U.S. patent application number 13/736240 was filed with the patent office on 2013-08-15 for system, method and computer program product for assessing risk of identity theft.
This patent application is currently assigned to ID Insight Incorporated. The applicant listed for this patent is ID Insight Incorporated. Invention is credited to Robert T. Clark, Adam Elliott.
Application Number | 20130211985 13/736240 |
Document ID | / |
Family ID | 32775811 |
Filed Date | 2013-08-15 |
United States Patent
Application |
20130211985 |
Kind Code |
A1 |
Clark; Robert T. ; et
al. |
August 15, 2013 |
SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR ASSESSING RISK OF
IDENTITY THEFT
Abstract
In one embodiment, this invention analyzes demographic data that
is associated with a specific street address when presented as an
address change on an existing account or an address included on a
new account application when that address is different from the
reference address (e.g., a credit bureau type header data). The old
or reference address and the new address, the new account
application address or fulfillment address demographic attributes
are gathered, analyzed, compared for divergence and scaled to
reflect the relative fraud risk.
Inventors: |
Clark; Robert T.; (Somerset,
WI) ; Elliott; Adam; (Lino Lakes, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ID Insight Incorporated; |
|
|
US |
|
|
Assignee: |
ID Insight Incorporated
Arden Hills
MN
|
Family ID: |
32775811 |
Appl. No.: |
13/736240 |
Filed: |
January 8, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12758171 |
Apr 12, 2010 |
8352281 |
|
|
13736240 |
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10697076 |
Oct 30, 2003 |
7870078 |
|
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12758171 |
|
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60423298 |
Nov 1, 2002 |
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Current U.S.
Class: |
705/35 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 40/025 20130101; H04M 2215/0148 20130101; G06Q 40/02 20130101;
H04M 15/00 20130101; G06Q 30/0185 20130101; G06Q 50/265 20130101;
H04M 15/47 20130101 |
Class at
Publication: |
705/35 |
International
Class: |
G06Q 50/26 20120101
G06Q050/26; G06Q 40/02 20060101 G06Q040/02 |
Claims
1-33. (canceled)
34. A method for assessing a risk of identity theft fraud with
respect to new applications, comprising: receiving first address
information relating to an applicant for an account; and using
demographic data relating to the address information.
35. The method of claim 34, further comprising receiving a
reference address.
36. The method of claim 35, wherein act of receiving a reference
address includes receiving reference address information from a
third party database
37. The method of claim 35, wherein the act of receiving a
reference address includes receiving reference address information
as part of input data provided in making a request to assess a risk
of identity theft fraud.
38. The method of claim 35, further comprises measuring at least
one difference in demographic data appended to the first and
reference address information.
39. The method of claim 38, further comprising calculating a score
indicative of a risk of identity theft.
40. The method of claim 39, further comprising reporting an
assessment of a risk of identity theft based at least in part on
the score.
41. The method of claim 40, further comprising analyzing negative
data for the first address.
42. The method of claim 41, wherein the act of assessing risk of
identity theft is based on the score and analysis of the negative
data.
43. A method for assessing a risk of fraud, comprising: using
demographic attributes of street addresses to predict the risk of
fraud, wherein the act of using comprises analyzing differences
between demographic attributes of the addresses.
44. The method of claim 43, further comprising reporting the
assessment of fraud.
45. The method of claim 44, wherein the act of assessing a risk of
fraud comprises assessing the risk of identity theft fraud due to
account takeover.
46. The method of claim 44, wherein the act of assessing a risk of
fraud comprises assessing the risk of identity theft fraud
perpetrated through a new application.
47. The method of claim 45, further comprising receiving
information relating to a media request.
48. The method of claim 47, further comprising assessing risk of
identity theft when the request for media is made on an emergency
basis.
49. The method of claim 43, wherein the act of assessing risk of
fraud comprises assessing a risk of identity theft in fulfillment
activities.
50. The method of claim 43, further comprises coupling negative and
positive information with address demographic attributes to assess
the risk of identity theft fraud.
51. A method for determining whether an account request for a
change of address from an applicant involves fraud, comprising:
receiving a request to change an address of an account, said
request including an old address and a new address of the
applicant; obtaining demographic data based on the old address of
the applicant; obtaining demographic data based on the new address
of the applicant; calculating a differential between the
demographic data based on the old address of the applicant and the
demographic data based on the new address of the applicant; and
calculating a score for the request based on the differential, the
score indicating whether the request may involve fraud.
52. The method of claim 51, further comprising reporting an
assessment of a risk of identity theft based at least in part on
the score.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 12/758,171, filed on Apr. 12, 2010, issued as U.S. Pat. No.
8,352,281, which is continuation of U.S. application Ser. No.
10/697,076, filed Oct. 30, 2003, issued as U.S. Pat. No. 7,870,078,
which claims the priority from U.S. Provisional Application No.
60/423,298, filed on Nov. 1, 2002, all of which are hereby
incorporated by reference in their entirety.
TECHNICAL FIELD
[0002] This invention relates, in general, to apparatuses and
methods for identifying account fraud. In particular, this
invention relates to detecting fraud and assisting in fraud
prevention due to identity theft including to but not limited to
address change, account takeover fraud, and new account application
fraud. In addition, this invention may be practiced using batch or
real-time, online processing or using customer hosted software
applications.
BACKGROUND OF THE INVENTION
[0003] Numerous businesses, such as financial institutions,
department stores, fulfillment businesses, on-line business, and
businesses making sales over the telephone face the challenge of
protecting the business from customers attempting to defraud it.
These businesses regularly handle thousands of accounts from its
users or consumers. Such accounts may include instant credit or
credit accounts with a department store or other retail outlet, or
accounts involving checks, credit cards, debit cards, or ATM cards
of a bank, credit or other financial institution.
[0004] Identity theft may include account takeover, wherein a thief
steals the identity of an individual and then uses that information
to take over ownership of that individual's account; or new account
fraud, wherein the identity thief uses stolen information to open
new accounts in another person's name.
[0005] Conventional methods for detecting identity theft when
opening new accounts or for modifying existing accounts may be
problematic. Currently, to detect identity theft type fraud,
businesses have used negative databases of suspicious addresses
like mail receiving agents or known fraud addresses. This method is
useful only if there is known negative information. Often, delivery
addresses are not included in a negative database.
[0006] In the case of new account application fraud, contemporary
detection methods focus on the verification of data elements that
are ascertainable by the criminal. These approaches seek to verify
the identity of the new account applicant based on the information
that is provided in the application process. There are typically
three methodologies used in the new account verification process.
First, businesses check negative file resources to see whether
there is negative information associated with a data element e.g.,
the provided social security number belongs to a deceased person.
Second, businesses attempt to verify the applicant's identity
through the use of matching those application data elements to
independent data sources which often only serve to corroborate the
stolen information that the crook is using. Third, there are
logical references like; does the driver's license number fit the
format from the issuing state? These techniques are generally used
for both "in and out of wallet solutions." "Out of Wallet"
verification adds a level of complexity to the criminal enterprise
through the presentation questions based on data not typically
stored in a wallet or purse. For instance, asking a person to
provide a the maiden name of his/her mother.
[0007] As recognized by the present inventors, what is needed is a
system, method, and computer program product for detecting identity
fraud theft using a method that may either supplant or complement
some of the methods discussed above. There is a further need for a
system, method and computer program that identifies both account
takeover identity theft and new account identity theft.
SUMMARY OF THE INVENTION
[0008] In light of the above and according to one broad aspect of
one embodiment of the invention, disclosed herein is a system and
methods for detecting fraud in account requests such as requests
for new accounts, requests for change of address of existing
accounts, and requests for media such as bank checks, duplicate
credits cards, ATM cards, debit cards, past financial statements,
and the like. In one example, embodiments of the present invention
may utilize demographic data based on addresses associated with the
account to determine whether an account request may involve
identity theft fraud, and scores may be generated indicating the
likelihood that the account request may involve identity theft
fraud.
[0009] In one embodiment, this invention analyzes demographic data
that is associated with a specific street address when presented as
an address change on an existing account or an address included on
a new account application when that address is different from the
reference address (e.g., a credit bureau type header data). The old
or reference address and the new address, the new account
application address or fulfillment address demographic attributes
are gathered, analyzed, compared for divergence and scaled to
reflect the relative fraud risk.
[0010] Another embodiment of the present invention relates to a
method for assessing a risk of fraud. The method comprises
receiving at least information relating to a first address relating
to one of an account holder or an applicant; receiving information
relating to a second address; and measuring demographic differences
between the first and second addresses.
[0011] Another embodiment of the present invention relates to a
method for assessing a risk of identity theft fraud with respect to
new applications. The method comprises receiving first address
information relating to an applicant for an account; and using
demographic data relating to the address information.
[0012] Another embodiment of the present invention relates to a
method for detecting a risk of identity theft fraud. The method
comprises combining warm address, known fraud address information,
USPS Deliverable Address File, NCOA files with address specific,
single point, demographic information; and coupling differential
information relating to the addresses to predict a risk of fraud
for at least one of account takeover new account application and
fulfillment fraud.
[0013] Another embodiment of the present invention relates to a
system for assessing a risk of fraud. The system includes a
processor, memory; computer instructions operable by the processor
to append data to at least one variable used in assessing a risk of
identity theft fraud; computer instructions operable by the
processor to analyze differences in demographic data for two
different street address; computer instructions operable by the
processor to calculate a score indicative of a level of risk of
fraud; and computer instructions operable by the processor to
output an assessment of a risk of level of fraud. In calculating
the score, the formula used is of the form:
Y=A+B1*x1+B2*x2+B3*x3 . . . +Bn*xn
where Y is the dependent or outcome variable is the result used to
predict the risk of identity theft fraud, A is a constant value, B1
. . . Bn are the coefficients or weights assigned to the
independent variables, and x1 . . . xn are the independent
variables.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a block diagram of an overall process in
accordance with an embodiment of the present invention.
[0015] FIG. 2 illustrates an example of logical operations for
processing new account requests, in accordance with an embodiment
of the present invention.
[0016] FIG. 3 is a block diagram showing the address information
used in an embodiment of the present invention to detect identity
theft via account takeover or via applications for new
accounts.
[0017] FIG. 4 illustrates examples of logical operations for
processing new account requests as illustrated in FIG. 2, in
accordance with an embodiment of the present invention.
[0018] FIG. 5 illustrates examples of logical operations for
processing new account requests as illustrated in FIG. 2, in
accordance with an embodiment of the present invention.
[0019] FIG. 6a illustrates an example of the logical operations for
determining a risk of identity theft fraud, in accordance with an
embodiment of the present invention.
[0020] FIG. 6b is a block diagram showing logical operations for
appending certain information to addresses in performing analysis
for determining a risk of identity theft fraud, in accordance with
an embodiment of the present invention.
[0021] FIG. 7 illustrates a Power of Sementation summary chart.
[0022] FIG. 8 illustrates another example for processing new
account request, in accordance with an embodiment of the present
invention.
[0023] FIG. 9 illustrates an example of logical operations for
processing requests to take over an account, in accordance with an
embodiment of the present invention.
[0024] FIG. 10 illustrates another example of logical operations
for processing a request to take over an account, in accordance
with an embodiment of the present invention.
[0025] FIG. 11 illustrates examples of operations of FIGS. 9-10, in
accordance with an embodiment of the present invention.
[0026] FIG. 12 illustrates examples of operations of FIGS. 9-10, in
accordance with an embodiment of the present invention.
[0027] FIG. 13 illustrates another example of logical operations
for processing a request to take over an account, in accordance
with an embodiment of the present invention.
[0028] FIG. 14 illustrates examples of logical operations for FIG.
13, in accordance with an embodiment of the present invention.
[0029] FIG. 15 illustrates examples of logical operations for FIG.
13, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION
[0030] The present invention generally relates to a system and
method for detecting or assessing the risk of identity theft fraud.
The present invention will be described in the context of detecting
or assessing the risk of identity theft fraud in two contexts: new
account application fraud and account takeover fraud. However, the
present invention is not limited to only detecting these type of
fraud schemes.
[0031] FIG. 1 shows the general steps used in an embodiment of the
present invention for detecting fraud. As shown in block 10, new
address information is obtained. In the context of a new account
application, this may be the address provided on the application
and in the context of takeover of an account, this may be the new
address provided that is to replace the current address on the
account. As shown in block 12, this new street address information
is compared to a reference address (which may be an address
obtained from a credit report for the person or the current address
prior to the change of address). Next, as shown in block 14,
demographic data associated with the addresses is gathered and
analyzed. As shown in block 16, an assessment of relative risk of
identity theft fraud is made based on the analysis. As such, the
present invention analyzes demographic data that is associated with
a specific street address when presented as an address change on an
existing account or an address included on a new account
application when that address is different from a reference address
(e.g., whether provided by the applicant or obtained from a credit
bureau). For the two addresses, demographic attributes are
gathered, analyzed, compared for divergence and scaled to reflect
the relative risk of identity theft fraud. Risk may be expressed in
a number of ways. In one embodiment, risk is expressed as an upper
bound numerical score from 1 to 100 that is returned with reason
codes to the customer for follow up.
[0032] One advantage to the present invention's use of address
information is that an address is the one element that a criminal
cannot manipulate. That is, when a criminal steals an identity, the
criminal may be able to obtain identity information relating to the
victim. However, the criminal cannot receive mail at the victim's
house. Consequently, the criminal needs to use an address where
he/she can receive mail (e.g., to obtain media or goods). As such,
the present invention compares addresses. The present invention
recognizes that there are demographic differences between
addresses. For instance, one address may have an upscale
socio-economic demographic as compared to the other address that
has a more downscale socio-economic demographic. By using street
address information as the basis for gathering, comparing and
analyzing demographic data, the present invention uses elements
that can be independently verified and analyzed to determine a risk
of identity theft. Also, in addition to the demographic data,
additional data elements such as warm address information or
undeliverable address information may be used to assist in
assessing the risk of identity theft fraud. Within the context of
this document, "Account" as used in this application includes its
ordinary meaning and is also intended to cover any business
relationship where there is financial risk on part of the product
or service provider including but not limited to relationships of
credit, debit brokerage, retail, non-face to face fulfillment
activities (e.g., on-line sales).
[0033] In general, the risk assessment is performed when a business
or service user sends/transmits the old or reference address and
the new (requested changed) or new account application address with
other identifying information for use by the software application
embodying the present invention. Input data is matched to address
specific demographic data which in turn is delivered to the
decision engine to produce a risk score. Data processing can occur
in batch, real time online or on customer or processor hosted
software application. Communications can occur through telephone,
data line, internet or tape/disk or other commercially available
method. The application output may be returned to the service user
via an internet accessed system, telephone, data line, or other
commercially available method.
[0034] In general, the present invention uses statistical modeling
of negative and demographic/socio-economic data elements associated
with a street address to identify suspected identity theft fraud
activity when there is a change in address or an address on a new
application that is different from a reference address (e.g., one
provided by the applicant or one obtained from a third party such
as a credit bureau). As such, this invention may be used to detect
identity theft fraud in existing accounts, new credit account
applications or other business risks associated with address
manipulation. The process generally analyzes the differences in
demographic data between an old address or reference address and an
address on a new application or an address change on an account to
a new address. If a reference address is not provided by the new
applicant or is not the address that was changed to a new address,
then a reference address may be a credit bureau header data or an
address secured from a third party database. Additionally, other
negative and logical data sources are used in the risk evaluation,
such as warm address information, driver's license syntax specific
to a state, or the year a social security number is issued is
compared to the date of birth for rationality. Analysis may
performed through the use of regression models, neural network and
expert rules based technology. A score that scales risk is
developed to identify the likelihood of identity theft fraud. The
score is returned along with supportive investigative data to the
customer/business for use in determining the level of risk it is
willing to take in entering into a business relationship with the
investigated person. Consequently, an embodiment of the present
invention provides businesses with the opportunity investigate a
potential identity theft fraud and take steps to prevent economic
loss. As will be discussed, in the preferred embodiment, the
present invention is implemented in software.
[0035] Referring to FIG. 2, the method for detecting new account
application fraud will be described. FIG. 2 illustrates an example
of logical operations for detecting fraud in the context of
receiving a new account request. As shown in block 20, a new
request is received including the client data, and the received
data is reformatted, normalized, or otherwise processed so that the
data can be further processed. An input data stream or data inputs
from the client/customer are delivered to the host system for
processing. Examples of the type of message elements or data inputs
include the following:
TABLE-US-00001 New Account Application/Address Change (New Address)
data inputs Customer identifier First name Transaction type Middle
initial/name Street directional Last name Street name Surname Unit
number Account or reference number City name Address type code
State Name Social Security Number Zip Code plus 4 Date of Birth
Driver's license information Loss potential - for takeover only
TABLE-US-00002 New Account Reference Address/Address Change Old
Address Customer identifier First name Transaction type Middle
initial/name Street directional Last name Street name Surname Unit
number Account or reference number City name Address type code
State name Social Security Number Zip Code plus 4 Date of Birth
Driver's license information
TABLE-US-00003 Account Access Device Requests, Normal or Emergency
(Credit/Debit Cards, Checks, 5 PIN) requests input file (Address
change process only) Transaction type State name Media type Zip
Code plus 4 Request type First name Account number Middle
initial/name Street directional Last name Street name Surname Unit
number Address type code City name Loss potential - Open to
buy/balance Driver's license information
[0036] However, depending on the implementation, not all of the
data elements need to be sent by the client. In one embodiment, for
assessing risk of new account application identity theft fraud,
input data includes name and address listed on the new application.
In one embodiment, for assessing risk of account takeover identity
theft fraud, input data includes name, current address or reference
address, and the address to which it was changed.
[0037] In general, information that may be provided by a business
that wants an assessment of risk of identity theft may provide the
type of transaction (e.g., new application, change of address,
etc.), information to identify the person that is to be
investigated (e.g., name, social security number, date of birth
etc.), address information as will be discussed with reference to
FIG. 3, account information, and whether there has been a media
request (e.g., a request for checks, credit cards, PIN number, or
other items of value).
[0038] FIG. 3 is a block diagram showing two forms of identity
theft fraud (block 150): takeover identity theft fraud (block 152)
and new application identity theft fraud (block 154). In general,
account takeover occurs when a person (e.g., a criminal) poses as
the customer of a business and changes the address from the
customers' address to the another address (i.e., the criminal's
address). The criminal then had media, such as checks, credit
cards, PIN number, or other items of value (including other goods)
to the new/criminal address. The criminal then may commit fraud
from the unauthorized use of the financial instrument or benefits
from the illegally obtained goods. New application identity theft
fraud involves a criminal submitting a new application that
includes information of another and attempts to obtain media or
other goods and services from the business.
[0039] In the account take over situation, usually there is an
address change to a new address. The current address prior to the
address change may be referred to as the old address, the reference
address, or the FROM address. The new address (i.e., the address
that the reference address was changed to) is sometimes referred to
as the TO address. Similarly, in the new application situation, the
reference address is the old address or FROM address. It may be
provided by the applicant or it may be obtained from a third party
such as a credit bureau. Also, in the new application, address
provided on the new application may be referred to as the new
address or TO address.
[0040] Usually in the takeover situation, because of the address
change, the business that is going to have an assessment made of
the risk of identity theft fraud has an old address or reference
address and a new address. In the new application situation,
usually, a business that is going to have assessment made will have
the address stated on the application but may not have a reference
address. It is more common to use a third party source to obtain a
reference address for analysis of a risk of identity theft in a new
application situation. However, the present invention may be used
when, in a new application situation, a reference address is
provided by the business that wants to analyze the applicant for
identity theft fraud. Some of the information provided by the
business in requesting an analysis for the risk of identity theft
is to provide other information such as a social security number to
assist in obtaining information a reference address for the person
named on the application from a third party source.
[0041] An embodiment of the present invention uses an input data
stream from the client/customer in a processing scenario or
delivers required data inputs to the customer hosted software
application. As shown above, data inputs for account takeover may
include a customer name, account number and the old or FROM
address, and new or TO address. As shown above, new account
application input data may include name, institutional reference
number, reference address and application addresses. If the
reference address is not available, a third party address database
will be consulted. Emergency "Over night" replacement" processing
inputs may include name, address, account or reference number,
account type and open to buy/available credit balance.
[0042] As will be described, input data is compared against the
warm address, known fraud data, USPS deliverable Address File and
the NCOA files. The outcomes of these comparisons are appended to
the inquiry record. The inquiry is then matched to the demographic
data file and appended to the inquiry record. The inquiry record is
written to the inquiry log.
[0043] At block 22, a determination is made as to whether a
reference address is present. If a reference address is provided in
the client data, then such address is also standardized (block 26).
Otherwise, a reference address is appended to the data received
(block 24). If the reference address is not available, a third
party address database may be consulted. For instance, the
reference address may be obtained from a credit bureau and appended
to the data received. Then, the appended reference address is
standardized (block 26).
[0044] In one embodiment, if the reference address and the new
account application address are the same the inquiry will be logged
to an inquiry database and no further action will be taken. In
another embodiment, if the reference address and the new account
application address are the same, the inquiry will be logged to an
inquiry database and the address will be checked to make sure it is
not a warm address or that it is not an undeliverable address.
Also, when the address on a new application matches the reference
address, then the business may not want the analysis conducted.
[0045] If there is a difference between the new account application
address and the reference address, then additional information such
as the information that will be described with respect to blocks
30, 40, 50, 60, 70, and 80 will be appended to both addresses
(block 28). All information is appended to both the reference
address and to the address provided in the application (block 28).
In one embodiment, the information appended includes demographic
data (block 30), U.S. postal service data (block 40), other data
(50), previous history file data (block 60), client fraud data
(block 70) received from a particular client, and address velocity
data (block 80).
[0046] With respect to FIG. 6b, a brief description of the logical
operations performed in determining the data appended from
demographic data (block 30). In selecting demographic data to
append to an address, first an attempt is made to match the name
and address (block 27). If there is a match, then the demographic
data is the appended from that file. However, if there is not a
match for both name and address, then there is an attempt made to
match the address. If a match is made, then the demographic data
for the address is appended. Also, for the area defined by a Zip+4
or Zip code+4, a demographic data for that area is appended. For
instance, if information related to length of residence was being
appended to each address, then first, a search would be made to
match the name and address to the file containing such information.
If a match is made, the length of residence data from that file
would be appended. If such a match is not made, then an attempt
would be made to match the address only. If there is a match, then
the length of residence for the last person at the address would be
appended. Also, the length of 5 residence for the residences in the
Zip+4 would be appended (or an average of the length of residences
for the residences in the Zip+4 would be appended).
[0047] Demographic data may come from a number of national
databases. Such data is compiled by companies such as Experian,
Equifax, InfoUSA, and Acxiom. These databases include publicly
available demographic data from sources such as vehicle
registration data, county assessor information, warranty cards, and
department of motor vehicle data among other sources. These
databases may be accessed to obtain demographic data information.
As shown in FIG. 3, demographic data appended to the addresses as
shown in block 30 may include appending demographic data related to
income (block 32), demographic census data (block 34), demographic
data relating to housing characteristics (block 36) and data
relating to household 15 membership characteristics. Example of
such data include:
TABLE-US-00004 Census/demographic data for
reference/application/change address Address type - residence,
single family Household income apartment, business Length of
residence Owner/renter Number of children Single family/renter
Deliverable address Primary and secondary names Longitude/latitude
Age, primary and secondary Neighbor wealth Gender, primary and
secondary Single family dwelling value Occupation, primary and
secondary Relocation velocity Marital status Education Number of
adults Vehicles
[0048] Further examples of demographic data related to income
include: [0049] RESEARCH--INCOME ESTIMATES [0050] EXPENDABLE INCOME
RANK [0051] NET WORTH RANK [0052] WEALTHFINDER CODE [0053]
POTENTIAL INVESTOR CONSUMER SCORE [0054] REVOLVER MINIMUM PAYMENT
MODEL [0055] BUYER BEHAVIOR CLUSTER CODE [0056] INTERNET USAGE
MODEL [0057] HIGH TECH HOUSEHOLD INDICATOR [0058] HOUSEHOLD OWNS
STOCKS OR BONDS
[0059] Examples of demographic data related to housing
characteristics include: [0060] LIKELIHOOD HOME IS OWNED OR RENTED
ED [0061] DELIVERY UNIT SIZE [0062] HOMEOWNER INDICATOR [0063] AGE
OF HOME SOURCE CODE [0064] AGE OF HOME [0065] ESTIMATED HOME VALUE
CODE [0066] LOAN-TO-VALUE RATIO RANGE CODE [0067] HOME LOAN AMOUNT
[0068] MORTGAGE AMOUNT SOURCE CODE [0069] MORTGAGE BALANCE CODE
[0070] HOME EQUITY ESTIMATE [0071] HOMEOWNER SOURCE CODE [0072]
HOUSEHOLD HAS MOVED FROM ADDRESS RESEARCH--ADDRESS VERIFICATION
ADDRESS VERIFIED BY ANY DICTIONARY [0073] PRIMARY SOURCE OF NAME
AND ADDRESS RESEARCH--SOURCE FLAGS/RECENCY DATE [0074] LENGTH OF
RESIDENCE IN YEARS. Examples of demographic data related to
household membership characteristics include: [0075] HEAD OF
HOUSEHOLD AGE CODE [0076] HOUSEHOLD MEMBER 1 GENDER CODE [0077]
HOUSEHOLD MEMBER 1 TITLE CODE [0078] HOUSEHOLD MEMBER 1 GIVEN NAME
[0079] HOUSEHOLD MEMBER 1 MIDDLE INITIAL [0080] HOUSEHOLD MEMBER 1
SURNAME [0081] HOUSEHOLD MEMBER 1 SURNAME SUFFIX
[0082] Also, the similar information about other members of the
household may be included. Similarly, as shown in FIG. 3, United
States Postal Service data appended to each address as shown in
block 40 may include application of Zip code+4 address
standardization programs (block 42), national change of address
(block 44), delivery point validation and service (block 46),
locatable address conversion system (block 48), NES/Nixie (block
52), delivery sequence file (block 54), and deceased, pandering and
suppression files (block 56). The deliverable address file and the
national change of address file are searched to match the address.
Examples of the delivery validation file and the national change of
address file is as follows:
TABLE-US-00005 U.S. Postal Service Deliverable Address File Street
number Unit number Street directional City name Street name State
Name Zip Code plus 4
TABLE-US-00006 National Change of Address - USPS Street number Unit
number Street directional City name Street name State Name Zip Code
plus 4 Confirmed change of address by USPS Move date
[0083] The following additional information may be gathered from
the United States Postal Service data: [0084] STREET DESIGNATOR
[0085] POST DIRECTION [0086] UNIT TYPE [0087] UNIT NUMBER [0088]
ZIP CODE [0089] ZIP+4 CODE [0090] DELIVERY POINT AND CHECK DIGIT
[0091] CARRIER ROUTE [0092] ZIP+4 MATCH LEVEL [0093] PRIMARY NUMBER
IS A BOX [0094] ZIP CODE STANDARDIZATION [0095] CITY CHANGE
INDICATOR [0096] LOT [0097] STATE CODE [0098] COUNTY CODE [0099]
LACS INDICATOR [0100] FINALIST UNIT RETURN CODE [0101] VENDOR
SOURCE [0102] CITY TYPE INDICATOR [0103] RECORD TYPE FROM ZIP+4
FILE
Appendage
[0103] [0104] MATCH LEVEL [0105] MOVE TYPE [0106] EFFECTIVE MOVE
DATA (YYYYMM) [0107] UNIT TYPE [0108] UNIT NUMBER [0109] CITY NAME
[0110] STATE ABBREVIATION [0111] ZIP CODE [0112] ZIP+4 ADD-ON CODE
[0113] DELIVERY POINT AND CHECK DIGIT [0114] CARRIER ROUTE [0115]
ZIP+4 MATCH LEVEL [0116] PRIMARY NUMBER IS A BOX [0117] LACS RECORD
TYPE [0118] MULTI SOURCE LEVEL [0119] NCOA MATCH FOOTNOTES [0120]
INDIVIDUAL MATCH LOGIC REQUIRED [0121] NIXIE MATCH [0122] HOUSE
NUMBER MISSING [0123] CLIENT RECORD MISSING BOX [0124] ADDRESSES DO
NOT MATCH [0125] STREET NAME DOES NOT MATCH [0126] UNIT NUMBER
MISSING IN CLIENT [0127] UNIT NUMBER TRANSPOSITION [0128] UNIT
NUMBER MISMATCH [0129] CLIENT MISSING 1ST NAME [0130] 1ST NAME
MATCHES 1ST INITIAL [0131] MIDDLE NAME/INITIAL MISMATCH [0132]
GENDER MISMATCH [0133] TITLE/SUFFIXES DO NOT MATCH [0134]
INDIVIDUAL MOVE AND 1ST NAMES DO NOT MATCH [0135] INDIVIDUAL MATCH
LOGIC AND 1ST NAMES DO NOT MATCH [0136] SURNAME MATCH TO GEN.
DELIVERY
Appendage
[0136] [0137] MATCHED TO ZIP+4 FILE [0138] NOT MATCHED TO ZIP+4
FILE [0139] ALL COMPONENTS MATCHED TO DPV [0140] DPV MATCHED BUT
SECONDARY NUMBER INVALID [0141] DPV MATCHED HIGHRISE DEFAULT [0142]
(MISSING SECONDARY [0143] PRIMARY NUMBER MISSING [0144] PRIMARY
NUMBER INVALID [0145] MISSING PO, RR, HC BOX NUMBER [0146] MATCHED
TO CMRA AND PMB, [0147] DESIGNATOR PRESENT [0148] MATCHED TO CMRA
AND PMB, [0149] DESIGNATOR NOT PRESENT [0150] DPV CONFIRMATION
INDICTOR [0151] INVALID ADDRESS PO, RR, OR HC [0152] BOX NUMBER
INVALID [0153] FUTURE EXPANSION [0154] ZIP+4 MATCH LEVEL [0155]
ADDRESS SORT SEQUENCE NUMBER [0156] VACANT INDICATOR [0157]
SEASONAL INDICATOR [0158] RESIDENTIAL/BUSINESS INDICATOR [0159]
THROWBACK INDICATOR [0160] DELIVERY TYPE CODE [0161] DELIVERY POINT
DROP INDICATOR [0162] NUMBER OF DELIVERIES AT THE DROP [0163]
LOCATION ADDRESS CONVERSION [0164] INDICATOR [0165] NO STATISTICS
INDICATOR
Appendage
[0165] [0166] ADDRESS SOURCE CODE [0167] ADDRESS DELIVERY CODES
[0168] PANDER CODE [0169] LOCAL ADDRESS LINE [0170] UNIT
INFORMATION LINE [0171] SECONDARY ADDRESS LINE/URBANIZATION CODE
[0172] LONG CITY NAME [0173] ZIP CODE [0174] ZIP+4 CODE [0175]
MAILABILITY CODE [0176] MILITARY ZIP CODE [0177] OPAC MATCH
INDICATOR [0178] NDI AFFIRMED APT INDICATOR [0179] SECONDARY
ADDRESS INDICATOR [0180] POSTAL COUNTY CODE [0181] LONG CITY NAME
INDICATOR [0182] CARRIER ROUTE CODE [0183] LINE OF TRAVEL
INFORMATION [0184] LOT SORTATION NUMBER [0185] PRESTIGE CITY NAME
USED [0186] ZIP/ADD-ON/DELIVERY POINT
Appendage
[0186] [0187] MATCH CODE
Appendage
[0187] [0188] MATCH CODE [0189] ZIP PLUS FOUR CODE (4 DIGITS)
[0190] ZIP+4 MATCH LEVEL [0191] 4 0 ADDRESS DSF GROUP CODE [0192]
USPS DELIVERY SERVICE TYPE [0193] CARRIER ROUTE CODE [0194]
DELIVERY POINT [0195] 1990 CENSUS CODES [0196] ADDRESS LOCATION
TYPE [0197] LOCATION (DWELLING UNIT) ID [0198] ADDRESS TYPE [0199]
ROUTE TYPE [0200] ROUTE NUMBER [0201] BOX TYPE [0202] BOX NUMBER
[0203] UNIT TYPE [0204] UNIT NUMBER.
[0205] Continuing to refer to FIG. 3, other data may be appended to
these addresses (block 50). Other data may include information from
warm address files comprising high risk addresses like mail
receiving agents, jails, prisons, hotels and the like (block 58).
Warm address file components may include:
TABLE-US-00007 Warm Address File Components Address type: Street
directional Mail receiving agent Street name Other high risk Unit
number Hotel/Motel City name Street number State Name Zip Code plus
4
[0206] Usually, an attempt is made to match the address to an
address in the warm address file. If there is a match, then in one
embodiment, the type (e.g., a description on the place where the
mail would be delivered such as a prison) of address would be
appended.
[0207] Other data may include non-client fraud address files
comprising third party sourced fraud address records (block 60).
Other data may further include Department of Justice county level
crime statistics that scale the geographic propensity to crime
frequency. Other similar information may be appended to the
addresses. This information may be search to match an address, and
append the information if there is a match.
[0208] Also, as shown in FIGS. 2 and 5, any data from a client
fraud file may be appended to the addresses (block 70). This data
may be contributed by the business making the request (block 66).
That is, the business provides fraud address data records. An
example of such a record is as follows:
TABLE-US-00008 Customer/Business Maintained Fraud/High Risk Address
File First name Street name Middle initial/name Unit number Last
name City name Surname State Name Street number Zip Code plus 4
Street directional
[0209] These records may be from on-line case management system
that have stored accessible addresses for confirmed fraud
incidents. This information will be used in the process for
determining a risk of fraud, which may be indicated by a score.
[0210] Also, information is derived relating to inquiry activity
relating to both new address and the reference addresses. This
information is stored and updated in an address velocity file.
Information is appended to the addresses relating to frequency of
inquiries. (block 80). Also, a previous history file is reviewed
for information relating to the new application and reference
addresses. This information may be appended to the addresses (block
60). This previous history file includes previously scored
addresses. This file may include date of scoring, address scored,
and the score. This file may be updated to reflect any scoring
performed on an address. False positive rates are improved through
the use of warm address data, customer maintained known fraud
address file coupled with the U.S. Postal Service National Change
of Address Database. These data sources will be used in the score
development process.
[0211] As shown in FIG. 2, once information has been appended to
the addresses, then a score is created based on all the data (block
82). Generally, statistical models are used to derive a score,
which is used to predict the risk of fraud. At block 82, a score is
created based on the data associated with the request and the
appended data. FIG. 6a shows the logical operations for determining
a score in accordance with one embodiment of the present
embodiment. As shown is FIG. 6a, as shown in block 180, the first
step is to analyze the demographic data appended to each of the
addresses and derive information used to predict the risk of fraud.
Next, as shown in block 182, a score is calculated based on the
weights placed for each of the selected variables. In one
embodiment of the present invention the following variables have
been selected to be used in the model to predict the risk of fraud:
(1) a variable that is based on the change in the financial make-up
of the two addresses; (2) a variable that identifies records that
were confirmed through third party data to match the name at a
given address; (3) a variable that is based on the home value
between the two addresses; (4) a variable that is based on the
distance of the move for the change of address; (5) a variable that
is based on whether the type of housing (e.g., apartment,
non-apartment, single family home) has changed for the current
address in comparison with the reference address or old address;
(6) a variable that is based on whether the application address or
the new address is a building (i.e., not an apartment or a home,
rather something other than an apartment or a home); (7) a variable
based on whether the new application address, the new address or
current address is a warm address; (8) a variable that is based on
the difference in Internet usages for the Zipcode+4 area for the
two addresses; and (9) a variable that is based on the average
length of stay at the residence at the Zip+4 area code for the
reference address or the old address (when there is an address
change requested). Then, the second step is to use the model to
obtain a score to predict the risk of fraud. Each of these
variables will be discussed in turn.
[0212] The first variable is based on the change in the financial
make-up of the two addresses. In one embodiment of this model, this
variable is called "Value1." This variable analyzes the change in
the financial make-up of the reference address, the old address
(e.g., in address change or account takeover situations), or FROM
address (e.g., old address) and new application address, the new
address, or the TO address (e.g., the address to which it has been
changed). It is a composite of three demographic variables: Income,
Net Worth and Home Ownership. In one embodiment, to derive the
composite information the following steps are used. First, the
difference in income is determined. As described with respect to
FIG. 6b, to determine the difference in income, for both addresses
(e.g., new application address and reference address in risk of
fraud relating to a new application or as will be described later,
reference or old and new addresses in a takeover situation), income
for the respective address is appended by matching name and address
to the appropriate demographic file. If there is not a match by
both name and address, then a search is made to match at by address
only to find income. If there is not a match by address only, then
the Zip+4 for an address is used and the average income for that
Zip+4 is appended to the address. If there is still not a match,
then the mean income for all individuals is assigned. For instance,
the mean income for all individuals may be assigned, when a Zip+4
for a particular address cannot be determined or when demographic
data cannot be located for the address of a Zip+4 area.
[0213] Once, a value has been appended to each address for income,
then the difference in income between the two addresses is
calculated using the following formula:
DF_INCOME=INCOME(FROM)-INCOME(TO)
[0214] Where DF_INCOME refers to the difference in income between
the two addresses, INCOME(FROM) refers income appended to the
reference address or old address, and INCOME(TO) refers to income
appended to new application address or the new address.
[0215] Next, the difference in net worth ranking is constructed. To
determine the difference in net worth, for both addresses, net
worth ranking is appended by first trying to match by name and
address to the demographic file. If a match is not found, then
match by address only is attempted to find net worth ranking. If
there is still no match, then a match is made to the Zip+4 of the
address and the average net worth ranking for that Zip+4 is
appended. If there is still no match, then the mean net worth
ranking for all individuals is appended to the address. For
instance, as with income, the mean net work ranking for all
individuals may be appended when a Zip+4 for a particular address
cannot be determined or when demographic data cannot be located for
the address of a Zip+4 area.
[0216] Once, a net worth value has been appended for both
addresses, then the difference in net worth between the two
addresses is calculated as follows:
DF_NETWR=NETWR(FROM)-NETWR(TO)
DF_NETWR refers to the difference in net worth. NETWR(FROM) refers
to the net worth of the reference address or old address and
NETWR(TO) refers to the net worth of the new application address or
the new address.
[0217] Next, the difference in homeownership is constructed. In
order to determine the difference in homeownership, for both
addresses, a homeowner indicator is appended to both addresses by
matching name and address to the appropriate demographic file. If
there is not match, then a homeowner indicator is appended by
matching by address only to find homeowner indicator. If there is
still no match, the average homeownership percentage for that Zip+4
is appended. If there is still no match, the mean homeowner
percentage for all individuals is assigned. For instance, as with
income, the mean homeowner percentage for all individuals may be
appended, when a Zip+4 for a particular address cannot be
determined or when demographic data cannot be located for the
address of a Zip+4 area.
[0218] Once, we have a value for both the FROM and TO address, we
then calculate the difference between the FROM and TO address as
follows:
DF_HOMEON=HOMEON(FROM)-HOMEON(TO)
[0219] Where DF_HOMEON refers to the difference in homeownership,
HOMEON(FROM) refers to homeownership for reference address or old
address, and HOMEON(TO) refers to homeownership for the new
application address or new address.
[0220] Once the three difference for the income, net worth and
homeownership have been calculated, then a variable that is a
combination of the three is created:
TABLE-US-00009 IF DF_HOMEON <= -1, THEN--VALUE1 = 0.00056 IF
DF_HOMEON > -1 and DF_HOMEON <= 0 AND DF_NETWR <= -4.7
THEN VALUE1 = 0.00701 IF DF_HOMEON > -1 and DF_HOMEON <= 0
AND DF_NETWR > -4.7 and DF_NETWR <= -2.7 THEN VALUE1 =
0.00131 IF DF_HOMEON > -1 and DF_HOMEON <= 0 AND DF_NETWR
> -2.7 and DF_NETWR <= -1.7 THEN VALUE1 = 0.00191 IF
DF_HOMEON > -1 and DF_HOMEON <= 0 AND DF_NETWR > -1.7 and
DF_NETWR <= -0.7 AND DF_INCOM <= -11,000 THEN VALUE1 =
0.00056 IF DF_HOMEON > -1 and DF_HOMEON <= 0 AND DF_NETWR
> -1.7 and DF_NETWR <= -0.7 AND DF_INCOM > -11,000 THEN
VALUE1 = 0.00565 IF DF_HOMEON > -1 and DF_HOMEON <= 0 AND
DF_NETWR > -0.7 and DF_NETWR <= 0.3 THEN VALUE1 = 0.00066 IF
DF_HOMEON > -1 and DF_HOMEON <= 0 AND DF_NETWR > 0.3 and
DF_NETWR <= 2.3 THEN VALUE1 = 0.00131 IF DF_HOMEON > -1 and
DF_HOMEON <= 0 AND DF_NETWR > 2.3 THEN VALUEI = 0.00297 IF
DF_HOMEON > 0 AND DF_NETWR <= 5.3 THEN VALUE1 = 0.01894 IF
DF_HOMEON > 0 AND DF_NETWR > 5.3 AND DF_INCOM <= 37,000
THEN VALUEI = 0.00275 IF DF_HOMEON > 0 AND DF_NETWR > 5.3 AND
DF_INCOM > 37,000 THEN VALUE1 = 0.01095
[0221] The numerical values are derived from a statistical analysis
using known methods of actual identity theft fraud data, which was
used to build this model.
[0222] The next variable identifies records that were confirmed
through third party data to match the name at a given address. This
variable is titled "MATCH." If a match is found to the third party
database (demographics) via name and address, this variable is
coded as a value of 1. If it is not confirmed, it is coded as a
0.
[0223] The next variable is based on the home value between the two
addresses. To determine the value for this variable an analysis of
the change in the home value is performed. This variable is named
"DF_HOMVL." In one embodiment, the difference between the home
value of the FROM address (e.g., reference address in a new
application situation or the old address in takeover situations)
and the TO address (e.g., the new application address in a new
application or a new address in takeover situations). For both the
FROM and TO address, a home value is appended by matching by name
and address to the appropriate demographic file. If there is not a
match, then the home value is appended based on a match by address
only. If there is still no match, then the average home value for
that Zip+4 of the address is appended. If there is still no match,
then the mean home value for all individuals is appended. Once, we
have a value for home value for both the FROM and TO address, we
calculate the difference between the FROM and TO address as
follows:
DF_HOMVL=HOMEVAL(FROM)-HOMEVAL(TO)
[0224] Where DF_HOMVL is the difference in home value,
HOMEVAL(FROM) refers to the home value of the address prior in time
to the one reflected as the address in a new application or in a
change of address, and HOMEVAL(TO) refers to the address on the new
application form as the current address or the new address provided
in changing the address.
[0225] The next variable in the model is based on the distance of
the move for the change of address. This variable is named
"DF_DISTN." In one embodiment, this variable measures the distance
of the move for the change of address. Using the delivery point for
both the FROM and TO address, we then determine the longitude and
latitude of the delivery point. We then calculate the distance of
the move as follows:
DF_DISTX=69.1*[TO(Latitude)-FROM(Latitude)]
DF_DISTY=69.1*[TO(Longitude)-FROM(longitude)]*COS[FROM(latitude)/57.3)
DF_DISTN=SQRT[(DF_DISTX*DF_DISTX)+(DF_DISTY*DF_DISTY)]
[0226] Where DF_DISTX refers to the change in latitude from the TO
and FROM addresses multiplied by 69.1, DF_DISTY refers to the
change in longitude from the TO and FROM addresses multiplied by
the cos of the latituted of the FROM address divided by 57.3, all
of which is multiplied by 69.1, and DF_DISTN is calculated by the
square root of the sum of the squares of DF DISTX and DF_DISTY. The
mathematical calculation is a known formula for converting
latitudinal and longitudinal information into a distance.
[0227] The next variable is based on whether the type of housing
(e.g., apartment, non-apartment, single family home) has changed
for the current address in comparison with the reference address or
old address. This variable is called "HOMAPT." In one embodiment,
this variable indicates whether or not a person has moved from a
non-apartment to an apartment. In one embodiment, if the FROM
address is not an apartment and the TO address is an apartment,
this variable is coded as a 1. Otherwise this variable is coded as
a 0.
[0228] The next variable is based on whether the new application
address or the new address is a building. This variable is named
"BLDNG." This variable indicates whether or not the TO address is a
building. In the model, If the TO Address is a Building, this
variable is coded as a 1. Otherwise this variable is coded as a
0.
[0229] The next variable is based on whether the new application
address, the new address or current address is a warm address. In
short, this variable indicates if the second address is "warm".
Warm addresses are addresses that are non-standard delivery
addresses. This type of address includes addresses such as UPS
Stores, Mail Boxes, Etc., hotels/motels, etc. The variable is named
"WARMADD." In the model, if a match is made by TO the address to
the Warm Address file, this variable is coded as a 1. Otherwise
this variable is coded as a 0.
[0230] The next variable is based on the difference in internet
usages for the Zipcode+4 area (sometimes also referred to as Zip+4)
for the two addresses. In one embodiment, this variable measures
the difference in internet usage for the area defined by Zip+4 for
the FROM address to the area defined by the Zip+4 for the TO
address. This variable is named "Z4_WEB." In one embodiment, this
information is derived as follows. First, the average internet
usage is calculated for the Zip+4 area for both the FROM address
and the TO address. This data is resident on the demographic file,
where a value of 1 indicates lowest likelihood of internet usage
and 9 indicates the highest. Then, the average value for all
addresses in the specific Zip+4 area is calculated. Once the value
for each the FROM and TO addresses is determined, the difference
variable is coded as follows:
Z4_WEB=WEBUSE(FROM)-WEBUSE(TO)
[0231] Where Z4 WEB refers to the difference is web usages for the
area defined by the Zip+4 for each of the addresses, WEBUSE(FROM)
refers to the average internet usage for area defined by the Zip+4
for the FROM address (e.g., the reference address in a new
application situation or the old address in a takeover situation),
and WEBUSE(TO) refers to the average internet usage for the Zip+4
for the area defined by the TO address (e.g., the new application
address or the new address in the takeover situation). While
average internet usage is used as the measure, other measures such
as median internet usage may be used in the appropriate model.
[0232] The last variable used in this embodiment of the model is
based on the average length of stay at the residence at the Zip+4
area code for the reference address or the old address (when there
is an address change requested). This variable is named "Z4 LORF."
In one embodiment, this variable measures the average length of
residence for the area defined by the Zip+4 for the 5 FROM address.
In one embodiment, this information is derived as follows. First,
the average length of residence for the area defined by the Zip+4
is calculated for the FROM address. This data is resident on the
demographic file, where the values indicate the number of years a
person has resided at that residence. Then, the average value for
all addresses in that Zip+4 area is calculated. The variable then
indicates the average length of residence for people living in the
area defined by the Zip+4 for the FROM address.
[0233] In one embodiment, the model used to predict has nine
variables. However, the model used to predict may have any number
of variables. Also, the variables used may evolve based on
information collected on the characteristics of confirmed fraud
accounts. Another factor that may change the variables used relates
to the evolution of methods used by the people committing the
fraud. As the methods change, the variables may have to be varied.
However, the present invention is not limited to the number of
factors used on the types of factor used in the model to predict
the risk of identity theft fraud.
[0234] Once the variables have been analyzed, the values for each
of the variables are plugged into the model. The basic formula for
the model is generalized as follows:
Y=A+B1*x1+B2*x2+B3*x3 . . . +Bn*xn,
[0235] Where Y is the dependent or outcome variable is the result
used to predict the risk of identity theft fraud, A is a constant
value, B1 . . . Bn are the coefficients or weights assigned to the
independent variables, and x1 . . . xn are the independent
variables themselves. In the embodiment described above, the
independent variables include VALUE1, MATCH, DF_HOMVLDF_DISTN,
HOMAPT, BLDNG, WARMADD, Z4WEB, and Z4_LORF.
[0236] Using known statistical methods to analyze actual data from
confirmed identity theft fraud cases, the following coefficients
were determined for the model:
COMPUTE SCORE = 0.00154 + VALUE 1 * 0.93061 + MATCH * - 0.00594 +
DF_HOMVL * 2.12 E - 09 + DF_DISTN * 1.53 E - 06 + HOTVLEAPT *
0.002093 + BLDNG * 0.002334 + WARMADD * 0.078844 + Z4_WEB * -
0.00021 + Z4_LORF * 0.000134 ##EQU00001##
[0237] Where COMPUTE SCORE refers to the score that will be used,
at least in part, to predict a risk of identity fraud. In this
method, the coefficients were determined using ordinary least
squares regression. However, other known statistical methods such
as logistic regression, CHAID, CART, discriminant analysis, neural
networks or the like may be used.
[0238] In one embodiment the score is between 0 and 1 with 1 being
most likely to be fraud. However, the scale may be any range. For
instance, the score may be in a range of 1 to 100. Similarly, the
score may be converted to a description. So depending on the risk
tolerance of the institution making the inquiry, ranges may be
provided that would indicate likelihood of identity theft fraud.
For instance, on a scale of 0 to 1, a 0.8 or above may be
designated as a high risk for fraud and the report to the company
making the inquiry may be a descriptive assessment based on a
numerical score rather than the score itself. The score itself
shows some level of risk of identity theft fraud. Whether the level
of risk is acceptable is one that must include input from the
business as to its tolerance of this risk. Also, while the score
itself may be used to predict whether identity theft is being
perpetrated, the score may be used with other data such as, without
limitation, warm address files, undeliverable mail addresses,
syntax of the drivers license for a particular state to assess a
risk of fraud, or the year the social security number was issued is
compared to the date of birth for rationality.
[0239] The model described for determining a score was developed
using confirmed identify theft fraud data. However, while the
variables selected are based on an analysis of this confirmed fraud
data, other variables may be selected. Because the model described
herein is based on a statistical analysis of confirmed fraud data,
the model takes what is known about the past and applies it to
future events. Over time, however, behaviors and relationships
change. This is especially true in the area of identity theft
fraud. As fraud models and tools are effectively deployed, the
fraud migrates, creating new behaviors and relationships. Because
of this, the model may be modified by using the same methods
described herein to emphasize certain variables or add other
variables from the information sources described herein. The model
described herein was tested to understand how well the model
"performs" or segments the entire population of applications. The
effectiveness of the model described here is shown by the
segmentation table and the ROC curve.
[0240] In developing the model, the confirmed fraud data is scored.
The scored data was categorized into equal sized buckets or
categories from lowest to highest. Thus, the identity theft fraud
rate present within each bucket is shown by categorizing the worst
5% into the first bucket, the next worst 5% into the second bucket,
etc. The following chart shows the performance of the model.
TABLE-US-00010 Percent Indexed Of Fraud Segment Cases Rate 1 5% 908
2 5% 279 3 5% 301 4 5% 93 5 5% 88 6 5% 88 7 5% 76 8 5% 59 9 5% 42
10 5% 17 11 5% 21 12 5% 4 13 5% 8 14 5% 0 15 5% 8 16 5% 4 17 5% 0
18 5% 0 19 5% 0 20 5% 0 TOTAL 100% 100
[0241] In this example, segment 1 is the worst 5% of scored records
from the test data set. As shown by the chart, this segment has a
fraud rate that is over 9 times the average fraud rate for the
entire population. (Note: the Indexed fraud rate is calculated by
taking the segment level fraud rate divided by the overall
population fraud rate*100.)
[0242] Another way to look at the performance of the model is to
look at a Power of Segmentation summary chart (FIG. 7). This is
sometimes also referred to as a ROC curve or Lorenz Diagram. This
view shows how many cumulative fraud records are identified for
each level of screening.
[0243] For example, this curve indicates that the model is able to
identify approximately 60% of the total frauds (y-axis) by only
looking at the worst 10% of records as identified by the model
(x-axis). Similarly, the curve shows that the worst 5% account for
approximately 45% of the total fraud. The top line shows how well
the model performs, whereas the lower line shows how a randomly
generated model performs (i.e., If one looked at 10% of the
records, one would expect to identify about 10% of the fraud.)
[0244] Going back to FIG. 2, after the score is determined, at
block 84, the address velocity file is updated with the score.
Next, at block 86, apply business rules to the data. This business
rules are to ensure that regardless of the score, certain data
elements are checked (e.g., whether the address is a warm address,
whether the address is a undeliverable mail address, whether social
security number is valid etc.) That is, create a file on this
analyzed case and include in that data relating to whether a warm
address was present, whether it was a reported fraud address, or
whether the address was an undeliverable mailing address. Such
information may be used in analysis of other inquiries in the
future. Moreover, regardless of the score, if the new address or
the address on an application is a warm address, then the rule may
be to report that as a high risk of identity theft.
[0245] Also, regardless of the fraud risk information, data
relating to undeliverable mailing addresses would be useful
information for the customer making the inquiry because sending
media (e.g., checks, credit cards or the like) to an undeliverable
providing address is expense to the business and creates a risk for
fraud to be committed. As such, the customer making the inquiry
that the address is an undeliverable mailing address would be
useful to the customer and would save the customer the expense of
mailing media to an undeliverable mailing address. Also, by not
mailing media to an undeliverable address, the customer would
reduce the risk of fraud being committed with the media.
[0246] Next, at block 88, user defined parameters are applied. That
is, the business making the request may have some criteria (e.g.,
verify syntax of the driver's license). Each may provide
information related to score thresholds based on its tolerance for
risk. Apply those requirements and append that information with the
score and the other information discussed with respect to business
rules to create an output for sharing with the business.
[0247] At block 90, fraud alerts may be created with reason codes
and transmitted to the business entity through a user interface at
block 92 or a web server at block 98. The reason codes may be based
on user defined criteria or codes based on the variables used in
the analysis or data considered in the analysis. At block 91, the
previous history file for this account may also be updated. As
shown in blocks 94 and 96, a case management system provides
display screen functionality for the fraud alerts, management
queuing functionality with operator and pending case tracking.
[0248] In terms of output to the customer who initiated the
inquiry, in one embodiment, the output message content includes the
following:
TABLE-US-00011 Output Message Content Score First name One or more
reason codes Middle initial/name Account or reference number Last
name Surname
[0249] However, the output may be provided in a other ways. For
instance, the output may be provided by simply stating a level of
risk or providing a statement of the level of risk of fraud in
addition to the score. Also, while the information related to the
level of risk of fraud may be communicated via a data line, the
internet, a facsimile or by voice (including an operator simply
calling the customer with an oral report of the risk analysis).
[0250] Also, the web server (block 98) may be used by the customer
to provide confirmed fraud data, which would be used to update the
client fraud data file for future use.
[0251] In operation, the business/customer makes an inquiry to
assess a level of risk of fraud on a new application. Data is
appended to the address provided on the new application and the
reference address (from a third party source such as a credit
report or this information may be on the application). A score is
derived using the model described above. The result may be provided
real-time or via batch processing. In either case, the results
maybe provided to the customer in any commercially practicable
method including, but not limited to, a data line, the internet, a
facsimile or by voice (electronic or human voice). Customers may
establish internal policies and procedures for handling accounts
based on the score.
[0252] The system described with reference to FIG. 2 is a
client-server system. The client transmitted the request and input
information to a remote server for processing. FIG. 8 shows the
logical operations used in a system that is hosted at the client
site. That is, the customer hosts the system for determining the
risk of fraud in a new application process or on an account
takeover situation.
[0253] As shown in FIG. 8, most of the logical operations are the
same as the operations described in FIG. 2. However, one difference
is that the client hosts the software to perform the analysis to
create the score. Also, depending on the level of resources
committed by the client may not access all the demographic data
described in the process described with respect to FIG. 2. For
instance, the client hosted solution may be limited to Zip code
plus+4 data variables. As such, the model may not be as rigorous as
the model as described with respect to FIG. 2. This type of system
may be provide a risk analysis that while less rigorous useful in
some situations.
[0254] FIGS. 8-15 show alternatives to the basic method described
with respect to FIG. 2 for use in account takeover situations. That
is, the basic logical operations of appending information to the
addresses and calculating a score as described with references to
FIGS. 2-6b would be used. As described with respect to FIG. 2, in
determining fraud with respect to a new application the reference
address is usually linked to the applicant's identity, not
necessarily the address on the new application form. As described
above, usually, in a new application situation, the reference
address is obtained from a credit bureau. However, in the takeover
situation, the old address or the FROM address would be the
reference address and the address to which it is changed to is the
new address (e.g., the TO address). A customer may want each change
of address analyzed to determine a risk of fraud and match to
subsequent media requests, a customer may want the change of
address analyzed only when such a request is matched to a media
request, or a client may want each change of address analyzed for
risk of fraud. Each of these situations will be discussed in turn
with reference to FIGS. 6b-13.
[0255] FIGS. 9, 11, and 12 show the logical operations for an
embodiment in which an analysis is performed for each address
change and a match is made for subsequent media requests. As shown
in FIGS. 9, 11, and 12, the logical operations for analyzing the
risk of fraud is the same as that described and shown in FIGS.
2-6b. That is, information is appended to the old address (the
address before the change of address request)--which for takeover
situation would be considered a reference address--and to the new
address (i.e., the address it was changed to). Then, a score would
be derived using the model described with reference to FIG. 6a.
However, as shown in block 300, there is an address change file
that maintains the change in address for a particular account.
Also, as shown in block 302, a media request file is maintained. A
media request may include a request for financial instruments such
as checks or credit cards. In addition, as shown in block 304, a
scored history file is maintained to store the score based on the
analysis done (consistent with the analysis as described in FIG. 2)
for an account in which there was a change in address. When a media
request is made, it is checked against the scored history file. If
there is a match in terms of an address change in the same account
on which the media request is made, business rules--which may be
supplied by the customer--are used to determine whether to honor
the media request. Some factors that may be used include the time
lapse between the media request and the address change and the risk
of identity theft fraud as determined by the scoring.
[0256] As shown in FIGS. 10, 11, and 12, customers may only want an
address change analyzed for risk of fraud if it is followed with a
media requested within a period of time of the address change. It
should be noted that the media request may be prior to the address
change request. In this situation, as shown in block 320, a media
request file is maintained storing media request information on
accounts. Also, as shown in block 322, a 90 day rolling address
change file is maintained. While in one embodiment the rolling
address change file has a 90 day window, the rolling address file
is not limited to a 90 day window but rather may be constructed to
any length of time. As shown in block 324, a determination is made
as to whether a media request matches a change in address request.
If so, then the analysis to score the change in address as
described with respect to FIG. 2-6b is performed (as shown in FIGS.
10-12).
[0257] FIGS. 13-15 show the process described with respect to FIGS.
2-6b being applied in the case when each address change is scored,
but no additional steps are performed with respect to media
requests.
[0258] As with the process described with respect to new
applications, a numerical score derived from this process may be
used to assess risk. However, in other embodiments, the score may
be considered along with data analyzed based on the business rules
and client-defined parameters to make as assessment of the risk of
identity theft. This information may be provided in any number of
ways including voice, data line, facsimile. Also, the processing
for takeover accounts may be done in batch, real-time, and in a
client-server structure where the server is in a remote location or
in a structure where the system is hosted at the client site.
[0259] There are several purpose for which this invention serves. A
purpose of this invention is to prevent fraud losses associated
with account takeover. An additional purpose of the invention is to
prevent fraud losses that accrue from criminals submitting
fraudulent credit account applications to financial institutions
where the criminal assumes the credit identity of an unknowing
person/victim. If the account is approved, the criminal receives
the credit card, debit card, checks or merchandise or services at a
street address other than that of the victim.
[0260] An additional purpose of this invention is to reduce fraud
losses in a form of account takeover that is associated with "over
night" emergency requests for the replacement of items such as
credit/debit cards, personal checks, traveler check replacements.
There is a business and competitive need for financial institutions
to provide emergency replacement services. Criminals can affect an
account take over by exploiting the Emergency replacement process
through requesting that an unauthorized replacement, be sent to an
address for which they have access. The criminal receives the
replacement and commits unauthorized use fraud. Emergency type
credit and debit card replacements are often requested to be sent
to an address other than the address of record. A financial
institution has a short processing window to establish the
legitimacy of these requests. "This invention would help to
identify potentially fraudulent requests using the analysis
described above.
[0261] Another purpose of this invention is to reduce fraud losses
where product or service fulfillment or billing activities involve
a street address and the effects of fraudulent addresses that would
be negative to business interests. This can occur in the retail
environment particularly in non-face to face transactions. In
addition to reduced direct fraud losses through superior detection,
the purpose of this invention is to reduce overhead and
infrastructure expenses associated with low false positive rates,
reduced infrastructure expenses that are necessary to process
fraudulent claims and an improved customer experience.
[0262] As can be seen by the above Figures, different factors may
be considered depending upon the particular request that is
received, and may be dynamically determined as to what factors
should be considered for a given request. For instance, some
requests may only utilize certain factors, while other requests may
involve checks of all factors in providing a score.
[0263] Hence, it can be seen that embodiments of the present
invention provide various systems and methods that can be used for
detecting fraud in account requests.
[0264] Embodiments of the invention can be embodied in a computer
program product. It will be understood that a computer program
product including one or more features or operations of the present
invention may be created in a computer usable medium (such as a
CD-ROM or other medium) having computer readable code embodied
therein. The computer usable medium preferably contains a number of
computer readable program code devices configured to cause a
computer to affect one or more of the various functions or
operations herein described.
[0265] While the methods disclosed herein have been described and
shown with reference to particular operations performed in a
particular order, it will be understood that these operations may
be combined, sub-divided, or re-ordered to form equivalent methods
without departing from the teachings of the present invention.
Accordingly, unless specifically indicated herein, the order and
grouping of the operations is not a limitation of the present
invention.
[0266] While the invention has been particularly shown and
described with reference to embodiments thereof, it will be
understood by those skilled in the art that various other changes
in the form and details may be made without departing from the
spirit and scope of the invention.
* * * * *