according to one aspect of the invention , systems are provided to combat fraud in the issuance of driver &# 39 ; s licenses (“ enrollment ”). prior to issuance of a driver &# 39 ; s license , state officials ( e . g ., field agents in the state &# 39 ; s department of motor vehicles ( dmv )) typically require production of two or three identification documents (“ collateral ” documents ). some states require that at least one of these documents provide some evidence of the applicant &# 39 ; s age . different jurisdictions have different requirements as to the type and number of collateral identification documents needed . acceptable documents in one exemplary state include ( but are not limited to ): 1 . original or certified copy of birth certificate ; 2 . military or armed forces id card ; 3 . military discharge papers ; 4 . selective service registration card ; 5 . u . s . passport ; 6 . non - u . s . passport ; 7 . alien registration card ; 8 . immigration or naturalization documents ; 9 . adoption decree or adoption certificate issued by a court ; 10 . canadian driver &# 39 ; s license , instruction permit , or identification card ; 11 . out - of - state driver &# 39 ; s license , instruction permit , or identification card ; 12 . department of corrections age and identity letter ( with photo ) 13 . department of corrections inmate identification card ; 14 . mexican consular id card ; 15 . student body id card ; 16 . social security card ; 17 . vehicle title or registration ; 18 . company identification card ; 19 . pistol or firearms permit ; 20 . liquor control service permit ; 21 . personalized check / statement , or savings account passbook ; 22 . driver license renewal reminder ; 23 . voter registration card ; 24 . property tax statement ; 25 . w - 2 tax form ; 26 . medical or health card ; 27 . department of corrections release letter ; 28 . parole papers ; 29 . certified copy of school transcript ; 30 . pilot &# 39 ; s license ; 31 . court papers or court orders , such as legal name changes ; 32 . affidavit of identity by parent . ( items 1 - 14 are accepted as proof of age .) to applicant &# 39 ; s knowledge , there has been no systematic study concerning patterns of use of collateral documents presented at the time of enrollment . in accordance with one aspect of the invention , information is gathered concerning documents presented at the time of enrollment , and this data is later analyzed to determine which documents are most frequently ( and / or most rarely ) correlated with fraud in the enrollment process . fraud in the enrollment process may be detected at various times , including during attempted enrollment ( e . g ., through automated or manual checking conducted before the license is issued to the applicant ), or later ( e . g ., through a police arrest of an individual found to be carrying multiple driver &# 39 ; s licenses with the same photo but different names ). in one particular embodiment , relevant information is collected in a database , including identification of collateral documents presented during enrollment , and whether fraud has been associated with the license . as the database grows , increasingly accurate analyses can be performed to match certain collateral documents to fraud ( or to certain types of fraud ). one type of analysis is correlation . a given enrollment document can be checked for its incidence of involvement in fraudulent and non - fraudulent license procurements . if the incidence for a particular type of collateral document deviates from the mean , use of that document can be regarded as being indicative of a higher , or lower , than normal possibility of fraud . consider a database of 10 million driver license records , with an overall fraud rate of 0 . 1 %. licenses for which a u . s . passport is presented as a collateral document may have an overall fraud rate of 0 . 02 %, whereas licenses for which a student id is presented as collateral may have an overall fraud rate of 1 %. the passport evidently has a relatively strong negative correlation with fraud , whereas the student id has a relatively strong positive correlation . the example just given is a relatively simple one . much more sophisticated analyses can be conducted . one class of powerful analysis techniques is known as factor analysis . such techniques consider a broad range of input variables , and assess their contributions ( both individually and in conjunction with other input variables ) to different results ( e . g ., incidence of fraudulent licenses , and arrests for speeding ). ( a number of books treat the subject in depth . a popular text is kim et al , “ factor analysis ,” sage publications , 1979 .) by applying factor analysis , outcomes contrary to the results given above may be discovered . for example , factor analysis may reveal that u . s . passports are positively correlated with fraud , if presented with a naturalization certificate as a companion collateral document . likewise , student ids may be found to be negatively correlated with fraud , if accompanied by an affidavit of an accompanying parent . factor analysis is an exercise in matrix mathematics and statistics . another analytic technique relies on neural networks and so - called “ fuzzy logic .” these techniques look for patterns in data that might look random on casual inspection . the foregoing techniques , and others , are used in the field of “ data mining ,” for which many different software tools are available . the artisan is presumed to be familiar with such art . the results of such analyses can be used in various ways . in one , state employees who issue driver &# 39 ; s licenses are provided with the results , and instructed to spend relatively more or less time questioning an applicant depending on whether the collateral documents suggest ( or not ) an increased likelihood of fraud . thus , personnel resources are deployed in a manner giving them a heightened fraud - fighting effect — with more of their time spent on cases where fraud is more likely . the employees can be provided with the result data in tabular form ( e . g ., a listing of collateral documents that may trigger more or less scrutiny of an applicant ), or the employee can be guided by a computer tool . in this latter arrangement , the user interface through which the employee annotates the dmv database record with information about the collateral documents used , can respond to the employee — based on the particular pair ( or triple ) of documents offered — and offer guidance as to actions the employee should , or needn &# 39 ; t , take . such arrangements may be regarded as expert systems — with expertise in reducing issuance of fraudulent licenses . in one embodiment , the computer system computes a score that ranks the applicant , based on the forms of collateral id produced . ( as noted below , other variables may also factor into this computation .) the score thus serves as a figure of merit for the reliability of the collateral identification , on which different actions can depend . consider a scoring system that yields an average value of 100 , with higher reliability scores indicating less likelihood of fraud . persons scoring between 95 and 105 may be given a regular degree of scrutiny . persons scoring less than 95 may be given progressively increasing amounts of scrutiny . for example , if the collateral documents indicate a reliability score of 93 , the dmv official may seek to corroborate identity by a fast , inexpensive , check . an example may be consulting a telephone directory database , to confirm that the name and address given to the dmv are consistent with information maintained by the local telephone company in their service records . if the reliability score is 90 or less , the dmv official may seek to corroborate identity by a slower , perhaps more expensive check . for example , the official may solicit the applicant &# 39 ; s social security number ( if this isn &# 39 ; t routinely provided as part of the enrollment process ). when typed by the official into the dmv computer system , the system can check a federal social security database to confirm that a person by applicant &# 39 ; s name was issued that social security number . for a score of 87 or less , both the telephone directory and social security check may be utilized . for a score of 84 or less , a still more rigorous check may be performed . for example , identifying information ( e . g ., name , address , social security number ) can be passed to credit reporting agency , which responds with a credit report or credit score ( e . g ., fico , an acronym for fair isaac & amp ; company ). this report is examined for consistency with the identifying information provided to the dmv official . for a score of 81 or less , the foregoing checks may all be conducted and , in addition , the person &# 39 ; s identifying information may be checked against local , state , and / or federal law enforcement databases . ( the scoring thresholds at which more rigorous verification is undertaken are , in the examples above , uniformly - spaced scores . more likely , these would be statistically - based brackets , e . g ., based on standard deviation .). these verification checks noted above needn &# 39 ; t be manually initiated or conducted by the dmv employee ; they can be undertaken automatically by a computer system . in some cases , the reliability score that triggers the checks isn &# 39 ; t even provided to the dmv employee . the foregoing are just a few examples of a great many verification procedures that may be performed . some verification procedures may be tied to the particular forms of collateral identification offered by the applicant . for example , if a military discharge certificate is offered , the dmv computer may consult with a federal database containing military service records to confirm that a person with applicant &# 39 ; s name and birth date served in the military . ( if additional data is captured from the collateral document — such as the discharge date — this information can be checked , also , against the military database .). likewise , if a w - 2 tax form is presented , applicant &# 39 ; s employment with the stated employer can be checked through on - line employment - reporting databases , such as a credit reporting bureau . ( again , to fully exploit such resources , it may be desirable to capture information from the collateral documents other than confirming applicant &# 39 ; s name , and optionally address . in the w - 2 case , the name of the employer could be captured and logged in the dmv database record , and used as an additional item for verification .). if the applicant passes the applicable verification checks , the license may be issued in the normal way . if one or more checks gives anomalous or conflicting results , different action may be taken . if the telephone directory search reveals no listing , the applicant may plausibly explain that they just moved into the jurisdiction and don &# 39 ; t yet have telephone service . in such case , the protocol may involve attempting a different form of verification , such as the social security number check . or the protocol may require the dmv official to solicit a third ( or fourth ) item of acceptable identification . a new reliability score can then be determined based on the enlarged set of collateral documents . if it still falls below 95 , other checks can be run ( e . g ., the social security database check ). if such other check ( s ) gives no cause for further suspicion , the driver &# 39 ; s license can be issued in the normal course . some verifications may suggest that a fraud is being attempted , or that a crime has been committed . for example , consider an applicant who presents a savings account passbook as an element of identification . if the expert system — on considering the ensemble of proffered collateral documents — determines a reliability score of less than 90 , the system may suggest that the official check ( or the computer may itself check ) with the issuing bank to confirm that the passbook is valid . on checking its records , the bank may report that the passbook was stolen during a house break - in . such a circumstance can trigger different responses . one is for the dmv official to advise the applicant that the computer system has flagged the application for further screening , and invite the applicant to return to the dmv office on the next business day to complete the process . ( optionally , the official may be requested to surreptitiously observe the applicant &# 39 ; s vehicle as they depart the parking lot , and enter a description of the vehicle and / or license plate information .) another response is to electronically send an alert to an appropriate law enforcement official while the person is at the counter , or to send the complete dossier of information collected from the applicant ( either in real - time or later , e . g ., overnight ). another response is to issue a license . although counter - intuitive , this step may be desirable from a law enforcement standpoint , e . g ., transforming an attempted fraudulent procurement of a license into an actual fraudulent procurement offense . the issued license may be marked so as to indicate , to authorized inspectors such as law enforcement , that is it suspect ( e . g ., a different color background may be printed behind the facial portrait , or a bar code or watermark formed on the license may convey such an alert , etc ). or a conventional license may be issued , and remedial steps can be taken later to recover same ( e . g ., seizure , at the time of arrest for the suspected offense ). naturally , frauds discovered through such checking in the enrollment process should be logged in the database so as to enhance the information on which the expert system decisions are based . in some embodiments , frauds noted in the database may be given a confidence score . a fraud that is established through a court decision may be given a high confidence score . a fraud that is suspected but never verified ( e . g ., ambiguous verification results , with the applicant asked to return the next day but never returning ) may be given a lower confidence score . again , this confidence measure is another variable that can figure into the expert system data analysis . desirably , a rich set of data relating to each examination / verification procedure is collected and added to the dmv database . this information will allow even more accurate reliability scoring to be determined in the future . that is , the reliability scoring can be based not just on the two or three types of collateral identification documents presented at the time of application , but can also be a function of the results of various verification procedures . and over the course of months , as reports of fraud are added to the database , the relevance of certain verification factors can change from obscure to clear . with sufficient experience , for example , the expert system may discern that certain verification check results — in combination with certain other circumstances ( e . g ., in combination with certain types of collateral documents )— may substantially change the statistical likelihood of fraud . consider the applicant with an initial reliability score ( based on the collateral documents alone ) of 93 . a telephone directory check is made . the check does not confirm applicant &# 39 ; s information . the failure of this check can now be added into the set of data on which the system computes the score , yielding a modified score . the modified score — since it is a function of a richer set of input variables — permits more accurate categorization of the fraud risk . based on analysis of historical data , for example , the system may advise that failure of the telephone directory check changed the applicant &# 39 ; s score from a 93 to an 84 . this steep drop in score may be because the applicant used a corporate id and a vehicle registration as collateral ids , and these have historically been found to be associated with fraud in contexts where the telephone directory check is failed . ( in contrast , if the applicant presented a pilot &# 39 ; s license and a u . s . passport as collateral documents , the score might have only fallen to a 91 , again based on historical patterns of experience .). thus , results from verification checks can be used as additional factors in assessing fraud risk . still other factors can be introduced into the assessment . one is credit history or scoring ( e . g ., fico score ). in the enrollment process , the dmv system may automatically solicit a fico score from one or more of the credit reporting agencies ( e . g ., equifax , experian , transunion ). if historical data stored in the dmv database includes such information for a meaningful number of prior applicants , the role of such a score as a factor in fraud can be determined , and used in establishing a reliability score for the application . ( if the historical data is insufficient to do a rigorous analysis , then the fico score might be used as a simple “ plus ” or “ minus ” factor . thus , the reliability score of an applicant with a fico score of more than 700 ( on a scale extending to 850 ) may be increased by 2 . the score of an applicant with a fico score of below 450 may be decreased by 2 .). another factor that can be included in assessing the reliability score is the applicant &# 39 ; s age . historical data compiled in the database may establish that applicants of different ages have different incidences of fraud . again , the role of age as a factor in fraud can be mined from the data , and used as another variable in determining the reliability score . ( or , again , it can serve as a simple “ plus ” or “ minus ” factor , e . g ., if the applicant is below 23 years old , his score is reduced by 2 ; if between 23 and 26 his score is reduced by 1 ; if over 70 his score is increased by 3 ; if over 80 his score is increased by 5 .). many other factors may also be utilized in such systems , subject to applicable legal considerations . these may include gender , zip code , type or model year of car , birthplace , marital status , etc ., etc . over time , a large set of data will be available in the data sources that are consulted to compute a reliability score ( i . e ., both the fraud database , and the ancillary verification sources ). to further increase accuracy , trends in the data over time can be used in rendering the expert advice . for example , the mexican consular card may have a relatively high historical incidence of fraud associated with it . however , further analysis may show that such fraud has dropped steeply in the past 9 months ( e . g ., due to redesign of the card , or re - working of the procedures for its issuance ). thus , in assessing risk , the historical high risk may be tempered with the better , recent , experience . this trend analysis can be performed in various ways . one is to assess the data patterns over different periods to discern any notable variance . thus , incidence of fraud may be computed over the life of the database ( e . g ., 0 . 2 %), over the past two years ( e . g ., 0 . 1 %), and over the past six months ( e . g ., 0 . 04 %). given these substantially different figures , the system can recognize that there is a shift underway in the statistics associated with this variable . thus , the system may depart from its usual protocol ( e . g ., using data from the past 2 years ), and instead use the average of the 2 year and the 6 month statistics ( i . e ., 0 . 07 % in the case just given ). in some cases , it may be appropriate to extrapolate a trend . this may be particularly prudent in cases where the risk appears to be rising . since the information in the database is necessarily from the past , a more accurate assessment of the current risk may be obtained by determining a trend curve , and estimating the current incidence of fraud by reference to that trend . consider a collateral document having fraud incidences over different window periods as follows : data window fraud incidence 12 - 9 months ago 0 . 1 % 9 - 6 months ago 0 . 13 % 6 - 3 months ago 0 . 17 % 3 months ago - current 0 . 22 % analysis of this data shows an exponential growth of about 30 % per quarter . this figure can be applied to the historical data to obtain an estimate of the risk today . for example , the mid - point of the most recent quarterly window is 1 . 5 months ago . applying 1 . 5 months of 30 % quarterly growth to the 0 . 22 % figure from the most recent quarter yields an estimate of 0 . 25 % today . ( more sophisticated analytic techniques take into account that the fraud data for the most current quarter is likely less comprehensive than that from successively older quarters , since less time has elapsed for fraud after issuance to be discovered and recorded ( e . g ., police stops and arrests ). thus , other techniques can apply trend analysis using a data confidence measure — relying more heavily on the data that has more indicia of reliability .). the foregoing procedures were described in the context of over the counter issuance systems . additional flexibility is available in central issue systems , since there is more time available to conduct verification checks . in a central issue system , the expert system can consider the applicant over a period spanning hours or days . many of the steps of the process may be performed at night , when database and bandwidth connection charges may be reduced . with the luxury of increased time , more comprehensive checking can be undertaken . again , each check provides more data by which the applicant &# 39 ; s reliability score can be further refined . if the process requires additional material from the applicant ( e . g ., a further piece of collateral identification ), a letter can be mailed soliciting the information . or a telephone solicitation can be made — either automated or by a human operator . the database containing fraud data , which is mined as described above , needn &# 39 ; t be limited to a single state . a larger sample set , and higher reliability results , may be obtained by using data from several states . this data can be assembled in a single database . alternatively , several separate databases may be maintained , and consulted individually for the information needed for the analysis . in some arrangements the reliability scoring can be performed by a public agency , such as a state department of motor vehicles . in others , the scoring can be performed by a private company — much like credit scores calculated by credit bureaus . another aspect of the technology involves tracking the contexts in which a particular driver &# 39 ; s license is used . for example , if a driver &# 39 ; s license is presented as a form of identification by a person cashing a check in new york , and the same license is presented an hour later by a traveler checking in for a flight at los angeles international airport , then something is amiss . desirably , a record is captured each time a license is presented in a commercial or identification transaction . the record may be generated in various ways . for example , the license can be imaged , and technology applied to read the data on the card ( e . g ., ocr can be used to “ read ” text and barcode data , while steganographically encoded data can be decoded by suitable image processing ). the license could also be swiped through a reader , that captured data from a magnetic stripe and / or optically encoded machine readable indicia on the card . the license information — together with place and time of presentment — can be forwarded to a database . the database can be maintained by the state that issued the driver &# 39 ; s license , or a centralized database can be used . ( such data capture is similar to the familiar practice used with credit cards , where every use is logged as to place and time .). it is possible that such data collection may be mandated by legislation , seeking to thereby enhance national security . alternatively , the data collection can be voluntary , with incentives provided to those who capture such data ( and / or to those who assent to such capture from their licenses ). for example , a vendor who captures license data from persons who pay a bill by check and offer a license as identification , may be given preferential commercial terms than a competing vendor who does not do so . thus , the former merchant may have less liability for accepting bad checks ( insufficient funds ), or may be charged a lower monthly account fee by the bank . similarly , the person presenting the license may be rewarded for allowing automated data capture . ( manual data capture is already widely accepted , e . g ., a supermarket clerk writing a driver &# 39 ; s license number on a check .) again , in the checking case , lower fees may be offered . alternatively , small cash rewards or other premiums may be available . once captured , analyses may be performed as to usage patterns for driver &# 39 ; s licenses . one outcome of such analysis is flagging inconsistent usage scenarios , such as the one noted above ( i . e ., physical presentment of the same license in new york and california within an hour ). when such inconsistent usage is detected , a responsive action can be taken ( such as denying boarding to an aircraft ). reference has been made to driver &# 39 ; s licenses and state authorities . however , it will be recognized that the technology isn &# 39 ; t so limited . other articles of identification can be made more secure by the methods described above . and the issuers of the identification needn &# 39 ; t be states — they can be other jurisdictions or entities , public or private .