Check Fraud Detection whitep1 by qdw43728


									 Image Based Fraud
Detection Technology

   A White Paper
  Mitek Systems, Inc.

       Mitek Systems, Inc.
       14145 Danielson St.
             Suite B
        Poway, CA 92064
      Phone: 888-363-6767

          February 2003
                      Image Based Fraud Detection Technology
                                   White Paper

                                   Table of Contents

Check Fraud: An Overview

Fraud Detection Technology Review

The Limits of Current Technology

Next Generation Image Based Fraud Detection Systems

Some Future Factors

Check Fraud: An Overview

        Check Fraud is often measured along two dimensions: gross dollars attempted and
net dollars lost. According to the American Bankers Association (ABA), the gross dollar
amount of attempted check fraud has been growing at about 50% per year for the past
four years, and was slightly over $4.3 billion in calendar year 2001. Successful check
fraud, or net (actual) dollar losses due to check fraud, has remained stable at $650 to $700
million over the same time period. Information technology fraud detection systems get
some of the credit for keeping the net dollars lost from growing in parallel with the gross
dollars attempted.

        Another interesting statistic from the ABA divides net dollars lost into several
categories, including large and community Banks. Large Bank losses as a percentage of
the total are declining. The same group of large banks report preventing over 80% of the
attempted frauds. Community Bank losses as a percentage of the total actually doubled in
CY 2001, and the reporting group of community Banks in the study prevented less than
50% of the attempted frauds.

       It would be wise for bankers to assume that criminals planning future check fraud
have access to either the ABA study itself or public summaries of the study, and that they
possess at least a rudimentary understanding of statistics. This line of thinking suggests
that community Banks in particular now face a disproportionate increase in future check
fraud attempts. It also suggests that there is room for improvement in making more
affordable and effective systems for all Banks, and for helping Community Banks
implement these systems more rapidly.

Fraud Detection Technology Review

        To understand how to make improvement in fraud detection systems, it is helpful
to understand the current scope and function of these systems. The major focus here is to
describe and categorize the current generation of check fraud technologies, and to
identify the kinds of check fraud that are not being detected at present due to limitations
in the current systems.

        According to the ABA, the most common type of check fraud was forgery of
check signatures and endorsements. A number of check imaging and document imaging
vendors position standard image archive and retrieval systems with remote access as
fraud protection solutions. These systems provide the ability for a teller to retrieve an
image of a signature card or a digital photo of a customer from an archive server. In
reality, these are just standard docume nt imaging system functions that are being
positioned as ‘fraud protection’.

      Several vendors of handwriting recognition systems have also produced signature
comparison and analysis tools. When incorporated into a Bank wide system, these tools
can compare check or item signatures to a good signature master image. If an uncertainty
threshold is exceeded, an application incorporating this technology may produce an
appropriate alarm to bank personnel. Signature comparison and analysis is a form of
image based fraud detection.

        Another type of fraud ‘prevention’ is based on the use of printed authentication
codes. At the time of issue an encrypted code is computed based on some combination of
unique elements for each check. Alteration of any negotiable informa tion corrupts the
authentication code when the image is processed. This type of system may not be
practical for high volumes of personal or business checks. It may be useful in financial
operations where there is a low volume of high dollar checks.

        The m widely deployed fraud detection systems in use today are based on
pattern analysis of transactions. Such systems are typically an extension or add-on to a
check or item processing application. They define a set of transaction variance criteria
that are tracked over time in a relational database. When variance exceeds a predefined
threshold, an alarm mechanism warns bank personnel. All of these systems require at
least 90 days of transaction data on which to base the variance criteria. Some of the
typical transaction variance criteria found in these systems include:

    •   Excessive deposit or withdrawal activity
    •   High dollar deposits or withdrawals
    •   Serial # variance

       These kinds of systems are most widely deployed today in large Banks, and they
have a solid track record of helping find and contain some types of check fraud. These
systems can be best described as transaction based fraud detection.

The Limits of Current Technology

        In the spring of 2002 one or more criminals used counterfeit Bankers cashier
checks in seven or more Banks to commit fraud. Fraud detection systems were unable to
detect some or most of these fraudulent items. These cashier checks were likely printed
using low cost digital color printing technology, and contained valid bank routing
numbers. Some of the items, upon close inspection, contained subtle differences in image
when compared to authentic cashier checks. For example, background and printed text
colors of the forged items were slightly different than authentic items.

       Another common check fraud involves the theft of bill payment envelopes from
home mailboxes. Personal checks intended for standard bill payment are removed from
the envelopes. The payee and amount information is altered on the checks and then

       Transaction based fraud detection systems may not detect either of the above
cases, because the items do not contain any transaction variance criteria that exceed
defined thresholds. These particular fraud examples point towards a need for better image
based fraud detection.

Next Generation Image Based Fraud Detection Systems

       One of the great benefits of the PC and microprocessor revolution has been the
tremendous decline in the cost of raw computing power. This has a direct impact on
image processing systems…it makes them much more affordable and practical from an
implementation and support standpoint.

      A check or financial instrument, once captured in a digital image format, can be
broken down by next generation image based fraud systems into multiple sub-
components. Sophisticated and specific tests can be defined by sub-component. Sub-
component tests can also be aggregated into a single overall score for the whole image.

        This approach provides Banks and their vendors with an ongoing set of tools to
tune image analysis against fraud attacks of varying types. For example, based on known
past patterns of fraud in a particular city, a Bank may define template settings for image
analysis as follows:

   •   A summary score by image plus a signature score for all items over $1,000 US

   •   A summary score by image plus signature score plus payee name, CAR, and LAR
       image zone analysis for all items over $5,000 US dollars.

       The same Bank may add a sub-zone logo analysis for all items of any amount
based on awareness of new, low dollar fraud incidents in a nearby town. What is new and
powerful about next generation sub-component image analysis systems is their ability to
rapidly establish and then revise combinations of fraud detection tests using affordable
PC platforms.

       Here is a more detailed diagram of the components and functions of this next
generation image based fraud technology running as an integrated subsystem within a
Proof of Deposit operation.
              Central Check                                  2b. Set template tests by acct #

               Processing                                     s
                                                      t Ma
                   Site                       1. S
                                             Fraud Protect                          Image Server
                                            (FPA Console)                                       Iden
                                                                                                By A fy Maste
                                                                                        4a.             cct # rs
                                                                                         Ima cess
                                                                                             ges New
                                                                                                  vs.       'O
                                                                                                       Ma nus'

                                        6. A on S

                                            lert usp

                                                FP ect

                                                  AC s
                                                                                                         e co

                                                                                                t #,            cct

                                                                                       ate                    a
                                                                                   alid                    by         FraudProtect
                                                                              3. V                 aba                   Server
                                                                                        ults to
                                                                           5. W
                                                                          Database Server

                                                                                                                   4b. Process New 'Onus' Images vs. Master


                                                                                           Windows PC          Branch Capture

Some Future Factors

         Even before the events of September 11, 2001 impacted the Federal Reserve’s
transit item air transportation system, the concept of digital image network exchange
clearing had significant momentum. It has become clear to many industry observers that
the pending passage of the Check Truncation Act will start a potentially rapid migration
in the handling of transit items. This migration will shift the transit item workload in a
typical Bank away from slower, hardware and transport workflow centric operations to
faster, network software centric modes of operation.

        This shift has obvious potential to improve the speed and resilience of the check
payment system. But it can also improve the speed and effectiveness of transit item fraud
detection. Large network connected image processing data center exchanges are the ideal
place for scalable image base fraud detection systems. Fraud patterns across wide
geographic regions can be detected in real time. Successful detection tests can be applied
almost instantly. While individual Banks should adopt fraud detection systems to handle
‘on us’ items, it will be up to the electronic and image exchanges to provide such services
for ‘transit’ items. Note the following diagram:
   Exchange                                                          Image
    Member                                                          Exchange
     Banks                                                         Data Center

                      Good Master Images (GMI)

                       Transit Item Images (TII)   Fraud Protect
                                                     Console          Image
    Bank 1                                                           Servers

                               IPsec                                             Fraud Protect
    Bank 2                      VPN
                       Alert Member on Suspect

    Bank N


        The ABA statistics on losses from check fraud make it clear that vendors who
really want to help Banks with anti- fraud technology need to focus on continuous
improvement. Image component sub-analysis is especially attractive because it provides
the flexibility and responsiveness that Banks need to react to constantly evolving fraud
techniques perpetrated by criminals.

About Mitek Systems

         Mitek Systems is a leading provider of check imaging software for financial
institutions, and an established global supplier of embedded software recognition engines.
These engines process over 8 billion checks and other documents per year for a variety of
OEM and reseller partners. Mitek helps reduce operating costs by automatically reading,
classifying and processing high volumes of checks and documents. Our applications also
help customers implement new image document Web Services for Internet and intranet
configurations. For more information about Mitek Systems, contact us at 14145
Danielson Street, Suite B, Poway, CA 92064; 858-513-4600 or visit our Web site at

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