Abstract:
A system for detecting fraudulent transactions is disclosed. The system breaks the transaction down into its component parameters. A first score is computed depending on the propensity of the transacted commodity to be involved in fraud. A second score is computed as a function of the authentication of the remaining parameters of the transaction. A total fraud score is computed from the first score and the second score and compared against a fraud threshold to determine the likelihood of the transaction being fraudulent.

Description:
“This application is a division of application Ser. No. 09/535,298, filed Mar. 24, 2000, (pending), which is hereby incorporated by reference herein.” 
    
    
     BACKGROUND 
     Everyday, millions of commercial transactions take place between customers and vendors of goods and/or services (“commodities”). Many of these transactions are consummated face to face in physical retail establishments, over the telephone, such as with catalog based vendors, through the Internet with e-commerce based vendors or through some combination thereof. 
     Each of these transactions involves the exchange of value, such as cash/currency, bank draft/check or credit, for commodities. One other commonality that all of these transactions share is the potential that the customer making the transaction is acting fraudulently to acquire the commodities from the vendor. 
     Fraud often occurs when the customer knowingly utilizes some form of value, such as a bank draft/check or credit card that has no value or does not belong to the customer. For example, a customer writes a check knowing there are no funds to back up that check or utilizes a fraudulently obtained or stolen credit card. While fraudulent transactions only make up a small percentage of the total number of transactions completed at any given time, the amount of revenue and/or resources lost due to fraud is substantial. 
     Vendors of goods and services rely on a number of different methods to detect fraudulent transactions. Some rely on the fraud detection systems of third party credit card processors or third party check verification systems to determine if the customer is trying to commit fraud. Other vendors rely on internal fraud detection systems. In either case, it is preferable to detect the fraudulent transaction before the customer receives the commodities, prevent the loss to the vendor in the first place and reduce or eliminate the expense of resources devoted to attempting to recover those commodities and/or financial compensation. 
     An important characteristic of these fraud detection systems is their error rate. Errors include false negative responses, where a fraudulent transaction goes undetected and false positive responses, where valid transactions are mis-flagged as fraudulent. A high false negative rate indicates that the system is not performing its intended function and results in continued losses to the vendor due to fraud. A high false positive rate costs the vendor in potentially lost sales and lost resources in further investigating and validating the mis-flagged transactions. 
     One typical fraud detection system functions by analyzing parameters of the transaction and attempting to identify characteristics which indicate that the transaction is fraudulent. This system breaks down the transaction into a few select component data parameters and single variable relationships. Points are assigned to each parameter/relationship based on the information it represents and whether it matches with known data. If the number of points of any one parameter/relationship exceeds a pre-determined threshold, potential fraud is indicated. Alternatively, the points of all of the parameters/relationships are summed to create a total score and then that total score is compared to a threshold. If the total score exceeds the threshold, fraudulent activity may be indicated within the transaction. An exemplary fraud detection system utilizing single parameters or relationships to identify a primary fraud characteristic is shown in more detail in U.S. Pat. No. 6,029,154, entitled “METHOD AND SYSTEM FOR DETECTING FRAUD IN A CREDIT CARD TRANSACTION OVER THE INTERNET” to Pettitt. 
     Another fraud detection system utilizes a more complex predictive model, such as a neural network or other form of model which utilizes self-learning of relationships among variables from historical transaction data. This system uses these complex models to analyze the transaction and predict whether or not the transaction is potentially fraudulent. The model is able to automatically correlate relationships among all of the parameters of the transaction to each other, and not just the single variable relationships of the above detection system. An exemplary fraud detection system utilizing these self-learning models is shown in more detail in U.S. Pat. No. 5,819,226, entitled “FRAUD DETECTION USING PREDICTIVE MODELING” to Gopinathan, et al. Self-learning based systems are complex, difficult to develop and require significant training and maintenance to maintain accuracy. 
     SUMMARY 
     The present invention is defined by the following claims, and nothing in this section should be taken as a limitation on those claims. By way of introduction, the preferred embodiments described below relate to a method for detecting fraudulent transactions between a customer and a vendor. The method includes receiving a plurality of transaction parameters from the vendor, where the plurality of transaction parameters represents at least one transaction for at least one commodity between the customer and the vendor. A first score is computed as a function of each commodity involved in the transaction. A second score is computed as a function of each of a first one or more of the plurality of transaction parameters. A fraud score is computed based on the first and second scores. The transaction is indicated to be potentially fraudulent if the fraud score exceeds a first pre-determined threshold. 
     The preferred embodiments further relate to a fraud detection processor. The processor includes a receiver operative to receive a plurality of transaction parameters from a vendor, where the plurality of transaction parameters represents at least one transaction for one or more commodities between a customer and the vendor. The processor also includes a first score processor coupled with the receiver and operative to compute a first score as a function of each of the one or more commodities. In addition, the processor includes a second score processor coupled with the receiver and operative to compute a second score as a function of each of a first one or more of the plurality of transaction parameters. Further, the processor includes a fraud score processor coupled with the first and second score processors and operative to compute a fraud score based on the first and second scores. Finally, the processor includes fraud determination logic coupled with the fraud score processor and operative to compute a determination of whether the at least one transaction is potentially fraudulent based on a comparison of the fraud score and a first pre-determined threshold and further operative to indicate this determination to the vendor. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 depicts a block diagram of an exemplary commercial transaction utilizing the preferred fraud detection system. 
     FIGS. 2A &amp; 2B depicts a flow chart detailing a first embodiment of a preferred fraud detection system. 
     FIGS. 3A &amp; 3B depicts a flow chart detailing a second embodiment of a preferred fraud detection system. 
    
    
     DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS 
     The disclosed fraud detection system is easily maintained and updated and has a low error rate. Further, the disclosed system is modular, being able to be sold and/or provided, in whole or in part, as a service or product to one or more vendors. It will be appreciated that the system, as a service or product, may also be embodied as hardware and/or software and packaged, in whole or in part, as a fraud detection processor. The preferred fraud detection system or processor is comprised of a number of component parts as described below. One or more components of the system or processor may be deployed as part of a vendor&#39;s internal transaction processing system while the remainder of the system or processor is operated by an entity, external to, and in communication with the vendor. 
     In one embodiment, the entire system resides internally as a part of, or coupled with, the vendor&#39;s order processing system in compliance with all of its interfaces and gateways. Herein, the phrase “coupled with” is defined to mean directly connected to or indirectly connected with through one or more intermediate components. Such intermediate components may include both hardware and software based components. 
     Alternatively, most of the fraud detection processing is offloaded to a consumer information provider. Where the consumer information provider is an entity external to the vendor, costs, maintenance, storage and processing considerations are alleviated for the vendor. In addition, centralization of the fraud detection process allows multiple vendors to pool information resources thereby enhancing the detection of fraud. 
     Further, in yet another alternative embodiment, the entire system resides externally to the vendor and in communication with the vendor&#39;s order processing system and is provided, as a “black box” service for example, by the external entity (i.e. transaction parameters are transmitted to the external entity and the external entity returns a fraud indication result). It will be appreciated that the alternative preferred embodiments disclosed herein relate to alternate divisions of the system&#39;s components between the vendor and the external fraud processing entity and that all embodiments of the disclosed fraud detection system are contemplated no matter how the division of its component parts is made. 
     To support multiple vendors, the system is configurable to account for the varying transactional characteristics of each participating vendor, allows each participating vendor the ability to individually re-configure the system to reduce error rates and permits each participating vendor to provide internal statistical, demographic and historical transaction data and/or have access to such data from other vendors (participating or not) to enhance the detection of fraud. 
     FIG. 1 shows an exemplary transaction  100  between a customer  102  and a vendor  106 . The transaction  100  comprises the purchase of a commodity  112  from the vendor  106 . Herein, a commodity  112  is a good and/or service or anything else purchased or traded for value. The customer  102  desires the commodities  112  and the vendor  106  provides the commodities  112 . In one embodiment, the customer  102  is another vendor. 
     To complete the transaction  100 , the customer  102  interfaces with the order processing component  104  of the vendor  106 , orders  110  a commodity  112  and provides payment information. The vendor  106  processes the order  110 , accepts the payment and delivers  114  the commodity  112  to the customer  102 . 
     The transaction  100  can occur in one or more of many different environments. For example, the transaction  100  occurs in a retail establishment between a customer  102  and a cashier (the order processor  104 ) working for the vendor  106 . Alternatively, the transaction occurs between a customer  102  and a catalog based vendor  106  using a telephone order processing system  104  (live operator, automated or a combination thereof) or between a customer  102  and an electronic commerce (“e-commerce”) based vendor  106  using an Internet, network or wireless based order processing system  104 . The order processor  104  may even be a vending machine, including a machine used to dispense gasoline. The order processing system  104  may be manual, automated or a combination thereof. 
     Where the customer  102  attempts to use a credit form of payment, such as a credit card, debit card or bank draft/check, there is an opportunity for defrauding the vendor  106 . In such cases, it is preferable to detect such fraudulent activity before the commodity  112  is delivered to the customer  102 . Early detection prevents loss of revenue or resources by the vendor  106 . 
     FIG. 1 further shows a fraud detection system  108  which interfaces with the order processing system  104  of the vendor  106  in order to detect fraudulent activity on the part of the customer  102 . The system  108  is capable of determining whether or not the transaction  100  is potentially fraudulent while the transaction  100  is still pending, before the commodities  112  have been delivered to the customer  102 . Alternatively, the system  108  is capable of analyzing the transaction  100  at a later time to determine whether the payment was potentially fraudulent. Both real-time processing and batch processing are contemplated. 
     The fraud detection system  108  receives transaction parameters  116  from the vendor&#39;s  106  order processing system  104 . The transaction parameters  116  represent the pieces of information which make up the transaction  100 . Exemplary transaction parameters  116  include the customer&#39;s name, address and telephone number, the identity of the commodity being transacted (also known as a “Stock Keeping Unit” or “SKU” number) and the information related to the form of payment being used such as a credit card number or checking account number. A SKU is unique identifier code which identifies a particular commodity. Other parameters  116  may include an alternate shipping address to account for a customer  102  who places an order and has it shipped to an alternate address, such as for a gift. 
     These transaction parameters  116  may be vendor  106  specific or generic to all vendors  106 . Some transaction parameters  116  are based on the nature of the order processing system&#39;s  104  requirements to process a given order. For example, a vendor  106  which transacts in products which are not delivered (such as a service), may not collect a shipping address and therefore would not collect this particular transaction parameter  116 . Further, e-commerce based vendors  106  may have access to additional parameters  116  such as Internet Protocol addresses, domain addresses or electronic mail addresses. The preferred embodiments disclosed herein are designed to account for the different transaction parameters  116  collected by or available from different vendors  106  and are capable of being configured to account for the availability or unavailability of transaction parameters  116  in the determination of fraud. Table 1.0 (See below) gives a non-exhaustive list of the transaction parameters  116  of the preferred embodiments. 
     The fraud detection system  108  accepts the transaction parameters  116  from the order processing system  104  and analyzes those parameters  116 , as discussed below in more detail. The fraud detection system  108  provides a result or signal  118  to the order processing system  104  signifying whether or not the analyzed transaction parameters  116  indicate that the transaction  100  is likely fraudulent. In one embodiment the result or signal  118  is a binary indicator of fraud or no fraud. Alternatively, the result or signal  118  may be numeric or other data indicating the likelihood of fraud, for example, as compared to a fraudulent value scale. The order processing system  104  is then able to account for that result or signal  118  in the processing of the transaction  100 . If the transaction  100  is still pending, the order processing system  104  may pass off the transaction  100  to a manual or automated investigation system, terminate the transaction  100  before the commodities  112  are shipped or delivered  114  or demand further information or an alternative form of payment from the customer before shipping or delivering the commodities  112 . Alternatively, the order processing system  104  may utilize internal fraud detection systems to double-check or override the fraud indication signal  118  from the fraud detection system  108 . If the transaction  100  has already been completed and the commodities  112  have been shipped to the customer  102 , the fraud indicator signal  118  may be used by the order processing system  104  to institute loss mitigation or recovery measures such as recalling the shipped commodities  112  back from the shipping agent or handing over the transaction to an internal or external investigation/collections entity. Transactions  100  determined to be potentially fraudulent or non-fraudulent may also be updated into the appropriate negative (described in more detail below and in FIG. 2B) or positive database (described in more detail below and in FIG. 2A) for use in processing and preventing losses from later transactions  100  by the same customer  102 . 
     First Embodiment 
     Referring now to FIG. 2A, there is shown a flow chart depicting a first embodiment of a fraud detection system  108 . Internally to the vendor  106 , the order processing system  104  receives a transaction  100  and initiates the fraud detection process. The fraud detection system  108  receives initial transaction parameters  116  from the order processing system  104  (Block  202 ). 
     The system performs a payment authorization check (Block  204 ). This check involves contacting either an external credit card issuing company or third party check verification service. Alternatively, internal checks are performed for vendor issued credit accounts. The external issuing company or authorization service performs an authorization process on the transaction  100 , as is known in the art. This authorization process typically catches stolen credit cards or checks which have already been reported to the issuing company. Further, the authorization process indicates whether the customer has exceeded a credit limit or available funds. 
     Alternatively, for credit card transactions, the authorization check includes Address Verification checking (“AVS”), Card Verification Value Checking (“CVV 2 ”), other fraud detection processes now or later developed by credit card issuing or authorization companies, or combinations thereof. AVS checking performs an additional check, beyond verifying funds and credit card status, to ensure that elements of the address supplied by the customer  102  match those on record with the issuing company. AVS checks can return results indicating that data is unavailable, or that there is a mismatch or a total or partial match. AVS checking can be repeated with manually tweaked addresses and/or names to correct misspellings, incorrectly specified billing addresses, and other customer service/order entry mistakes. 
     CVV 2  checking determines whether or not the customer  102  is actually in possession of the physical credit card by asking for an identification code imprinted only on the card in a manner which does not get reproduced by credit card imprinting or swipe machines. If the authorization check fails, the transaction is stopped immediately. Otherwise, the processing further continues. However, customers who commit fraud often utilize methods which avoid being detected by standard payment authorization techniques. For example, the customer  102  intercepts credit cards along with the associated address information, in the mail and uses the credit cards before the cards are reported stolen to the issuing company. Further, these standard payment authorization techniques may fail to work with internationally originated transactions  100 . 
     The transaction  100  is also checked to see if the value of the commodities  112  being purchased is greater than a pre-determined threshold amount  208  (Block  206 ). A vendor  106  may determine that transactions  100  whose total value is less a particular threshold are less likely to be fraudulent. Low value transactions  100  are bypassed from further fraud processing to avoid wasting resources, and the transaction  100  is accepted (Block  230 ). The pre-determined threshold  208  is vendor  106  specific or generalized for multiple vendors  106  and vendors  106  may adjust their threshold  208  periodically. Each vendor  106  may experience more or less fraud for commodities with differing values. In addition, the pre-determined threshold may be dynamically adjusted to account for other transaction parameters  116  and the results of other fraud detection algorithms (as discussed below). An exemplary pre-determined threshold value is $100.00. 
     If the value is greater than the pre-determined transaction amount threshold, transaction parameters  116  of the transaction  100  are compared against a positive database  212  (Block  210 ). The positive database  212  maintains customer  102  identity information, including one or more of the transaction parameters from past transactions which have been determined to be non-fraudulent. Such customer identity information may include the customer name, credit card number, checking account number, address or combinations thereof. This database  212  accounts for customers  102  who have a known good relationship with a vendor  106  but often have their transactions  100  flagged as fraudulent by fraud detection system  108  for other reasons such as a high transaction amount, shipping to a high risk zip code or an unusually high frequency of orders (“velocity”). As the nature of such positive information is likely highly confidential, such positive databases  212  and comparisons (Block  210 ) are preferably completed internally to said vendor  106 . In addition, this positive data tends to be vendor specific, i.e. just because a customer has not committed fraud at one vendor does not necessarily mean they are not likely to commit fraud at another vendor. If the current customer  102  is found in the positive database  212 , the transaction  100  is bypassed from further processing, and the transaction accepted (Block  230 ). Otherwise the fraud detection system continues its analysis (Block  214 ). In alternative embodiments, the positive identification is used to decrease the likelihood of a false positive result from the fraud detection system. 
     In order to further analyze the parameters  116 , the form of payment which the customer  102  is using is determined (Blocks  218 ,  220 ). Credit card based transactions  100  have different parameters  116  than bank draft or check based transactions  100 . In alternative embodiments, the transaction  100  environment is determined, i.e. whether the transaction  100  is originating from a retail establishment, a telephone ordering vendor or an e-commerce based vendor. 
     In one embodiment, the transaction parameters  116  are formatted and transmitted to a consumer information provider  224  who completes the fraud detection process (Block  222 ). The consumer information provider  224  is preferably an entity external to the vendor  106  and is preferably a fraud detection service provider who provides fraud detection services to multiple vendors  106 . Alternatively, the consumer information provider  224  is another vendor  106  which sells excess fraud processing capacity as a service to other vendors  106  or the consumer information provider  224  is an internal part of the vendor  106  (e.g. part of or coupled with the order processing system  104 ). The consumer information provider  224  returns a signal  118  indicating whether or not the transaction  100  is likely fraudulent (Block  226 ). If the transaction is likely non-fraudulent (Block  228 ), the transaction is accepted (Block  230 ) and the commodities  112  are delivered. If the transaction  100  is likely fraudulent (Block  228 ), the system performs the desired vendor specific action to combat this potential fraud (Block  232 ). For example, the system cancels the transaction and prevents delivery of the commodities  112  to the customer  102 , or the transaction is flagged for further review. 
     Referring now to FIG. 2B, there is shown a flow chart depicting the fraud detection processing undertaken by the consumer information provider  224 . The consumer information provider  224  receives the request to determine the fraudulent status of a transaction  100  and the accompanying transaction parameters from the vendor  106  (Block  302 ). This request is transmitted by telephone, network, Internet or other communication medium. Initially, the transaction  100  is assumed to be non-fraudulent and subsequent processing determines if the transaction is likely fraudulent (Block  304 ). In alternative embodiments, the initial assumption is that the transaction is likely fraudulent and subsequent processing attempts to disprove this. 
     The consumer information provider  224  first computes a fraud multiplier for the transaction  100  (Block  306 ). The fraud multiplier is a score based on the value of the transaction parameters  116  and whether the transaction parameters authenticate against databases  308 ,  310 ,  312  of known information. These databases  308 ,  310 ,  312  include a customer information database  308 , a negative account database  310  and a negative address database  312 . Generally, these databases  308 ,  310 ,  312  contain specific customer and consumer information, statistical data and historical transaction data known to the participating vendors  106 ,  316 ,  318 . 
     The customer information database  308  is a collection of information about the customers  102  of each of the vendors  106 ,  316 ,  318 . There will be a separate database  308  for each vendor  106 ,  316 ,  318  since such customer information is typically only relevant to the particular vendor that the customer belongs to. Alternatively, by collecting the information from each participating vendor  106 ,  316 ,  318  together, a broad database is provided to all of the vendors  106 ,  316 ,  318 , enhancing the accuracy of the fraud detection system and reducing error rates. Further, where the consumer information provider  224  is an internal function of the vendor  106 , or due to privacy issues, this data may represent only one vendor&#39;s customer information. The customer information database  308  preferably includes data representing information about existing customers such as the customer name, address, how long the customer has been a customer and information about one or more of their past transactions. In the e-commerce environment, this database  308  may further contain information such as the customer&#39;s Internet Protocol Address, e-mail address or domain address. 
     The negative account database  310  and the negative address database  312  include information about past transactions that are known to have been fraudulent. The negative account database  310  includes credit card account numbers and/or checking account numbers used in past fraudulent transactions. The negative address database  312  includes customer, card holder, or shipping addresses previously involved in fraudulent transactions. The data in the databases  310 ,  312  is provided by the participating vendors  106 ,  316 ,  318 , financial institutions or other entities. The negative databases  310 ,  312  are provided as a recognition of the fact that a fraud committed or attempted against one vendor  106 ,  316 ,  318  is generally relevant information to all vendors  106 ,  316 ,  318 . In this way, one vendor  106 ,  316 ,  318  benefits from the negative experience of another vendor  106 ,  316 ,  318 . 
     The databases  308 ,  310 ,  312  may further include the positive databases  212  used in Block  210  of FIG. 2A so that this comparison may be performed by the fraud detection system  108 . Alternatively, each participating vendor  106 ,  316 ,  318  has access to the other vendors&#39; positive transaction history data where it is relevant across vendors  106 ,  316 ,  318 . Where positive databases are included, the positive data is used by the fraud detection system  108  as a factor in the fraud multiplier computation and not to override the fraud determination process as is shown in FIG.  2 A. Alternatively, where such positive data is relevant across vendors, it may be used to bypass further fraud processing and approve the transaction. Additional databases comprising publicly available data such as credit histories or bankruptcy information, whether internal to the fraud detection system or provided by third party entities, may also be used to augment one or more of the databases  308 ,  310 ,  312 . Additional databases containing non-public information such as proprietary internal data from other vendors or other private entities, whether internally provided or provided by a third party entity, may also be included. 
     In addition to the above databases  308 ,  310 ,  312 , a fraud multiplier point database  314  is also provided. The fraud multiplier point database  314  includes data representing the transaction parameters  116  that a participating vendor  106  provides with each fraud determination request. This data may be different or specific for each participating vendor  106 ,  316 ,  318  depending upon each vendors  106  transaction environment and fraud experience. In one embodiment, the data is stored in a common database  314  or alternatively, each vendor  106 ,  316 ,  318  may have their own database  314 . For example, the customer status transaction parameter can be defined by each vendor  106 ,  316 ,  318 . For vendors  106 ,  316 ,  318  that sell exclusive memberships but still allow non-members to transact, the customer status parameter may indicate a member or non-member status. Alternatively, the customer status parameter indicates a preferred status or how good of a customer that vendor  106 ,  316 ,  318  feels the customer is, poor, fair, good or excellent depending on the amount they buy or their frequency of returns, etc. For each transaction parameter  116 , a corresponding point value is provided which represents the number of points to be computed into the fraud calculation (described in more detail below) if that particular transaction parameter authenticates or doesn&#39;t authenticate in one of the databases  308 ,  310 ,  312 . The point values are determined at the discretion of each vendor  106  depending on their individual business environments and point values for the same transaction parameters  116  may vary from vendor  106  to vendor  106 . For example, some vendors  106 ,  316 ,  318  may experience more fraud from particular geographic areas than other vendors  106 ,  316 ,  318 . These vendors  106 ,  316 ,  318  then may assign higher point values to address matches with the particular zip codes covering those geographic areas then might be assigned by another vendor  106 ,  316 ,  318 . 
     Some transaction parameters  116  are essentially Boolean variables which have a True value if the value of the parameter can be authenticated against one of the databases  308 ,  310 ,  312 . In this case, the parameter  116  is assigned points based on whether it is true or false. In addition, some transaction parameters  116  are assigned points based on the value of that parameter  116 . For example, the transaction parameter  116  representing the total value of the transaction  100  is assigned a specific number of points as a function of this value (e.g. 0.5 points per $100 of value). 
     Alternatively, the points assigned to a particular transaction parameter  116  are dependent on the values of one or more other transaction parameters. For example, a transaction parameter  116  representing the credit card number velocity is assigned points based on the frequency of use of that credit card number (itself another transaction parameter  116 ). A velocity is a frequency of use over a specified period of time or a specified number of transactions. Velocity checks are done for transaction  100  counts (“count velocity”) as well as amounts (“amount velocity”), i.e. the frequency of transactions  100  of a particular value. The velocity checks are performed against customer  102  identities, credit card account numbers, checking account numbers and addresses, cardholder, ship-to or otherwise (e.g., the frequency of address changes). Other relationships can also be captured by the point assignments. For example, depending on the value of one or more transaction parameters  116 , one or more other transaction parameters  116  may be further computed with a weighting to increase or decrease significance to the overall fraud determination. Generally, points or point and parameter relationships are assigned and/or computed for a particular transaction parameter  116  as a function of that parameter&#39;s  116  relationship to fraudulent transactions. (i.e. whether a particular value of a parameter  116 , or relationship among parameters  116 , is likely to indicate fraud or not). For example, in one embodiment, the point values for the transaction velocity parameter  116  are adjusted based on geographic dispersion of the locations from which each of the transactions, included in the velocity calculation, were initiated. This accounts for the likelihood of the customer  102  physically being present at all of those locations at the times when the transactions  100  were initiated. An exemplary fraud multiplier point database  314 , corresponding transaction parameters  116  and points for a particular vendor  106 , is shown in Table 1.0. 
     
       
         
               
               
             
           
               
                 TABLE 1.0 
               
               
                   
               
               
                 TRANSACTION PARAMETER 
                 POINTS 
               
               
                   
               
             
             
               
                 Positive Database match 
                  −5.00 
               
               
                 Negative Database (this vendor) match 
                 +10.00 
               
               
                 Negative Database (other vendor) match 
                  +8.50 
               
               
                 Not shipping to card holder address 
                  +0.50 
               
               
                 Shipping to card holder address 
                  −1.00 
               
               
                 Not shipping to customer address 
                  +0.50 
               
               
                 Shipping to customer address 
                  −1.00 
               
               
                 Shipping to Freight forwarder 
                  +1.00 
               
               
                 Third party address verification (AVS) ok 
                  −1.00 
               
               
                 Third party address verification (AVS) partial ok 
                  +2.50 
               
               
                 Third party address verification (AVS) not ok 
                  +5.00 
               
               
                 Customer Svc. Rep. Suspects fraud 
                  +5.00 
               
               
                 High risk zip code 
                  +1.50 
               
               
                 Telephone order 
                  +0.25 
               
               
                 Total transaction amount 
                  +0.50/$100 
               
               
                 Air shipment 
                  +0.50 
               
               
                 Customer duration 
                  −0.05/year 
               
               
                 Customer status = excellent 
                  −1.00 
               
               
                 Customer status = good 
                  −0.25 
               
               
                 Customer status = fair 
                  +0.25 
               
               
                 Customer status = poor 
                  +1.00 
               
               
                 Credit card amount velocity exceeded 
                  +0.50 
               
               
                 Credit card count velocity exceeded 
                  +0.50 
               
               
                 Customer amount velocity exceeded 
                  +0.25 
               
               
                 Customer count velocity exceeded 
                  +0.25 
               
               
                 Ship-to-address amount velocity exceeded 
                  +0.50 
               
               
                 Ship-to-address count velocity exceeded 
                  +0.50 
               
               
                 Card Verification Value (CVV2) mismatch 
                  +0.50 
               
               
                 Card Verification Value (CVV2) match 
                  −1.00 
               
               
                   
               
             
          
         
       
     
     The databases  308 ,  310 ,  312 ,  314  are preferably updated periodically. For example, a batch update process is used from each participating vendor  106 . Alternatively, the databases  308 ,  310 ,  312 ,  314  are updated in real time as each participating vendor  106  acquires the relevant data. These updates occur automatically or manually. The negative databases  310 ,  312  are updated by the participating vendors  106 ,  316 ,  318  with transactions  100  which are verified by the vendor  106 ,  316 ,  319  as being fraudulent. Records are removed from the negative databases  310 ,  312  when it is determined that the fraudulent indicativeness of the stored information is no longer accurate. In one embodiment, this occurs only after a manual review indicates that the record was entered into the negative databases  310 ,  312  in error or the customer&#39;s or address&#39; fraudulent status has changed. The customer&#39;s fraudulent status may change, for example, after a specified time period has elapsed in which there has been no further fraudulent activity by that customer. An address&#39; fraudulent status may change if it is determined that the previous occupants, who perpetrated the original fraud, have moved and new customers now live at the address. In alternative embodiments, records are removed from the negative databases  310 ,  312  automatically based on pre-determined rules such as a defined period of non-fraudulent activity or through a combination of automated processes and manual review. 
     The fraud multiplier for the transaction is preferably computed by first determining the points for each transaction parameter  116 , as discussed above. To compute the fraud multiplier, the points determined for each transaction parameter  116  are summed to compute a total score (Block  306 ). Alternatively, other mathematical formulae may be used to compute the fraud multiplier. It is preferable that a mathematical computation be used which is statistically designed to minimize the overall error rate for the fraud detection process. 
     Once the fraud multiplier has been computed, the fraud detection system next computes the SKU points for the transaction (Block  320 ). As was discussed above, a SKU is a unique identifier code which identifies particular commodities involved in transactions  100 . Each commodity has a unique SKU. Each SKU is associated with a point value indicating that particular commodity&#39;s propensity to be the subject of fraud. For each participating vendor  106 ,  316 , 318 , an associated SKU point database  322  contains points for the SKU&#39;s of the commodities which the particular vendor trades in. The SKU points for the transaction  100  is computed by summing the individual SKU points for each commodity involved in the transaction. The sum of the SKU points (i.e. the minimum SKU point sum) is at least 1. This prevents a transaction  100  with a high fraud multiplier but involving SKU&#39;s with no associated points (i.e., SKU&#39;s with very low fraud potential) from attaining a fraud score of 0 (e.g., where a customer purchases 1000 tubes of lipstick). Where more than one of the same commodity is being purchased, the SKU points for each is included or alternatively, that commodity may only be counted once. Alternative mathematical formulae may also be used such as diminishing or increasing point values or dynamic point values. For example, the larger the quantity ordered of any one product, the higher the points for that product (i.e., where 500 lipsticks are ordered, the first 100 each have 0 points, the next 100 each have 10 points, the next 100 each have 20 points, etc.) to allow for the possibility that orders of large quantities of any one product are more likely to be fraudulent. However, the mathematical computation used is statistically designed to minimize the overall error rate for the fraud detection process. An exemplary SKU points database  322  showing SKU&#39;s and associated point values for a particular vendor  106  is shown in Table 2.0. 
     
       
         
               
               
               
               
             
           
               
                   
                 TABLE 2.0 
               
               
                   
                   
               
               
                   
                 SKU 
                 POINTS 
                 DESCRIPTION 
               
               
                   
                   
               
             
             
               
                   
                 XX001 
                 +10 
                 OFFICE CHAIR - LEATHER 
               
               
                   
                 XX002 
                 +10 
                 FAX MACHINE 
               
               
                   
                 XX003 
                 +10 
                 CORDLESS PHONE-900/DK/CID 
               
               
                   
                 XX004 
                 +10 
                 PLAIN PAPER FAX 
               
               
                   
                 XX005 
                 +10 
                 CORDLESS PHONE-900/2LINE/CID 
               
               
                   
                 XX006 
                 +10 
                 CORDLESS PH-900/2LINE/CID/TA 
               
               
                   
                 XX007 
                 +10 
                 COMM. BREWER W/COFFEE 
               
               
                   
                 XX008 
                 +40 
                 STEREO SYSTEM 
               
               
                   
                 XX009 
                 +20 
                 CD CHANGER/12 CAP 
               
               
                   
                 XX010 
                 +40 
                 CAMCORDER VHS-C 
               
               
                   
                 XX011 
                 +10 
                 BUTCHER BLOCK CART 
               
               
                   
                 XX012 
                 +10 
                 RED DEVIL GRILL 
               
               
                   
                 XX013 
                 +10 
                 PRESSURE COOKER 
               
               
                   
                 XX014 
                 +10 
                 SECURITY AUTO-DIALER 
               
               
                   
                 XX015 
                 +10 
                 CD RACK 
               
               
                   
                 XX016 
                 +15 
                 SECURITY CAMERA 
               
               
                   
                 XX017 
                 +10 
                 COCKTAIL TABLE 
               
               
                   
                 XX018 
                 +10 
                 END TABLE 
               
               
                   
                 XX019 
                 +10 
                 SOFA TABLE 
               
               
                   
                 XX020 
                 +10 
                 LOVESEAT-FLORAL 
               
               
                   
                 XX021 
                 +10 
                 SOFA-FLORAL 
               
               
                   
                 XX022 
                 +15 
                 COMPACT STEREO KEVLAR SPE 
               
               
                   
                 XX023 
                 +15 
                 COMPACT COMPONENT SYSTEM 
               
               
                   
                 XX024 
                 +15 
                 COMPACT COMPONENT DOLBY 
               
               
                   
                 XX025 
                 +40 
                 DVD PLAYER 
               
               
                   
                 XX026 
                 +10 
                 TREADMILL/0-10 MPH 
               
               
                   
                 XX027 
                 +20 
                 TV 1.6″ 
               
               
                   
                 XX028 
                 +10 
                 CAMERA 
               
               
                   
                 XX029 
                 +15 
                 RACK SYSTEM 
               
               
                   
                 XX030 
                 +20 
                 4HD VCR 
               
               
                   
                   
               
             
          
         
       
     
     A total fraud score is computed from the fraud multiplier and the SKU points computed for the transaction (Block  324 ). The total fraud score is computed by multiplying the SKU points by the fraud multiplier. Alternatively, other mathematical computations may be used. The fraud score computation balances the SKU points vs. the other transaction parameters and is statistically designed to minimize the overall error rate for the fraud detection process. Since points can be subtracted from the fraud multiplier for positive transaction characteristics, the fraud score can be a negative number indicating that fraud is extremely unlikely. 
     The total fraud score is compared with a fraud score threshold  328  (Block  326 ). The fraud score threshold indicates the degree of potentially fraudulent behavior that indicates that the transaction  100  is likely fraudulent. Each participating vendor  106 ,  316 ,  318  may provide a fraud score threshold. An exemplary fraud score threshold is 50 points. Alternatively, multiple fraud score thresholds or a fraud score range are provided indicating a degree or range of fraudulently indicative behavior. In addition, the fraud score threshold may be a dynamic value, automatically adjusted based on the values of one or more of the transaction parameters  116  or SKU&#39;s of the commodities  112  involved. The dynamic fraud score threshold compensates for those parameters which do or do not indicate a substantial likelihood of fraud and may unbalance the computation. 
     If the fraud score is less than or equal (or alternatively just less than) to the fraud score threshold (Block  326 ), the consumer information provider  224  returns an indication back to the vendor  106  that the transaction is likely non-fraudulent (Block  338 ). If the fraud score is above the threshold, the consumer information provider  224  returns an indication that the transaction  100  is likely fraudulent. The returned indication may be a Boolean flag (i.e. likely fraudulent or not) or a confidence score related to the likelihood of the transaction  100  being fraudulent (e.g., “0”, meaning fraud unlikely, to “9”, meaning fraud extremely likely) 
     Alternatively, subsequent processing ensures that non-fraudulent transactions  100  are not mistakenly indicated as fraudulent. The transaction  100  is further checked against other consumer information databases  332  (Block  330 ). These other consumer information databases  332  are typically fee based and include proprietary databases of the consumer information provider  224  as well as third party public and non-public information sources. Access charges to these databases  332  are usually based on the nature of the database  332  and the nature of the authentication query or lookup being performed. For example, there may be a fixed fee charged for each address verification query. These databases  332  are alternate databases to the databases  308 ,  310 ,  312  used in the fraud multiplier computation (Block  306 ). Matches among the transaction parameters  116  and these alternative databases  332  can be used to diminish the likelihood that the current transaction  100  is fraudulent. It is preferred that these databases  332  be used to supplement the fraud determination process to minimize the external costs in accessing fee based data. In addition, some vendors  106  may prefer to supplement their fraud determination with more or fewer checks against the databases  332 , depending on the amount of access charges they are comfortable incurring versus the added benefit to the determination of fraud. 
     If it is determined that the check of the alternate databases  332  has made it unlikely that the current transaction is fraudulent (Block  334 ), a non-fraudulent indicator is returned to the vendor  106  for further processing as described above (Block  338 ). Otherwise an indicator indicating that the current transaction  100  is likely fraudulent is returned to the vendor  106  (Block  338 ). In one embodiment, the customer address, card holder address and ship-to address are all verified against databases  332 . If all three match, the fraud score is overridden and the transaction is approved. Alternatively, a less stringent match is required or other parameters are verified in place of or in addition to the addresses. In yet another alternative embodiment, the authentication results from the databases  332  are, themselves, assigned point values. Utilizing these point values, the fraud score is recomputed and re-compared against the fraud score threshold. Alternatively, the authentication results can be used to weight the point values of one or more of the transaction parameters  116  in the fraud multiplier computation and the fraud score is recomputed. 
     As the fraud detection system  108  operates, there may be errors (i.e. false positive and false negative responses from the system which are later determined by the vendor  106  or consumer information provider  224 ). In one embodiment, as these errors occur, the mis-flagged transactions  100  may be further reviewed or analyzed, either manually or automatically, by fraud investigators, to determine why the system failed. Where it is determined that a particular transaction parameter  116  or SKU is not accounted for or has an unbalanced effect on the fraud determination process, the databases  308 ,  310 ,  312 ,  314  are adjusted to correct the error. Such adjustments include altering point values, defining new or refining existing inter-parameter relationships or defining additional transaction parameters to be considered. 
     Further, the disclosed fraud detection system is capable of detecting fraud generally and not just specific instances of fraud such as a bad check or a stolen credit card. For example, the disclosed fraud detection system detects fraudulent use of coupons or discounts, fraudulent use of insurance for medical, dental or automobile related vendors  106  or fraudulent use of prescriptions for pharmaceutical related vendors  106 . All of the characteristics of the transaction are balanced so that factors which are more likely to indicate fraud have a larger impact on the determination but other factors and relationships among factors are still significantly considered. 
     In addition, while the above processes, comparisons and computations are disclosed as being performed in a particular order, in alternative embodiments the performance order may be different and all orderings are contemplated. For example, in one embodiment, the SKU points are computed prior to the computation of the fraud multiplier. 
     The disclosed processes, comparisons and computations are preferably implemented in software as computer programs written in the RPG language. The software and databases are preferably executed on an AS/400 computer system manufactured by IBM Corporation, located in Armonk, N.Y. The computer systems are preferably executing the OS/400 4.0 or higher version operating system provided by IBM. The databases are preferably implemented using the AS/400&#39;s Integrated File System. Alternatively, the software and databases are implemented on a mainframe computer system complying with the IBM 390 architecture. In still another embodiment, the software and databases are implemented on an RS/6000 computer system manufactured by IBM Corporation utilizing the UNIX operating system. In still another alternative embodiment, the software and databases are implemented using the Structured Query Language and executed on a computer system having a processor equivalent to a Pentium III or better, manufactured by Intel Corporation, located in Santa Clara, Calif. and utilizing the Microsoft Windows NT 4.0 Operating System and Microsoft SQL server manufactured by Microsoft Corporation located in Redmond, Wash. In further embodiments, one or more of the component parts of the fraud detection system are implemented directly in hardware. It will be appreciated that the implementation details will vary depending on the hardware and software environments of the participating vendors  106 ,  316 ,  318  and the consumer information provider  224 . 
     In the first embodiment, most of the fraud processing is external to the vendor  106  and can therefore be implemented in the single computing environment of the consumer information provider. This contains development of the fraud detection system to a single computing platform and single programming language, etc. easing development and maintenance complexity. 
     Second Embodiment 
     FIGS. 3A and 3B show a second embodiment of the fraud detection system  108 . This embodiment differs from the first embodiment only in the division of processing between the vendor  106  and the consumer information provider  446  which may or may not be external to the vendor  106 , as described above. 
     Referring to FIG. 3A, most of the processing remains internal to the vendor  106 , including the computation of the fraud multiplier (Block  416 ), computation of the SKU points (Block  426 ), and computation of the total fraud score (Block  430 ) as well as the associated databases  418 ,  420 ,  422 ,  424 ,  428 ,  434 . In this embodiment, however, the negative information databases  418 ,  422  only contain negative information collected by or imparted to this particular vendor  106 . The secondary checking that occurs when the fraud score has exceeded the fraud score threshold is performed by the consumer information provider  446  (Blocks  432 ,  436 ). Typically, this secondary checking involves access to fee-based databases (as described above). In one embodiment, the decision to perform secondary checking by the consumer information provider  446  is manually determined after a manual review of the transaction  100 . Alternatively, the secondary checking can be automatically performed for all transactions  100  whose fraud score exceeds the fraud score threshold or the decision to perform secondary checking can be based on one or more transaction parameters  116  and resultant point values. In yet another alternative embodiment, secondary checking with the consumer information provider  446  is done for specific transaction parameters  116  such as the customer, cardholder and ship-to addresses with subsequent secondary checking of other transaction parameters determined at the discretion of a manual reviewer. 
     Referring to FIG. 3B, the consumer information provider  446  checks the transaction  100  against negative databases  508 ,  510  which contain negative transaction information from other participating vendors  106 ,  512 ,  514  or sources (Block  506 ). From this information, it is determined whether or not there is a history of related fraud (Block  518 ). Further secondary checking is performed as described above against other proprietary, other public and non-public databases to further confirm the fraudulent status of the transaction  100  (Blocks  520 ,  524 ,  526 ). The fraud history, if any, and the results of the secondary checking are returned to the vendor  106  (Block  528 ). 
     Referring back to FIG. 3A, the fraud detection system determines the likelihood of fraud based on the total fraud score and the data returned by the consumer information provider  446 . In one embodiment, the fraud score and the data returned by the consumer information provider  446  are manually reviewed and reconciled to determine the likelihood of fraud (Block  440 ). Alternatively, the data returned by the consumer information provider  446  and the fraud score are automatically reconciled to determine the fraudulent status. For example, the data returned by the consumer information provider is used to add or subtract points from the fraud multiplier and the fraud score is recomputed and re-compared with the fraud threshold. If it is determined that the transaction  100  is non-fraudulent, the transaction is accepted (Block  442 ). If it is determined that the transaction is fraudulent, further action can be taken as described above (Block  444 ). 
     In addition to the advantages noted above for the first embodiment, the second embodiment offers easy data management and lower bandwidth requirements between the vendor  106  and the consumer information provider  446 . The main databases  418 ,  420 ,  422 ,  424 ,  428 ,  434  reside internally to the vendor  106  making updates and adjustments simpler and faster. Further, the amount of data needed to pass to the consumer information provider  446  is reduced since the consumer information provider  446  performs less of the overall fraud detection process. Further, the second embodiment provides a high reliability system which is not subject to communications problems between the vendor  106  and the consumer information provider  446 . In the event of a communications failure, the system still detects fraudulent transactions  100 . Finally, an internal system for detecting fraud operates and responds faster than a system which relies more heavily on external processing. 
     It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention.