelihood of being positive is approximately 700 times
higher than for the population as a whole.8 For roughly six percent of the
5 These arrests were made prior to our study and were not based on our analyses.
6 Indeed, maintaining the confidentiality of Variable Z is consistent with prior academic research
on fraud detection which, for obvious reasons, has paid more attention to analytical tools than to
specific methods of detection (Bolton and Hand 2002). 7 The model we develop targets a very specific type of terrorist; clearly in another time and place,
a different model would be necessary, although we suspect that the same principles would be at
work. 8 In making these predictions for a particular positive, we estimate the model using all of the data
except the information for that one individual, and then fit the model that excludes that individual
to that person’s data. This avoids the obvious bias that arises if one fits the model using this
individual’s data and then makes predictions based on that fit.
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positives, the predicted likelihood of their being a terrorist is estimated to be
greater than .8 percent; among the overall bank population, only one in 65,000
people cross that threshold.
The model also generates strong predictions regarding particular
individuals who have not been identified by authorities as positives, but appear to
have sets of characteristics that make them likely to be positives. Based on data
from October 2007 –February 2009, we identified 90 bank customers whom we
viewed as serious terrorist risks. Over the next 14 months, 0.00054 percent of the
bank’s customers were arrested on terrorist related charges9. Two of these arrests
were of individuals identified on our 90 person watch list. The likelihood of this
occurring by chance is vanishingly small.10
The analysis in this paper contributes to two separate economic literatures.
The first of these is what some have dubbed “forensic economics,” (Zitzewitz,
2012) in which economists use subtle data patterns to ferret out evidence of
cheating and corruption in settings as varied as tax evasion (Fisman and Wei,
2004), illegal weapons dealing (DellaVigna and Le Ferarra, 2007), procurement
(e.g. Di Tella and Schargrodsky, 2003; Olken, 2007), corruption (Fisman, 2001;
Olken, 2009), employee sabotage (Krueger and Mas, 2004), cheating on
standardized tests (Jacob and Levitt, 2003), and sports (Duggan and Levitt, 2002,
Zitzewitz, 2006).
Terrorism poses unique challenges not present in these earlier forensic
applications. First, most prior examples of forensic economics have centered on
detecting cheating or fraud in a narrowly defined context (e.g. cheating on
standardized tests, collusive bidding, the building of roads, etc.) after the fact.
The goal of this analysis is different: to identify potential terrorists before they
have actually carried out the terrorist attack. A second difference between this
application and prior research is that in the settings studied previously, the
prevalence of cheating was generally high among the target population, whereas
terrorists represent an extremely small share of the population. Reliable estimates
of the number of terrorists are difficult to obtain, but given the near absence of
terrorism on United States soil since the attacks that took place on September 11,
2001, it would not seem unreasonable to argue that the prevalence of terrorists in
the United States is less than one per one million residents. In the United
Kingdom, the target of this analysis, terrorist acts and arrests of suspected
terrorists have been more frequent in recent years, suggesting the prevalence of
terrorists is likely to be greater. According to Jonathan Evans, the Director-
9 In order to protect anonymity of the bank, we report only the percentage and not the exact
number. 10
Upon discovery that our methods appeared efficacious, the cooperating bank provided the list
of names to the appropriate authorities. As of this writing, we have received no information
regarding the value of the list in the war on terror.
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General of MI-5, there were 4,000 operating terrorists as of November 2007
which implies roughly 65 terrorists per million residents of the UK.11
The second literature with which this paper connects is the growing body
of research focused directly on terrorism and its effects. The work that is most
similar in spirit is Krueger (2007), which addresses many terrorism related issues,
including the fact that terrorists are disproportionately drawn from amongst the
relatively well educated. Krueger (2007) identifies characteristics observed
among terrorists, but unlike the current work, does not attempt to extend his
analysis to predicting which individuals are likely to be terrorists. Pape (2005),
based on an analysis of an exhaustive database of suicide bombing and terrorist
attacks between 1980 and 2003, concludes that foreign occupation is the strongest
predictor of terrorism. A number of papers have tried to measure the costs
associated with terror. Becker and Rubinstein (2004) focus on the fear-related
costs of terrorism. Other research has tried to measure its impact on economic
activity (Abadie and Gardeazabal, 2008; Blomberg, Hess, and Orphanides, 2004;
Eckstein and Tsiddon, 2003; Zussman and Zussman, 2006).
The remainder of this paper is structured as follows. Section II describes
the data used in the analysis. Section III presents the statistical model and the
results. Section IV concludes.
II: DATA DESCRIPTION
Three main sources of data are used in this analysis. The first source of data
comes from the depersonalized administrative records of a large bank in the
United Kingdom. Because of privacy concerns, only data with all personal
identifiers removed, (including names, account numbers, and addresses) have
been made available to members of this project who are not bank employees. As a
further privacy safeguard, the bank provided only coarse data on variables such as
age and when the account was opened. In addition to demographic and account
data, there are also records corresponding to individual banking transactions, such
as debit card purchases. As with the demographic information, the transactions
data provided to researchers outside the bank have been veiled in a manner that
fully protects the privacy of bank customers.
The second data source used in the analysis is a list of people who have
been arrested or investigated on terrorist charges in the United Kingdom. This list
of names was constructed primarily based on information provided at the website
www.salaam.co.uk, which has a database of arrests on terrorism related charges.
Information from this list was supplemented with data from other newspaper
11
Rise in number of terrorists.05 November 2007. Manchester Evening News. Retrieved: 26 April
2010 (http://www.manchestereveningnews.co.uk/news/special_reports/editors/s/
1022830_rise_in_number_of_terrorists)
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reports of terrorism arrests, as well as customer accounts whose banking records
had been requested by law enforcement in anti-terror investigations. Of the names
gathered from these various sources, 112 were determined to be bank customers.12
The third source of data compiled for the paper is a list of predominantly
Islamic names, which was constructed using a variety of public sources including
telephone books from Islamic nations and baby naming books. Because of the
great variety in names, a large number of less common names are missing from
the list, introducing noise into our name-based classifications.
CHOICE OF SAMPLE
With millions of customers, the bank’s activities produce an extraordinary amount
of data. Consequently, the bank keeps data in active storage for only 14 months at
a time. After that period, the data are archived. We were not able to obtain access
to any archived data for this study.
Because of the massive scale of the data, our main analysis is limited to a
small subset of the bank’s customers that includes the 112 positives as well as a
random sample of roughly 19,000 individuals that over-weights people with given
names or surnames that appear on the list of Islamic names described above. The
sampling rate of individuals with no Islamic names is roughly a one in 2,000. For
those with exactly one name identified as Islamic, the sampling rate is about one
in 100. People with both names on the Islamic list are sampled at a one in 35 rate.
Ultimately, we have between 6,000 and 7,000 individuals in the sample in each of
the three categories. Additionally, after the importance of Variable Z became
apparent, a separate extract of 294 customers with high values for Variable Z was
carried out. This last extract is not used in estimation of the model, but rather,
only to identify those non-positive customers with the greatest likelihood of being
involved in future terrorist activities. The high Variable Z sample, like the
positives, includes all individuals who fit that category.
Table 1 presents summary statistics for our six different groups: the bank’s
overall population of customers, the positives, the three random samples stratified
by Islamic name status, and the sample of non-positive individuals who have high
values of Variable Z. Fewer than 1 in 100,000 of the bank’s customers are
positives. We explicitly exclude positives from the other samples, although given
their rarity in the data, very few positives would be expected in the sample sizes
we use. The three variables included under the heading Islamic name are mutually
12
Since the bank provided us with depersonalized data, we are unable to ascertain what fraction of
the 112 arrestees was convicted on terrorism charges. However, data from the UK Home Office
reveal that between 2001 and 2012, 23 percent of the cumulative 2174 individuals arrested under
anti-terror laws have been convicted (http://www.homeoffice.gov.uk/publications/science-
research-statistics/research-statistics/counter-terrorism-statistics/hosb1112/).
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exclusive indicator variables corresponding to whether both the given name and
surname are identified as Islamic, neither the given name nor the surname of the
individual is identified as Islamic, or only the given name or surname is identified
as Islamic. Those with Islamic names are overrepresented in the list of positives.
Whereas only 1.3 percent of the bank’s customers have two Islamic names, 72.3
percent of the positives fall into that category. The share of positives with one
Islamic name is almost three times higher than the corresponding share in the
overall customer pool.
The next row in the table corresponds to Variable Z, which is a continuous
variable with a weight of mass near zero and a long right-tail. For the banking
population as a whole, the mean of Variable Z is very close to zero. Among the
positives, the mean of Variable Z is near 20. Those with two Islamic names have
a mean value of Variable Z that is much higher than those with no Islamic name,
but still over 70 times lower than that of positives. Inclusion in the non-randomly
drawn sample in the final column is predicated on having a high value for
Variable Z, and that fact is reflected in the high mean for that variable in the last
column: 306.
The next row in the table corresponds to gender. The bank’s overall pool
of customers is split nearly evenly by gender. In contrast, more than three-quarters
of the positives are male. Males are also overrepresented in the Islamic name
samples, most likely because our algorithm for identifying Islamic names works
better for males. Age reflects a series of indicator variables corresponding to
whether the individual falls into various age windows. Almost two-thirds of the
bank customers are over the age of 45, whereas less than 10 percent of the
positives fall into this category. Half of the positives are between the ages of 26
and 35. Those with Islamic names and the sample of high Variable Z individuals
are also younger on average than the bank’s average customer. The next three sets
of indicators correspond to marital, employment, and residential status. These
variables are captured at the time that a customer signs up with the bank and are
only sporadically updated over time, limiting their usefulness. The next set of
variables reflect patterns of ATM usage: the average value of ATM withdrawal,
and indicator variables for the percentage of ATM withdrawals made during the
nighttime, between 8pm and 6am, or during periods that coincide with Muslim
prayers on Friday. Positives are more likely to make late-night withdrawals and
much less likely to make withdrawals during Friday prayers (although,
interestingly, among those with Islamic names, withdrawals in this these time
windows are not that much lower than for the overall customer base).
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Table 1: Summary Statistics (Means)
Islamic Name
Overall
bank
customers
Positives
Both
names
Muslim
One
Muslim
Name
No
Muslim
Names
High
Variable
Z
sample
(1) (2) (3) (4) (5) (6)
N 18,929 112 5,802 6,210 6,805 294
Positive 0.0007% 100.0%
Muslim Names: None 93.5% 13.4%
100.0% 54.4%
Muslim Names: First name only 2.4% 8.0%
45.8% 3.1%
Muslim Names: Last name only 2.8% 6.3%
54.2% 13.9%
Muslim Names: Both 1.3% 72.3% 100.0%
28.6%
Variable Z -0.039 20.339 0.274 0.197 -0.057 305.658
Gender and Age
Male 49.1% 78.6% 65.8% 56.2% 48.5% 55.8%
Age: Under 16 0.1% 0.0% 0.0% 0.3% 0.1% 0.0%
Age: 16 to 25 4.6% 21.4% 8.1% 15.9% 3.9% 18.0%
Age: 26 to 35 8.3% 50.0% 18.3% 21.8% 7.5% 28.6%
Age: 36 to 45 19.4% 18.8% 25.3% 19.5% 19.3% 25.5%
Age: Over 45 67.4% 9.8% 48.2% 42.3% 69.1% 27.6%
Age: Unknown 0.1% 0.0% 0.1% 0.1% 0.1% 0.3%
Marital Status
Single 20.8% 50.9% 22.8% 31.8% 20.1% 38.8%
Married 55.4% 37.5% 62.6% 40.2% 56.1% 44.6%
Other (Widowed, divorced, etc.) 10.2% 1.8% 4.9% 7.0% 10.4% 5.4%
Unknown 13.7% 9.8% 9.7% 21.0% 13.4% 11.2%
Employment Status
Employed 47.8% 57.1% 46.1% 52.7% 47.6% 61.9%
Self-Employed 4.3% 5.4% 8.7% 6.2% 4.1% 3.4%
Retired 19.3% 0.0% 10.6% 9.5% 20.0% 3.7%
Unemployed 1.7% 6.3% 3.7% 3.3% 1.6% 4.8%
Full-time Student 2.4% 13.4% 5.3% 8.9% 2.0% 4.4%
Housewife 3.5% 4.5% 10.0% 4.9% 3.3% 12.2%
Unknown 21.0% 13.4% 15.7% 14.5% 21.4% 9.5%
Residential status
Owner 55.2% 11.6% 51.6% 35.6% 56.4% 21.1%
Renter 17.7% 45.5% 19.4% 28.1% 17.1% 42.5%
With parents 7.0% 22.3% 14.4% 15.2% 6.5% 18.0%
Other 3.4% 5.4% 4.3% 4.4% 3.3% 6.5%
Unknown 16.6% 15.2% 10.4% 16.7% 16.7% 11.9%
Proximity to Mosque 10.4% 32.1% 22.0% 16.0% 9.9% 24.8%
Notes: Column 1 shows weighted averages for the overall bank customers who were randomly sampled at
different rates depending on their first and last names. Column 2 shows data for the 112 positives we
identified. Columns 3 through 5 separate out our random sample by name status and Column 6 shows only
the sample that we specially selected because they were high on Variable Z
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Table 1 (continued): Summary Statistics (Means) Islamic Name
Overall
bank
customers
Positives
Both
names
Muslim
One
Muslim
Name
No
Muslim
Names
High
Variable
Z
sample
(1) (2) (3) (4) (5) (6)
ATM Usage
Average withdrawal amount (£) 84 97 98 77 84 73
% of Withdrawals during Nighttime
(8pm-6am) 5.2% 15.6% 8.6% 8.6% 4.9% 8.3%
% of Withdrawals during Friday
prayer (10am-12pm) 4.2% 1.9% 2.8% 3.3% 4.3% 3.1%
% of Withdrawals during Friday
prayer (12-1pm) 2.0% 1.2% 1.6% 1.8% 2.0% 1.7%
% of Withdrawals during Friday
prayer (1-3pm) 3.7% 3.0% 3.3% 3.6% 3.7% 3.1%
Types of financial products
Business customer 2.5% 2.7% 2.3% 5.7% 2.3% 2.7%
Debit/credit cards 56.6% 52.7% 55.4% 45.1% 57.3% 49.0%
Loans (excluding Mortgages) 43.0% 19.6% 30.1% 32.4% 43.8% 26.5%
Mortgages 9.9% 0.9% 6.5% 7.9% 10.1% 5.8%
Life Insurance 41.0% 25.9% 34.5% 31.6% 41.6% 39.8%
Savings products 65.3% 25.9% 51.6% 54.0% 66.1% 50.3%
Extras 0.9% 0.9% 0.3% 0.9% 0.9% 0.7%
Longterm 47.0% 7.1% 30.6% 29.1% 48.2% 20.7%
Notes: Column 1 shows weighted averages for the overall bank customers who were randomly sampled at
different rates depending on their first and last names. Column 2 shows data for the 112 positives we
identified. Columns 3 through 5 separate out our random sample by name status and Column 6 shows only
the sample that we specially selected because they were high on Variable Z
Proximity to mosque is an indicator variable that takes on a value of one if
the customer’s postal code is within one mile of a registered mosque. 10 percent
of all customers fall into this category. 22 percent of those with two Islamic
names live near a mosque; nearly one-third of the positives do.13
The remaining
rows are indicators for the types of products associated with the customer’s
accounts. These categories are not mutually exclusive. The precise definitions of
these variables are presented in the data appendix. Positives are less likely to use a
number of the bank’s services, including loans, mortgages, savings accounts, and
“long-term” products such as life insurance.
13
Other than proximity to a mosque, no other geographic identifiers (e.g. region of the country)
were made available for the analysis.
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Table 2: Distribution of Variable Z
Islamic Name
Overall
bank
customers
Positives
Both
names
Muslim
One
Muslim
Name
No
Muslim
Names
High
Variable
Z sample
(1) (2) (3) (4) (5) (6)
% Positive on Variable Z 0.24% 8.93% 0.41% 0.14% 0.04% 100.00%
Distribution of Variable Z (when Variable Z > 0)
10th percentile 52.5 113.7 51.6 37.6 93.2 72.3
25th percentile 71.8 150.6 56.5 74.8 93.2 132.1
Median 109.0 169.8 75.1 190.7 165.4 209.7
75th percentile 190.7 229.1 127.9 318.2 195.1 339.1
90th Percentile 318.2 509.1 182.9 480.2 195.1 474.3
Mean 155.2 234.5 102.8 208.0 151.2 305.7
Notes: The first row illustrates the percentage of customers in each of our groups who have positive values of
Variable Z. In the bottom portion of the table, we include only those people who have positives values of
Variable Z and show those values at different points in the distribution. The last row shows the mean value of
Variable Z conditional on the customer having a positive value of Variable Z.
Because Variable Z plays such a critical role in the analysis, Table 2
provides greater detail on the distribution of Variable Z in the various samples.
The top row of the table shows the share of individuals with non-zero values for
Variable Z. The subsequent rows present the value of Variable Z at various
points in the distribution, conditional on having a non-zero value. Only 0.24
percent of banking customers have a positive value for Variable Z, compared to
8.93 percent of the positives. 0.41 percent of those with two Islamic names have a
non-zero value for Variable Z. Conditional on having a positive value for
Variable Z, there are no strong patterns of difference across the positives and the
randomly drawn samples.
SECTION III: ESTIMATION APPROACH AND RESULTS
We estimate probit models in which the dependent variable is an indicator
variable equal to one if the customer has been identified as a suspected terrorist by
the police (i.e. is a “positive”), and otherwise is equal to zero.14
Included on the
right-hand-side of the equation are all of the variables described in the summary
statistics above. The results are presented in Table 3. Column 1 pools all of the
14
Here, as elsewhere in the paper, we exclude the special extract of high variable Z individuals,
since it was not randomly drawn, but rather constructed ex post to allow identification of the most
suspicious customers.
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data. Columns 2 and 3 divide the sample by Islamic name status. Column 2 is
restricted to those with both Islamic given names and surnames and column 3
limits the sample to those with only one Islamic given name or surname or those
with no Islamic names. In all columns, the estimates are probability weighted to
make the data set representative of the bank’s overall customer pool (column 1),
or subsets of the data by Islamic name status (columns 2 and 3). The values
reported in the table are the Z-statistics for each variable from the probit
estimation. Because the coefficients have no easy interpretation with respect to
magnitude, the discussion of Table 3 focuses on statistical significance; in Table 4
we present results that speak more directly to the magnitude of the impact of the
key explanatory variables.15
For those sets of variables which are mutually
exclusive and exhaustive indicators (e.g. housing status, age, etc.) the omitted
category is identified in the table notes.
Column 1 presents the pooled results. As would be expected given the
summary statistics presented earlier, having two Islamic names enters strongly
positive with a z-stat over 18. Having either a first or last name identified as
Islamic is also associated with an increased likelihood of being a positive, but to a
much lesser extent. Variable Z enters with a positive sign and a z-stat over five.
Only a handful of the other covariates achieve statistical significance in column 1.
One of these is being older than 45 years of age (relative to the omitted category
of age 25 or less), which enters negatively. Being a renter or having an unknown
housing status (relative to being a home owner) enters positively. Living close to
a mosque also carries point estimates that are positive and significant. Having a
savings account and the fraction of a customer’s ATM transactions that occurred
during Friday prayers enter negatively and with significance. Note that a number
of variables which were highly correlated with being positive in the raw data (e.g.
being male, having “long-term” products with the bank), are not statistically
significant after controlling for other factors.
15
Note that we do not follow the more common practice of reporting the implied marginal effects
of the probit estimates evaluated at the sample mean in Table 3. Because the fraction of positives
is so small in our data set, evaluating marginal effects at the mean, or even in the 90th
percentile of
the distribution proves not to be particularly informative.
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Table 3: Z-scores for Probit Estimation
Overall
bank
customers
Two
Muslim
Names
One or No
Muslim
Names
(1) (2) (3)
Muslim Names: First name only 4.44***
4.68***
Muslim Names: Last name only 2.07*
3.00***
Muslim Names: Both 18.09***
Variable Z 6.50*** 5.79*** 4.47***
Gender and Age
Male 2.20* 2.69** 0.79
Age: 26 to 35 0.78 2.19* -2.06*
Age: 36 to 45 -2.25* -0.65 -2.79**
Age: Over 45 -4.50*** -3.33*** -3.50***
Employment Status
Self-Employed -0.43 -0.55 0.24
Unemployed 1.52 1.89 0.19
Full-time Student 0.02 1.18 -1.16
Homemaker -0.28 -0.12 1.01
Marital Status
Single 1.64 0.87 0.95
Married 1.65 0.63 1.87
Residential status
Renter 3.91*** 3.54** 1.35
With parents 2.12* 1.87 1.44
Other 1.96* 1.45 1.40
Unknown 4.57*** 3.75*** 2.42*
Proximity to mosque 2.51* 0.92 3.43***
ATM Usage
Average withdrawal amount 2.56* 1.51 2.44*
Withdrew during Nighttime (8pm-6am) 2.74** 2.23* 2.01*
Withdrew during Friday prayer (10am-12pm) -1.50 0.34 -2.04*
Withdrew during Friday prayer (12-1pm) -0.81 -1.00 -0.07
Withdrew during Friday prayer (1-3pm) -1.44 -0.02 -1.80
Types of financial products
Business customer 0.89 -0.63 1.50
Debit/credit cards 2.33* 2.03* 1.37
Loans (excluding Mortgages) 0.69 0.18 0.80
Mortgages -1.02 -0.28 -
Life Insurance -0.84 -1.46 0.07
Savings products -4.05*** -3.42*** -2.18*
Extras 1.36 1.99* -
Longterm -1.94 -0.66 -2.05*
Number of observations 18,929 5,883 13,046
Notes: Column 1 shows estimated z-scores for each variable for our entire sample of randomly sampled
customers and positives. Because the high Variable Z customers were non-randomly selected, we cannot use
them for estimation. Column 2 estimates a separate model for customers with two Islamic names. Column 3
estimates the model for customers with zero or one Islamic names. Significance levels are indicated by * p <
.050, ** p < .010, *** p < .001. Omitted categories include: females under 25 (for age and gender), those
who are employed (as well as a small number of other miscellaneous employment statuses such as unknown,
no response, other or retired), persons divorced or separated (or with unknown/no responses), and
homeowners.
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Because having two Islamic names is so strongly correlated with the
dependent variable, columns 2 and 3 divide the sample according to whether the
customer has two Islamic names.16
Column 2 presents the results for those with
two Islamic names. With one exception, the variables that were statistically
significant in the pooled regression continue to be significant and enter with the
same sign in column 2. The lone exception is the variable measuring proximity to
a mosque, which remains positive, but is no longer statistically significant. A few
additional variables gain statistical significance when the sample is restricted to
those with two Islamic names: being male is associated with a higher rate of being
positive, as is having opened an account within the past two years, being
unemployed, and having “extras” associated with the account.
The estimates in column 3, which excludes those with two Islamic names,
generally paint a similar picture to those of column 1 which includes the whole
sample. One difference is that the coefficients on 26-35 and 36-45 year olds take
on statistically significant negative coefficients roughly the same magnitude as
those for customers over the age of 45. Among the population that does not have
two Islamic names, it is those under the age of 25 who are most heavily
represented among the positives.
DOES THE MODEL HAVE SUFFICIENT EXPLANATORY POWER TO BE OF USE IN
PROSPECTIVELY IDENTIFYING POSSIBLE TERRORISTS?
One goal of this analysis is to provide an additional tool for anti-terror law
enforcement efforts. A necessary condition in that endeavor is that the model
generates sufficiently strong predictions in the right-hand tail to warrant allocating
investigative resources towards those deemed suspicious.17
Table 4, which
presents the distribution of fitted values from the three specifications in Table 3,
sheds light on that question. The columns of Table 4 correspond to the same
columns in Table 3, i.e. column 1 includes the entire sample, column 2 is limited
to those with two Islamic names, and column 3 excludes those with two Islamic
names. The top panel of Table 4 shows estimated values for the randomly drawn
16
Note that only the positives with two Islamic names are included in column 2, and the opposite
is true in column 3. We pool those observations with fewer than two Islamic names because the
frequency of positives is so low among this group, especially for those with no Islamic names.
There are only eight positives among the more than ten million customers in this category, and
thus little information to identify the coefficients. 17
Of course, a second necessary condition to make this model useful to legal authorities is that the
predictions of the model in the extreme right tail are accurate. Evidence on this point will arise
with the passage of time as more positives appear in the data. If, indeed, anti-terror authorities
(who have been provided a list of the most suspicious individuals by the bank) are sufficiently
moved by the arguments in this paper to investigate those deemed most suspicious, the learning
process will occur much more rapidly.
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sample that is representative of the banks overall customer base. The second panel
shows results for the actual positives in the sample. The bottom panel presents
estimates for the non-randomly drawn subset of the bank’s customers who have
high values for Variable Z. For the top and bottom panels, the numbers presented
in the table are the fitted values from the probits in Table 3. For the positives, we
generate these fitted values by running the same specifications reported in Table
3, but excluding that particular individual from the specification when creating the
predicted value. This approach guarantees that no information about a specific
positive is used when constructing his or her predicted value.
The top panel of Table 4 presents results for the random draw of bank
customers. As would be expected given the low frequency of positives in the data,
the predicted mean for this group is vanishingly small: 0.000007 for the overall
sample, and still quite small (0.000392) in column 2 when the sample is restricted
to those with two Islamic names. Even at the 99th
percentile of the distribution of
all bank customers, the estimated likelihood of being positive is only .000085, or
about one in 12,000.
The predicted mean for the actual positives (0.005184) in the middle panel
of Table 4, is not high in absolute terms – the model predicts that there would be
only about .58 positives among the 112 true positives whereas in reality all of the
positives were positives– but the difference relative to the predictions for the
random sample of customers is impressive. If one restricts the sample of positives
to those with two Islamic names, then the model identifies the actual positives in
the sample as being roughly 700 times more likely to be positive than a customer
randomly drawn from the bank’s pool (.005184 versus .000007) and over five
times more likely to be positive than a randomly drawn bank customer with two
Islamic names (.001998 versus .000392).18
Actual positives with two Islamic
names (.001998) have a predicted value that is roughly 90 times larger than
positives with zero or no Islamic names (.000023).
18
It is important to stress that there is nothing mechanical about this relationship since the model
that generates predicted value for the positives in the sample excludes that individual.
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Table 4: Predicted Probability of Being a Positive
at Different Points in the Distribution
All
customers
Two
Muslim
Names
One or No
Muslim
Names
(1) (2) (3)
Random sample
Mean 0.0007% 0.0392% 0.0002%
Median 0.0000% 0.0060% 0.0000%
90th percentile 0.0004% 0.1086% 0.0003%
99th percentile 0.0085% 0.3924% 0.0033%
Positives
Mean 0.5184% 0.1998% 0.0023%
Median 0.0676% 0.1072% 0.0008%
90th percentile 0.4280% 0.5324% 0.0064%
99th percentile 5.5188% 1.4764% 0.0119%
High Variable Z
Mean 5.3669% 14.6104% 2.2638%
Median 0.0805% 0.9256% 0.0116%
90th percentile 3.4010% 95.4286% 0.6030%
99th percentile 100.0000% 100.0000% 89.4580%
Notes: Columns 1-3, defined as before, show the predicted probabilities for being positive based on the three
models in Table 3. The top panel shows various points in the distribution for the randomly selected sample.
The middle panel shows estimated probabilities for the positives (when making these estimates, each positive
is first excluded from estimation). The bottom panel shows people who were selected for having values of
Variable Z. Because the sample of High Variable Z individuals is so small, we omit the results for the 99th
percentile for this group.
The final panel of the Table 4 reports results for individuals with high
Variable Z. The entries in this bottom panel should be thought of differently than
the other entries in the table, because this high-value sample is not randomly
drawn, but rather, explicitly selected on this trait. Because there is almost no data
in this range for Variable Z included in the estimating specifications,
extrapolating the estimates to this sample is highly speculative and the functional
form assumptions of the probit will be critically important, since these values for
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Variable Z are largely out of sample.19
With that important caveat squarely in
mind, the bottom panel reports results for this group. This sample has by far the
highest mean predicted value for being positive (far higher, even, than the actual
positives): 0.053669.
For our high Variable Z sample with two Islamic names, we predicted a
mean value of .146104 which is a more than 370 times improvement over
selecting randomly from among people who have two Islamic names.
Because our primary interest is in the ability of the model to identify
suspects in the far-right hand tail of the distribution, we report other moments of
the data in addition to means. The same patterns emerge when moving from the
means of the data into the right tail. For instance, the model’s predictions for the
actual positives, evaluated at the 90th
percentile of the data, are more than 1,000
times larger than for the randomly drawn sample (.004280 versus .000004).
The extreme results for the specially drawn sample of high Variable Z
customers highlight the critical role that this variable plays, especially in the
upper tail of the distribution; the 90th
percentile individual in our high Variable Z
sample is estimated to have a 3.4 percent chance of being a positive (and among
those with two Islamic names, the estimated probability is near one), although
again we must emphasize that estimate is based on extrapolating the probit results
far out of sample.20
The analysis above focuses on the performance of the full model.
Additional perspective can be gained by analyzing how the performance of the
model degrades when particular information sources are not utilized. Table 5
presents these results. Each entry in the table corresponds to the expected number
of positives who would be identified based on the number of individuals
identified as suspicious (the rows) and the particular model under consideration
(the columns). Moving down the rows of the table, we systematically reduce the
number of customers flagged as suspicious. In the top row, the 10,000 customers
with the highest predicted values from the model are flagged; in the bottom row,
only 250 customers are flagged.
19
Under a logit specification, the results do not change significantly except that predictions in the
far right-tail become slightly more extreme. 20
The key feature of Variable Z, relative to our other explanatory variables, is that it a continuous
variable with a long right tail, whereas the other covariates are indicator variables. Consequently,
the potential value of Variable Z in identifying terrorists is far out of proportion with its degree of
statistical significance in the probit.
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Table 5: The Tradeoff Between False and True Positives Across
Models
Number of
people
identified
At least 1
Islamic
Name
2 Islamic
Names
Full model
without
names or
Z
Full model
without Z
Full model
with Z but
without
names
Full model
(1) (2) (3) (4) (5) (6)
10,000 0.20 3.98 6.62 25.95 10.98 30.10
5,000 0.10 1.98 4.12 15.78 8.92 19.80
2,500 0.05 0.99 2.61 9.18 7.28 13.14
1,000 0.02 0.39 1.33 4.34 5.99 8.07
500 0.01 0.20 0.69 2.41 5.27 5.89
250 0.00 0.10 0.46 1.33 4.41 4.40
Notes: The table entries are the predicted number of terrorists identified, when using the model specified at
the top of the column. Each row corresponds to a differing stringency of screen, e.g. the top row reports the
expected number of terrorists among the 10,000 individuals with the highest predicted likelihood of being a
terrorist; row two is the same information, but for only the 5,000 most likely terrorists according to each
model. Expected values are determined by multiplying the actual flagged list size by the mean fitted
likelihood of being positive within that list. For positives, fitted values are generated by running the model,
but excluding that particular individual from the specification when creating the predicted value.
The columns of Table 5 correspond to different models. In column 1, the
only information exploited is whether a person has at least one Islamic name.
Column 2 conditions only on a person having two Islamic names. Column 3 uses
all the variables in the model except the names and variable Z. Column 4 is based
on the full model, including names, but excluding variable Z. Column 5 uses the
full model, including variable Z, but leaves out the names variables. The final
column corresponds to the full model. The rows of the table capture how many
tight the screen is.
Columns 1 and 2 are pure religious profiling. As is evident from column 1,
one Islamic name, by itself, is not a powerful signal. Screening 10,000 randomly
chosen individuals fitting that criteria would yield only one-fifth of a terrorist.
Screening purely on two Islamic names does better, but still performs quite poorly
compared to the fuller models. An important shortcoming of names as an
indicator is that they do not make strong predictions in the far tail; there are many
thousands of customers with Islamic names; each of these are predicted to be
equally likely to be terrorists when only names are used.
Column 3 takes the opposite approach to Columns 1 and 2, excluding
information on Islamic names. Variable Z is excluded as well. Column 3
outperforms the name-only models across the board, and does especially well as
the number of suspicious individuals screened is reduced.
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Column 4 uses names and behavioral data – everything except Variable Z.
Combining names and behavior produces results that are far better than relying on
either one alone. For instance, when 10,000 customers are screened as suspicious,
three to four times as many positives are detected as when information on names
is excluded, despite the fact that names, by themselves, are not particularly good
predictors.
Columns 5 and 6 add Variable Z, first without names included, and then in
the full model in the final column. When a wide net is cast for suspects, Variable
Z is not that helpful because it takes on a large value for very few bank customers.
The power of Variable Z, however, becomes clear as the number of suspicious
customers screened shrinks. If only the 250 most suspicious customers are
considered, including Islamic names in the model adds nothing; implying that
large values of Variable Z are equally predictive of terrorists whether Islamic
names are present or not. When casting a narrow net, the inclusion of Variable Z
quadruples the power of the model to identify terrorists.
A PROSPECTIVE TEST OF THE MODEL
In September 2009, based on the data set described above, we delivered to the
bank a list of 90 customers who had not previously been investigated on terrorist
charges, but who appeared to be at high risk for such activities. We assembled this
list based on the regression analysis described above, in combination with a more
subjective analysis of Variable Z.21
At the time the list was compiled, the data we
had received from the bank stopped in February 2009. Thus, the period from
February 2009 to May 2010 provide a prospective, out-of-sample test of the
model’s predictions.
Over the 16 month period of the test, the fraction of the bank’s customers
who became “positives” (i.e. became suspected of terrorism by the authorities)
was 0.00055 percent; roughly one in 180,000 bank customers became a positive
over this period. For those on our watch list, however, 2.22 percent (2 of 90 were
arrested) became positives. The individuals on the watch list became terrorism
suspects at a rate that was 4,000 times greater than that of the general banking
population.
While two successful predictions out of 90 may not sound particularly
impressive, it is nonetheless a difficult feat to accomplish.22
The likelihood that a
randomly drawn subset of ninety bank customers would include two new
positives in this time frame is less than one in 8 million. The odds against even
21
It is impossible to describe in precise detail how we carried out this subjective analysis without
revealing the nature of Variable Z. 22
Indeed, based on our model, we had roughly estimated that the expected number of transitions
to positive in this time window for those on our watch list was approximately one.
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one positive out of ninety, based on a random draw, are 2,025 to 1. To put our
results further into perspective, TSA recently released the results of its Screening
of Passengers by Observation Techniques (SPOT) program, an anti-terror
initiative carried out in U.S. airports over the period May 29th
2004 to August 31st,
2008 in which TSA agents trained as Behavioral Detection Officers identified
suspicious looking travelers based upon behavioral and appearance indicators.
Over that time period, more than 150,000 travelers were singled out for further
investigation by the screeners. These 150,000+ investigations failed to yield a
single terrorism-related arrest.23
Upon the discovery that our watch list had succeeded in predicting arrests
out of sample, the bank forwarded the watch list to the relevant anti-terror
agencies.
SECTION IV: CONCLUSION
Combining a variety of data sources, most notably account information from a
large British bank, this paper analyzes the correlates of terrorist involvement, and
explores the extent to which incorporating this type of data might be of use in the
fight against terrorism. A number of demographic factors, especially having an
Islamic first and last name, are strong predictors of being arrested for terrorist
activities. Additionally, a number of behaviors, most notably our Variable Z,
which we do not fully reveal because of its explanatory power, correlate with
terrorism. The efficacy of our approach was subsequently verified in a
prospective, out-of-sample test.
The analysis in this paper provides further evidence that the tools of
forensic economics can be applied to pressing social issues, not simply to
trivialities like sumo wrestling (Duggan and Levitt, 2002) or figure skating
(Zitzewitz, 2006). More broadly, this paper provides an example of the unique
possibilities that arise out of academic-business collaborations (see Levitt and
List, 2009). The human capital required to carry out analyses such as those in this
paper are scarce outside of academics; businesses are the repository of data of a
scale and scope far beyond what academics typically have available.
23
U.S. Government Accountability Office. May 3 2010. Efforts to Validate TSA’s Passenger
Screening Behavior Detection Program Underway, but Opportunities Exist to Strengthen
Validation and Address Operational Challenges. Publication No. GAO-03-631. available from
http://www.gao.gov/new.items/d10763.pdf
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DATA APPENDIX
DEMOGRAPHIC VARIABLES:
Positive: Indicator variable of whether the bank customer was a suspected
terrorist. The customer was either identified from publicly available data on
terrorism-related arrests from salaam.co.uk or from a terrorism-related inquiry by
law enforcement about a customer’s account.
Muslim name status: Islamic first and last names taken from the Contemporary
African Database to include 9,014 common names from Egypt, Tunisia, Morocco,
Libya, Algeria and several other North African or heavily-Islamic countries.
Proximity to mosques: Whether the postal code of a customer’s address is located
within 1 mile of a mosque.
Variable Z: A variable associated with a particular pattern of banking behavior
which dramatically improves our ability to identify terrorists. Because of its
predictive power, we have been asked not to make the nature of the variable
known.
Gender: Gender of the primary account holder.
Age variables: To protect confidentiality and ensure anonymity, data on the
customers’ age were only made available over ranges: Under 16, 16 to 25, 36 to
45 and over 45.
Residential Status: This variable describes the living situation of primary account
holder. Includes owner (with and without a mortgage), tenant (private or
government-sponsored), with parents, or other.
Employment status: Broadly-defined category of employment: Employed,
Unemployed, Retired, Full-time student, Housewife, Self-employed, and
Unknown. Although we also have limited data on job types (further broken down
into categories such as Manager, Clerical, Laborer, etc.), our sample of positives
is not large enough to make use of the data.
ACCOUNT VARIABLES:
Types of financial products: List of what other account types are associated with
the account. Business – whether the customer account is a business account.
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Cards – whether there are associated debit or credit cards. Mortgages – whether
the customer has a mortgage outstanding. Loans – whether the customer has
outstanding loans or other borrowing products (excluding mortgages). Longterm –
includes insurance, any kind of investment, retirement or pension product.
Protections – income protection or other life insurance products (excluding home
or building insurance). Insurance – home and building insurance. Savings –
includes Savings Products, Term Deposits, Stepped Bonds and Flexible Savings
accounts. Extras – Includes miscellaneous financial products (i.e., Marketlink,
Practice Call, Business Cheque products not included in the Business category).
ATM USAGE PATTERNS:
Frequency of withdrawals during Muslim prayer times: The percentage of a
customer’s ATM withdrawals that occurred during Friday prayer hours (10am-
11pm, 12-1pm, 1-3pm).
Fraction of nighttime withdrawals at ATMs: The percentage of a customer’s
ATM withdrawals that occurred at night (from 8pm until 6am).
Average ATM withdrawal amount: Average amount withdrawn per ATM
transaction.
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