Abstract:
A method, system, and medium are provided for detecting fraud, the method comprising obtaining a plurality of communication records associated with a communications device associated with a user over a fixed period of time, calculating a fraud metric for said records, comparing said metric to a threshold, if said metric exceeds said threshold, generating a fraud alert for said user.

Description:
SUMMARY 
     Embodiments of the invention are defined by the claims below, not this summary. A high-level overview of various aspects of the invention are provided here for that reason, to provide an overview of the disclosure, and to introduce a selection of concepts that are further described in the detailed-description section below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in isolation to determine the scope of the claimed subject matter. 
     At a high level, ways of detecting fraud in a telecommunications environment are provided. Differences in social patterns of fraudulent and nonfraudulent users are leveraged to detect likely fraudulent uses and fraudulent users. In another aspect, a system implements this method and generates fraud alerts for users determined to be fraudulent. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       Illustrative embodiments of the present invention are described in detail below with reference to the attached drawing figures, and wherein: 
         FIG. 1  depicts a set of communication records and the fields contained in each communication record in accordance with one embodiment of the present invention; 
         FIG. 2  depicts a flow diagram for a method for classifying users as fraudulent or nonfraudulent in accordance with one embodiment of the present invention; 
         FIG. 3  depicts a typical histogram for the distribution of values taken on by the fraud metric for a set of users in accordance with one embodiment of the present invention; 
         FIG. 4  depicts a typical histogram for the distance, in terms of standard deviations, of the distance from the mean value of the fraud metric for a set of users in accordance with another embodiment of the present invention; 
         FIG. 5  depicts the different distributions of one fraud metric for fraudulent and nonfraudulent users in accordance with an embodiment of the present invention; 
         FIG. 6  graphically depicts the trade-off between the fraction of fraudulent users correctly identified as fraudulent and the fraction of nonfraudulent users mistakenly identified as fraudulent; and 
         FIG. 7  depicts a flow diagram for a method for determining a threshold value of the fraud metric in accordance with one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The subject matter of embodiments of the present invention is described with specificity herein to meet statutory requirements. But the description itself is not intended to necessarily limit the scope of claims. Rather, the claimed subject matter might be embodied in other ways to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described. 
     Throughout this disclosure, several acronyms and shorthand notations are used to aid the understanding of certain concepts pertaining to the associated system and services. These acronyms and shorthand notations are intended to help provide an easy methodology of communicating the ideas expressed herein and are not meant to limit the scope of the present invention. The following is a list of these acronyms: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 BTS 
                 Base Transceiver Station 
               
               
                   
                 SMS 
                 Simple Message Service 
               
               
                   
                   
               
             
          
         
       
     
     Further, various technical terms are used throughout this description. An illustrative resource that fleshes out various aspects of these terms can be found in Newton&#39;s Telecom Dictionary by H. Newton, 24th Edition (2008). 
     Embodiments of the present invention may be embodied as, among other things: a method, system, or set of instructions embodied on one or more computer-readable media. Computer-readable media include both volatile and nonvolatile media, removable and nonremovable media, and contemplates media readable by a database, a switch, and various other network devices. By way of example, and not limitation, computer-readable media include media implemented in any method or technology for storing information. Examples of stored information include computer-useable instructions, data structures, program modules, and other data representations. Media examples include, but are not limited to information-delivery media, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile discs (DVD), holographic media or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage, and other magnetic storage devices. These technologies can store data momentarily, temporarily, or permanently. 
     Turning now to  FIG. 1 , an illustrative set of communication records  102  is presented. Each of communication records corresponds to a single communication. In one embodiment, these communications are exclusively voice calls. In another embodiment, these communications are SMS messages. In yet another embodiment, these communications include voice, SMS, and data communications. In one embodiment of the present invention, each communication record  102  contains an identifier for a user  104  associated with the communication and one or more fields describing various features of the communication. In one variant of this embodiment, these fields include a destination identifier  106  corresponding the other endpoint of the communication (i.e. the destination of an outgoing call or the origination of an incoming call), a start time  108  representing the time at which the communication was initiated, a duration  110  for the communication, and a BTS identifier  112  corresponding to the base transceiver station with which the mobile device communicated during the communication. Other embodiments may include more, fewer, or different fields in each communication record  102  without departing from the scope of the claims below. 
     Turning now to  FIG. 2 , a flow diagram is presented for a method of classifying a user as fraudulent or nonfraudulent in accordance with one embodiment of the present invention and referenced generally by the numeral  200 . In step  202 , all the communications records  102  for the user to be classified are extracted for the set of all communications records  102  for a fixed time period. In one embodiment, this time interval is a single day. In another embodiment, it is an entire billing period. 
     In step  204 , a feature is extracted from each communications record  102  and categorized as belonging to one of a plurality of categories. In an illustrative example, the feature is the duration of the call, and the categories are one-minute intervals; in another example, the feature is the destination identifier, and the categories are the distinct destination identifiers. Other features and other ways of assigning features to distinct categories are possible without departing from the scope of the present invention. 
     In step  206 , a fraud metric is calculated over the features extracted in step  204 . A variety of metrics are possible; in one embodiment, the metric is the number of distinct destination identifiers. In a more complex embodiment, the metric is the Shannon entropy (or similar) of the fraction of calls made to each destination identifier. This metric, denoted H(A) for a set of records A, is calculated in one embodiment as: 
                 H   ⁡     (   A   )       =     -       ∑     i   =   1     m     ⁢     [       (            B   i               A          )     ⁢       log   2     ⁡     (            B   i               A          )         ]           ,         
where B, is the set of all call records with destination identifier i of m, and in particular,
 
                   ∑   m       i   =   1       ⁢          B   i            =          A        .           
In yet another embodiment, the metric is the Shannon entropy of the fraction of total call time spent connected to each distinct destination identifier, calculated as:
 
                 H   ⁡     (   A   )       =     -       ∑     i   =   1     m     ⁢     [       (         ∑     j   =   1            B   i            ⁢     t   ⁡     (     b   ij     )             ∑     a   ∈   A               ⁢     t   ⁡     (   a   )           )     ⁢       log   2     (         ∑     j   =   1            B   i            ⁢     t   ⁡     (     b   ij     )             ∑     a   ∈   A               ⁢     t   ⁡     (   a   )           )       ]           ,         
where b ij  is the j th  call to destination identifier i of m, t(x) is the duration of call x,
 
                   ∑   m       i   =   1       ⁢          B   i            =        A              
as above, and B i ={b ij } j . Other fraud metrics are contemplated, and each of these possible metrics can be calculated over any of the features in communication records  102  without departing from the scope of the claims below.
 
     Once the fraud metric is calculated for the user to be categorized in step  206 , it is compared with a threshold value in step  208 . If the calculated value of the metric is greater than the threshold value, the user is categorized as fraudulent in step  210 ; otherwise the user is categorized as nonfraudulent in step  212 . 
     The choice of threshold value used in step  208  influences the accuracy of classifying users. The values of the fraud metrics can be distributed according to an approximately normal distribution.  FIG. 3  shows a histogram of the number  302  of users whose fraud metric value  304  falls into each bucket of a certain size for an exemplary data set. Also included is a visual depiction of the threshold value  306 . Threshold value  306  partitions the users in the data set into a set of users classified as nonfraudulent  308  and a set of users classified as fraudulent  310 . Increasing threshold value  306  will classify fewer users as fraudulent, while decreasing it will classify more users as fraudulent. 
     Turning now to  FIG. 4 , an alternate embodiment of method  200  is illustrated. For this embodiment, consider that a metric, such as the number of distinct destination identifiers, may increase for the general population on a holiday such as Christmas. However, the normal shape of the distribution will remain unchanged, though its mean and standard-deviation parameters may change. In embodied shown in  FIG. 4 , the values of the metric  402  and the threshold value  404  are not expressed in terms of an absolute value of the fraud metric (such as “calls to 30 distinct numbers”); but rather, in terms of the distance, in standard deviation values, from the mean value of the metric over all calculations for the same time period (such as “three standard deviations greater than the mean”).  FIG. 4  also has several components which correspond to those in  FIG. 3 : count  406  corresponds to count  302 , and the set of users classified as not-fraudulent  408  corresponds to set of users  308 , while the set of users classified as fraudulent  410  corresponds to the set of users  310 . 
     Turning now to  FIG. 5 , an illustration of the tradeoff between the false negative rate and the false positive rate is presented and referenced generally by the numeral  500 . The first curve  502  plots relative frequency  504  of fraud metric values  506  for the population of nonfraudulent users, and the second curve  508  represents the distribution of fraud metric values for fraudulent users for an exemplary data set. Note that fraud metric value  506  corresponds to fraud metric value  304 . Because these distributions can have significant overlap, generally, no selection of the threshold value  510  can classify users as fraudulent or nonfraudulent with perfect accuracy. Threshold  510  corresponds to threshold  306  and threshold  404 . 
     Threshold value  510  divides the populations of users into four regions. Region  512  represents those nonfraudulent users correctly classified as nonfraudulent. Region  514  represents those nonfraudulent users incorrectly classified as fraudulent (i.e., false positives). Region  516  represents those fraudulent users incorrectly classified as nonfraudulent (i.e., false negatives). Region  518  represents those fraudulent users correctly classified as fraudulent. Thus, increasing threshold  510  (i.e., moving it to the right) has the effect of increasing the size of region  512  at the expense of region  514 , and increasing the size of region  516  at the expense of region  518 ; decreasing the threshold (i.e., moving it to the left) has the opposite effects. 
     Turning now to  FIG. 6 , the tradeoff  500  is presented explicitly for the exemplary data set of  FIG. 5 . Curve  602  plots the fraction of fraudulent users correctly identified  604  (i.e., the size of region  518 ) against the fraction of nonfraudulent users incorrectly identified as fraudulent  606  (i.e., the size of region  514 ). Each point  608  on this curve corresponds to a particular value of threshold  510 . Thus, in the extreme cases, point  610  corresponds to having a false positive rate of 0%, at the expense of correctly identifying only 50% of fraudulent users, and point  612  corresponds to correctly identifying every fraudulent user, at the cost of a 50% false positive rate. Point  608  corresponds to an intermediate value of the threshold, as in the case of threshold  306 , threshold  404 , or threshold  510 . Of course, these figures are specific to the exemplary data set shown; other data sets will have their own extreme and optimum threshold values. 
     Turning now to  FIG. 7 , an illustrative method for determining a threshold fraud metric value in accordance with the present invention is presented. In step  702 , a first distribution of values for the fraud metric for a set of users known a priori to be nonfraudulent is calculated; this distribution corresponds to curve  502 . In one embodiment, this set of users is obtained by manually screening a subset of all users for fraudulent behavior. In another embodiment, it is obtained by using historical records for which no fraud complaints were received. 
     In step  704 , a second distribution of values for the fraud metric for a set of users known a priori to be fraudulent is calculated; this distribution corresponds to curve  508 . In one embodiment, this set of users is obtained from the set of users who have complained of fraud on their accounts; in another embodiment, it is obtained from the set of users who have been detected as fraudulent by another screening system. 
     In step  706 , an initial threshold is selected according to a heuristic policy. In one embodiment, the threshold is selected such that 95% of fraudulent users have a fraud metric value higher than the initial threshold. In another embodiment, it is selected such that 99% of nonfraudulent users have a fraud metric value lower than the initial threshold. Other heuristics are possible without departing from the scope of the claims below. 
     In step  708 , a fraud metric is calculated for a third set of users who are not in the first set or the second set. In one embodiment, this set of users is not known to be fraudulent and not known to be nonfraudulent. In another embodiment, this third set of users was selected and removed from the first set and the second set before steps  702  and  704 . In one variant of this embodiment, users selected from the first set and users selected from the second set are chosen in equal proportion. In another variant, users selected from the first set and users selected from the second set are chosen in proportion to the estimated proportion of users who are fraudulent and users who are nonfraudulent, respectively. In step  710 , the users of the third set are then classified as being fraudulent or nonfraudulent according to the initial threshold selected in step  706 . 
     In step  712 , feedback is obtained for the classifications made in step  710 . In one embodiment, this feedback is obtained by comparing the classifications made in step  710  to the set from which the users were selected, and generating a confusion matrix from the results. 
     In step  714 , the false positive rate is extracted from the confusion matrix generated in step  712  and compared to a maximum false positive rate. If the false positive rate exceeds the maximum false positive rate, the threshold is raised in step  716  and steps  710  et seq. are repeated. In one embodiment, the steps of raising the threshold and repeating are only performed if the false negative rate is not above a permissible false negative rate. 
     Otherwise, in step  718 , the false negative rate is extracted from the confusion matrix and compared to a maximum false negative rate. If the false negative rate exceeds a maximum false negative rate, the threshold is lowered in step  720  and steps  710  et seq. are repeated. In one embodiment, the steps of lowering the threshold and repeating are only performed if the false positive rate is not above a permissible false positive rate. In one embodiment, if it is determined that no threshold can simultaneously satisfy the maximum false positive rate and the maximum false negative rate, one or both of the maximum false positive rate and the maximum false negative rate are increased. In another embodiment, an alert is generated for manual intervention and adjustment of one or both of the maximum false positive rate and the maximum false negative rate. 
     Once a threshold is found which simultaneously satisfies the maximum false positive rate and the maximum false negative rate, the classifications are finalized in step  722 . In one embodiment, the method terminates at step  722 . In another embodiment, the final threshold is used as the initial threshold determined in step  706 , and the method continues to classify another set of users beginning from that point. 
     Many different arrangements of the various components depicted, as well as components not shown, are possible without departing from the scope of the claims below. Embodiments of our technology have been described with the intent to be illustrative rather than restrictive. Alternative embodiments will become apparent to readers of this disclosure after and because of reading it. Alternative means of implementing the aforementioned can be completed without departing from the scope of the claims below. Certain features and subcombinations are of utility and may be employed without reference to other features and subcombinations and are contemplated within the scope of the claims.