Iterative processing of transaction information to detect fraud

A fraud management system is configured to store rules for detecting fraud, receive a transaction from a merchant, process the transaction using a first subset of rules to generate a fraud score for the transaction, and output information regarding the fraud score to the merchant to assist the merchant in determining whether to accept, deny, or fulfill the transaction. The fraud management system is further configured to receive, after outputting the information regarding the fraud score to the merchant, additional information relating to the transaction, re-process the transaction using a second subset of rules to generate an update fraud score, and output information regarding the updated fraud score to the merchant to assist the merchant in determining whether to accept, deny, or fulfill the transaction.

BACKGROUND

Merchants are much more responsible for the cost of fraud than are financial institutions and consumers. Accordingly, merchants are the most motivated victim group to adopt mitigation strategies. The mitigation strategies vary for online merchants as compared to the “brick and mortar” merchants. For example, online merchants typically employ a mixture of purchased and internally developed software solutions and manage significant fraud operations and claims management departments. “Brick and mortar” merchants adopt different mitigation strategies, where in-person interactions with consumers are possible. The techniques used to commit fraud against merchants are ever-changing. Thus, fraud protection, adopted by merchants, needs to be constantly adapting to the ever-changing fraud techniques.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

An implementation, described herein, may detect a fraudulent transaction, from a merchant, by analyzing information associated with multiple transactions from the merchant and/or one or more other merchants. In one implementation, a particular transaction may be analyzed iteratively. For example, the particular transaction may be analyzed, for fraud, when the particular transaction is received from the merchant, and may be analyzed again, for fraud, one or more later times as additional information, relevant to the particular transaction, is received. The additional information may provide additional context to the fraud analysis and, thereby, improve the determination of whether the particular transaction may be fraudulent.

FIG. 1is a diagram of an overview of an implementation described herein. For the example ofFIG. 1, assume that a consumer makes an online purchase of electronic goods via a website of a merchant. To complete the online purchase of the electronic goods, the consumer may provide credit or debit card information to the merchant.

The merchant may provide information regarding the transaction to a fraud management system. The term “transaction,” as used herein, is intended to be broadly interpreted to include an interaction of a consumer with a merchant. The interaction may involve the payment of money, a promise for a future payment of money, the deposit of money into an account, or the removal of money from an account. The term “money,” as used herein, is intended to be broadly interpreted to include anything that can be accepted as payment for goods or services, such as currency, coupons, credit cards, debit cards, gift cards, and funds held in a financial account (e.g., a checking account, a money market account, a savings account, a stock account, a mutual fund account, a paypal account, etc.). In one implementation, the transaction may involve a one time exchange of information, between the merchant and the fraud management system, which may occur at the completion of the interaction between the consumer and the merchant (e.g., when the consumer ends an online session with the merchant). In another implementation, the transaction may involve a series of exchanges of information, between the merchant and the fraud management system, which may occur during and/or after completion of the interaction between the consumer and the merchant.

The fraud management system may process the transaction using selected sets of rules to generate fraud information. The fraud management system may output the fraud information to the merchant to inform the merchant whether the transaction potentially involves fraud. The fraud information may take the form of a fraud score or may take the form of an “accept” alert (meaning that the transaction is not fraudulent) or a “reject” alert (meaning that the transaction is potentially fraudulent). The merchant may then decide whether to permit or deny the transaction, or proceed to fulfill the goods or services secured in the transaction, based on the fraud information. In the description to follow, the phrase “fulfill the transaction,” or the like, is intended to refer to fulfilling the goods or services secured in the transaction.

After the fraud management system initially processes the transaction, the fraud management system may receive additional information that may be relevant to the transaction. The additional information may include information regarding another transaction from this consumer, information regarding a transaction from another consumer, information regarding another transaction from the merchant, information regarding a transaction from another merchant that is affiliated with the merchant, information regarding a transaction from another merchant that is unaffiliated with the merchant, information regarding a transaction from another merchant that is associated with the same industry as the merchant, information regarding a transaction from another merchant that is associated with a different industry than the merchant, information regarding user behavior of the consumer on the merchant's website, information regarding user behavior of the consumer on another merchant's website, information regarding a human analyst's analysis of the transaction or other transactions from the consumer, and/or other information that might be useful in determining whether the transaction is fraudulent.

Based on the additional information, the fraud management system may re-process the transaction (one or more times) to generate updated fraud information. The fraud management system may output the updated fraud information to the merchant if the updated fraud information differs from the previously provided fraud information. For example, if the fraud management system previously indicated, to the merchant, that the transaction is not fraudulent and the re-processing of the transaction indicates that the transaction is potentially fraudulent, the fraud management system may notify the merchant that the transaction is potentially fraudulent. As a result, the merchant may be able to take remedial actions to reduce the merchant's loss due to the fraud.

In some scenarios, the fraud management system may detect potential fraud in near real-time (i.e., while the transaction is occurring). In other scenarios, the fraud management system may detect potential fraud after conclusion of the transaction (perhaps minutes, hours, or days later). In either scenario, the fraud management system may reduce revenue loss contributable to fraud. In addition, the fraud management system may help reduce merchant costs in terms of software, hardware, and personnel dedicated to fraud detection and prevention.

WhileFIG. 2shows a particular number and arrangement of devices, in practice, environment200may include additional devices, fewer devices, different devices, or differently arranged devices than are shown inFIG. 2. Also, although certain connections are shown inFIG. 2, these connections are simply examples and additional or different connections may exist in practice. Each of the connections may be a wired and/or wireless connection. Further, each consumer device210and merchant device220may be implemented as multiple, possibly distributed, devices. Alternatively, a consumer device210and a merchant device220may be implemented within a single device.

Consumer device210may include any device capable of interacting with a merchant device220to perform a transaction. For example, consumer device210may correspond to a communication device (e.g., a mobile phone, a smartphone, a personal digital assistant (PDA), or a wireline telephone), a computer device (e.g., a laptop computer, a tablet computer, or a personal computer), a gaming device, a set top box, or another type of communication or computation device. As described herein, a user, of a consumer device210, may use consumer device210to perform a transaction with regard to a merchant device220.

Merchant device220may include a device, or a collection of devices, capable of interacting with a consumer device210to perform a transaction. For example, merchant device220may correspond to a computer device (e.g., a server, a laptop computer, a tablet computer, or a personal computer). Additionally, or alternatively, merchant device220may include a communication device (e.g., a mobile phone, a smartphone, a PDA, or a wireline telephone) or another type of communication or computation device. As described herein, merchant device220may interact with a consumer device210to perform a transaction and may interact with fraud management system230to determine whether that transaction is potentially fraudulent.

Fraud management system230may include a device, or a collection of devices, that performs fraud analysis. Fraud management system230may receive transaction information from merchant devices220, perform fraud analysis with regard to the transaction information, and provide, to merchant devices220, information regarding the results of the fraud analysis.

Network240may include any type of network or a combination of networks. For example, network240may include a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a metropolitan area network (MAN), an ad hoc network, a telephone network (e.g., a Public Switched Telephone Network (PSTN), a cellular network, or a voice-over-IP (VoIP) network), an optical network (e.g., a FiOS network), or a combination of networks. In one implementation, network240may support secure communications between merchants220and fraud management system230. These secure communications may include encrypted communications, communications via a private network (e.g., a virtual private network (VPN) or a private IP VPN (PIP VPN)), other forms of secure communications, or a combination of secure types of communications.

FIG. 3is a diagram of example components of a device300. Device300may correspond to consumer device210, merchant device220, or fraud management system230. Each of consumer device210, merchant device220, and fraud management system230may include one or more devices300.

As shown inFIG. 3, device300may include a bus305, a processor310, a main memory315, a read only memory (ROM)320, a storage device325, an input device330, an output device335, and a communication interface340. In another implementation, device300may include additional components, fewer components, different components, or differently arranged components.

Bus305may include a path that permits communication among the components of device300. Processor310may include one or more processors, one or more microprocessors, one or more application specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), or one or more other types of processor that interprets and executes instructions. Main memory315may include a random access memory (RAM) or another type of dynamic storage device that stores information or instructions for execution by processor310. ROM320may include a ROM device or another type of static storage device that stores static information or instructions for use by processor310. Storage device325may include a magnetic storage medium, such as a hard disk drive, or a removable memory, such as a flash memory.

Input device330may include a mechanism that permits an operator to input information to device300, such as a control button, a keyboard, a keypad, or another type of input device. Output device335may include a mechanism that outputs information to the operator, such as a light emitting diode (LED), a display, or another type of output device. Communication interface340may include any transceiver-like mechanism that enables device300to communicate with other devices or networks (e.g., network240). In one implementation, communication interface340may include a wireless interface and/or a wired interface.

Device300may perform certain operations, as described in detail below. Device300may perform these operations in response to processor310executing software instructions contained in a computer-readable medium, such as main memory315. A computer-readable medium may be defined as a non-transitory memory device. A memory device may include space within a single physical memory device or spread across multiple physical memory devices.

The software instructions may be read into main memory315from another computer-readable medium, such as storage device325, or from another device via communication interface340. The software instructions contained in main memory315may cause processor310to perform processes that will be described later. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

FIG. 4is a diagram of example functional units of fraud management system230. In one implementation, the functions described in connection withFIG. 4may be performed by one or more components of device300(FIG. 3) or one or more devices300, unless described as being performed by a human.

As shown inFIG. 4, fraud management system230may include fraud detection unit410, fraud operations unit420, and portal unit430. In another implementation, fraud management system230may include fewer functional units, additional functional units, different functional units, or differently arranged functional units. Fraud detection unit410, fraud operations unit420, and portal unit430will be described generally with regard toFIG. 4and will be described in more detail with regard toFIGS. 5-10.

Generally, fraud detection unit410may receive information regarding transactions from merchant devices220and analyze the transactions to determine whether the transactions are potentially fraudulent. In one implementation, fraud detection unit410may classify a transaction as: “safe,” “unsafe,” or “for review.” A “safe” transaction may include a transaction with a fraud score that is less than a first threshold (e.g., less than 5, less than 10, less than 20, etc. within a range of fraud scores of 0 to 100, where a fraud score of 0 may represent a 0% probability that the transaction is fraudulent and a fraud score of 100 may represent a 100% probability that the transaction is fraudulent). An “unsafe” transaction may include a transaction with a fraud score that is greater than a second threshold (e.g., greater than 90, greater than 80, greater than 95, etc. within the range of fraud scores of 0 to 100) (where the second threshold is greater than the first threshold). A “for review” transaction may include a transaction with a fraud score that is greater than a third threshold (e.g., greater than 50, greater than 40, greater than 60, etc. within the range of fraud scores of 0 to 100) and not greater than the second threshold (where the third threshold is greater than the first threshold and less than the second threshold). In one implementation, the first, second, and third thresholds and the range of potential fraud scores may be set by an operator of fraud management system230. In another implementation, the first, second, and/or third thresholds and/or the range of potential fraud scores may be set by a merchant. In this case, the thresholds and/or range may vary from merchant-to-merchant. The fraud score may represent a probability that a transaction is fraudulent.

If fraud detection unit410determines that a transaction is a “safe” transaction, fraud detection unit410may notify a merchant device220that merchant device220may safely approve, or alternatively fulfill, the transaction. If fraud detection unit410determines that a transaction is an “unsafe” transaction, fraud detection unit410may notify a merchant device220to take measures to minimize the risk of fraud (e.g., deny the transaction, request additional information from a consumer device210, require interaction with a human operator, refuse to fulfill the transaction, etc.). Alternatively, or additionally, fraud detection unit410may provide information regarding the unsafe transaction to fraud operations unit420for additional processing of the transaction. If fraud detection unit410determines that a transaction is a “for review” transaction, fraud detection unit410may provide information regarding the transaction to fraud operations unit420for additional processing of the transaction.

Generally, fraud operations unit420may receive information regarding certain transactions and may analyze these transactions to determine whether a determination can be made whether the transactions are fraudulent. In one implementation, human analyzers may use various research tools to investigate transactions and determine whether the transactions are fraudulent.

Generally, portal unit430may generate reports and permit merchants to request and gain access to reports relating to transactions associated with the merchants. Portal unit430may also function like an access port via which a merchant device220may gain access to information from fraud detection unit410and/or information from fraud operations unit420, and/or otherwise interact with fraud detection unit410and/or fraud operations unit420.

FIG. 5is a diagram of example functional components of fraud detection unit410. In one implementation, the functions described in connection withFIG. 5may be performed by one or more components of device300(FIG. 3) or one or more devices300. As shown inFIG. 5, fraud detection unit410may include a merchant processor component510, a transaction memory520, a rules memory530, and a fraud detector component540. In another implementation, fraud detection unit410may include fewer functional components, additional functional components, different functional components, or differently arranged functional components.

Merchant processor component510may include a device, or a collection of devices, that may interact with merchants to assist the merchants in using fraud management system230and that may periodically collect user behavior data concerning activities of consumers in relation to websites of the merchants. For example, merchant processor component510may exchange encryption information, such as public/private keys or VPN information, with a merchant device220to permit secure future communications between fraud detection system230and merchant device220.

Merchant processor component510may receive, from the merchant or merchant device220, information that might be useful in detecting a fraudulent transaction. For example, merchant processor component510may receive a black list (e.g., a list of consumers or consumer devices210that are known to be associated with fraudulent activity) and/or a white list (e.g., a list of consumers or consumer devices210that are known to be particularly trustworthy). Additionally, or alternatively, merchant processor component510may receive historical records of transactions from the merchant or merchant device220. These historical records may include information regarding transactions that were processed by a system other than fraud management system230. Additionally, or alternatively, merchant processor component510may receive a set of policies from the merchant or merchant device220. The policies may indicate thresholds for determining safe transactions, unsafe transactions, and for review transactions, may indicate a range of possible fraud scores (e.g., range of 0 to 100, range of 0 to 1000, etc.), or may indicate other business practices of the merchant. Additionally, or alternatively, merchant processor component510may receive a set of rules that are particular to the merchant.

Merchant processor510may also receive user behavior data from a merchant or merchant device220. User behavior data may include information regarding a consumer's activity with regard to a merchant's website, such as the pages that the consumer accessed, the searches that the consumer performed, the selections that the consumer made, the purchases that the consumer has made, etc. The user behavior data may correspond to a single session, or multiple sessions, involving the consumer and the merchant. In one implementation, a merchant or merchant device220may send user behavior data to merchant processor510on-the-fly (i.e., as the activity is happening). Additionally, or alternatively, a merchant or merchant device220may send user behavior data to merchant processor510at some point after the activity has occurred. In either situation, the user behavior data may be used to determine whether a transaction, associated with this consumer (or another consumer), is fraudulent.

Transaction memory520may include one or more memory devices to store information regarding present and/or past transactions, and/or user behavior data. Present transactions may include transactions currently being processed by fraud detector component540, and past transactions may include transactions previously processed by fraud detector component540. In one implementation, transaction memory520may store data in the form of a database, such as a relational database or an object-oriented database. In another implementation, transaction memory520may store data in a non-database manner, such as tables, linked lists, or another arrangement of data.

Transaction memory520may store various information for any particular transaction. For example, transaction memory520might store: information identifying a consumer or a consumer device210(e.g., a consumer device ID, an IP address associated with the consumer device, a telephone number associated with the consumer device, a username associated with the consumer, a consumer ID, etc.); information identifying a merchant or a merchant device220(e.g., a merchant ID, merchant name, merchant device ID, etc.); information identifying an industry with which the merchant is associated (e.g., retail, medical, travel, financial, etc.); a name, telephone number, and address associated with the consumer; information regarding consumer device210(e.g., an IP address associated with the consumer device, a type/version of browser used by the consumer device, cookie information associated with the consumer device, a type/version of an operating system used by the consumer device, etc.); a dollar amount of the transaction; line items of the transaction (e.g., identification of each good/service purchased, each leg of an airplane flight booked, etc.); information regarding a form of payment received from the consumer (e.g., credit card information, debit card information, checking account information, paypal account information, etc.); a day and/or time that the transaction occurred (e.g., 13:15 on Nov. 5, 2010); a geographic location associated with the transaction or the consumer (e.g., a destination location associated with a form of travel, an origination location associated with a form of travel, a location of a hotel for which a room was reserved, a location of a residence of the consumer, etc.), and/or other types of information associated with the transaction, the merchant, the merchant device220, the consumer, or the consumer device210, and/or a past transaction associated with the merchant, the merchant device220, the consumer, or the consumer device210.

Transaction memory520may also store other information that might be useful in detecting a fraudulent transaction. For example, transaction memory520may store black lists and/or white lists. The black/white lists may be particular to a merchant or an industry or may be applicable across merchants or industries. The black/white lists may be received from merchants or may be generated by fraud management system230.

Transaction memory520may also store historical records of transactions from a merchant. These historical records may include transactions that were processed by a system other than fraud management system230. The historical records may include information similar to the information identified above and may also include information regarding transactions that the merchant had identified as fraudulent.

Rules memory530may include one or more memory devices to store information regarding rules that may be applicable to transactions. In one implementation, rules memory530may store rules in one or more libraries. A “library” may be a block of memory locations (contiguous or non-contiguous memory locations) that stores a set of related rules. In another implementation, rules memory530may store rules in another manner (e.g., as database records, tables, linked lists, etc.).

The rules may include general rules, merchant-specific rules, industry-specific rules, consumer-specific rules, transaction attribute specific rules, single transaction rules, multi-transaction rules, heuristic rules, pattern recognition rules, and/or other types of rules. Some rules may be applicable to all transactions (e.g., general rules may be applicable to all transactions), while other rules may be applicable to a specific set of transactions (e.g., merchant-specific rules may be applicable to transactions associated with a particular merchant). Rules may be used to process a single transaction (meaning that the transaction may be analyzed for fraud without considering information from another transaction) or may be used to process multiple transactions (meaning that the transaction may be analyzed for fraud by considering information from another transaction). Rules may also be applicable to multiple, unaffiliated merchants (e.g., merchants having no business relationships) or multiple, unrelated consumers (e.g., consumers having no familial or other relationship).

FIG. 6is a diagram of example libraries that may be present within rules memory530. As shown inFIG. 6, rules memory530may include rule libraries610-1,610-2,610-3, . . .610-P (P≧1) (collectively referred to as “libraries610,” and individually as “library610”) and rule engines620-1,620-2,620-3, . . .620-P (collectively referred to as “rule engines620,” and individually as “rule engine620”). WhileFIG. 6illustrates that rules memory530includes a set of rule libraries610and a corresponding set of rule engines620, rules memory530may include fewer, additional, or different components in another implementation.

Each rule library610may store a set of related rules. For example, a rule library610may store general rules that are applicable to all transactions. Additionally, or alternatively, a rule library610may store rules applicable to a single transaction (meaning that the transaction may be analyzed for fraud without considering information from another transaction). Additionally, or alternatively, a rule library610may store rules applicable to multiple transactions (meaning that the transaction may be analyzed for fraud by considering information from another transaction (whether from the same merchant or a different merchant, whether associated with the same consumer or a different consumer)).

Additionally, or alternatively, a rule library610may store merchant-specific rules. Merchant-specific rules may include rules that are applicable to transactions of a particular merchant, and not applicable to transactions of other merchants. Additionally, or alternatively, a rule library610may store industry-specific rules. Industry-specific rules may include rules that are applicable to transactions associated with a particular industry of merchants (e.g., financial, medical, retail, travel, etc.), and not applicable to transactions associated with other industries. Additionally, or alternatively, a rule library610may store consumer-specific rules. Consumer-specific rules may include rules that are applicable to transactions of a particular consumer or a particular set of consumers (e.g., all consumers in the consumer's family, all consumers located at a particular geographic location, all consumers located within a particular geographic region, all consumers using a particular type of browser or operating system, etc.), and not applicable to transactions of other consumers or sets of consumers.

Additionally, or alternatively, a rule library610may store location-specific rules. Location-specific rules may include rules that are applicable to transactions associated with a particular geographic area (e.g., an origination location associated with a travel itinerary, a destination location associated with a travel itinerary, a location from which a transaction originated, etc.), and not applicable to transactions associated with other geographic areas. Additionally, or alternatively, a rule library610may store rules associated with a particular transaction attribute, such as a dollar amount or range, a name of a traveler, a telephone number, etc.

The rules in rule libraries610may include human-generated rules and/or automatically-generated rules. The automatically-generated rules may include heuristic rules and/or pattern recognition rules. Heuristic rules may include rules that have been generated by using statistical analysis, or the like, that involves analyzing a group of attributes (e.g., a pair of attributes or a tuple of attributes) of transactions, and learning rules associated with combinations of attributes that are indicative of fraudulent transactions. Pattern recognition rules may include rules that have been generated using machine learning, artificial intelligence, neural networks, decision trees, or the like, that analyzes patterns appearing in a set of training data, which includes information regarding transactions that have been identified as fraudulent and information regarding transactions that have been identified as non-fraudulent, and generates rules indicative of patterns associated with fraudulent transactions.

In other implementations, rule libraries610may store other types of rules, other combinations of rules, or differently-generated rules. Because fraud techniques are constantly changing, the rules, in rule libraries610, may be regularly updated (either by manual or automated interaction) by modifying existing rules, adding new rules, and/or removing antiquated rules.

Each rule engine620may correspond to a corresponding rule library610. A rule engine620may receive a transaction from fraud detector component540, coordinate the execution of the rules by the corresponding rule library610, and return the results (in the form of zero or more alarms) to fraud detector component540. In one implementation, rule engine620may cause a transaction to be processed by a set of rules within the corresponding rule library610in parallel. In other words, the transaction may be concurrently processed by multiple, different rules in a rule library610(rather than serially processed).

Returning toFIG. 5, fraud detector component540may include a device, or a collection of devices, that performs automatic fraud detection on transactions. Fraud detector component540may receive a transaction from a particular merchant device220and select particular libraries610and particular rules within the selected libraries610applicable to the transaction. Fraud detector component540may then provide the transaction for processing by the selected rules in the selected libraries610in parallel. The output of the processing, by the selected libraries610, may include zero or more alarms. An “alarm,” as used herein, is intended to be broadly interpreted as a triggering of a rule in a library610. A rule is triggered when the transaction satisfies the rule. For example, assume that a rule indicates a situation where a consumer reserves a hotel room in the same geographic area in which the consumer lives. A transaction would trigger (or satisfy) the rule if the transaction involved a consumer making a reservation for a hotel room in the town where the consumer lives.

Fraud detector component540may sort and group the alarms and analyze the groups to generate a fraud score. The fraud score may reflect the probability that the transaction is fraudulent. Fraud detector component540may send the fraud score, or an alert generated based on the fraud score, to a merchant device220. The alert may simply indicate that merchant device220should accept, deny, or fulfill the transaction. In one implementation, the processing by fraud detector component540from the time that fraud detector component540receives the transaction to the time that fraud detector component540sends the alert may be within a relatively short time period, such as, for example, within thirty seconds, sixty seconds, or ten seconds. In another implementation, the processing by fraud detector component550from the time that fraud detector component550receives the transaction to the time that fraud detector component550sends the alert may be within a relatively longer time period, such as, for example, within minutes, hours or days.

FIG. 7is a diagram of example functional components of fraud detector component540. In one implementation, the functions described in connection withFIG. 7may be performed by one or more components of device300(FIG. 3) or one or more devices300. As shown inFIG. 7, fraud detector component540may include a rule selector component710, a rule applicator component720, an alarm combiner and analyzer component730, a fraud score generator component740, and an alert generator component750. In another implementation, fraud detector component540may include fewer functional components, additional functional components, different functional components, or differently arranged functional components.

Rule selector component710may receive a transaction from a merchant device220. In one implementation, the transaction may include various information, such as information identifying a consumer (e.g., name, address, telephone number, etc.); a total dollar amount of the transaction; a name of a traveler (in the case of a travel transaction); line items of the transaction (e.g., information identifying a good or service purchased or rented, origination, destination, and intermediate stops of travel, etc.); information identifying a merchant (e.g., merchant name or merchant identifier); information regarding a form of payment received from the consumer (e.g., credit card information, debit card information, checking account information, paypal account information, etc.); and information identifying a day and/or time that the transaction occurred (e.g., 13:15 on Nov. 5, 2010).

Additionally, or alternatively, rule selector component710may receive other information (called “meta information”) from the merchant in connection with the transaction. For example, the meta information may include information identifying a consumer device210(e.g., a consumer device ID, an IP address associated with the consumer device, a telephone number associated with the consumer device, etc.); other information regarding consumer device210(e.g., an IP address associated with the consumer device, a type/version of browser used by the consumer device, cookie information associated with the consumer device, a type/version of an operating system used by the consumer device, etc.); user behavior data associated with the consumer; and/or other types of information associated with the transaction, the merchant, the merchant device220, the consumer, or the consumer device210.

Additionally, or alternatively, rule selector component710may receive or obtain other information (called “third party information”) regarding the transaction, the merchant, the merchant device220, the consumer, or the consumer device210. For example, the other information may include a geographic identifier (e.g., zip code or area code) that may correspond to the IP address associated with consumer device210. The other information may also, or alternatively, include information identifying an industry with which the merchant is associated (e.g., retail, medical, travel, financial, etc.). Rule selector component710may obtain the third party information from a memory or may use research tools, such an IP address-to-geographic location identifier look up tool or a merchant name-to-industry look up tool.

Additionally, or alternatively, rule selector component710may receive or obtain historical information regarding the merchant, the merchant device220, the consumer, the consumer device210, or information included in the transaction. In one implementation, rule selector component710may obtain the historical information from transaction memory520(FIG. 5).

The transaction information, the meta information, the third party information, and/or the historical information may be individually referred to as a “transaction attribute” or an “attribute of the transaction,” and collectively referred to as “transaction attributes” or “attributes of the transaction.”

Rule selector component710may generate a profile for the transaction based on the transaction attributes. Based on the transaction profile and perhaps relevant information in a white or black list (i.e., information, relevant to the transaction, that is present in a white or black list), rule selector component710may select a set of libraries610within rules memory530and/or may select a set of rules within one or more of the selected libraries610. For example, rule selector component710may select libraries610, corresponding to general rules, single transaction rules, multi-transaction rules, merchant-specific rules, industry-specific rules, etc., for the transaction.

Rule applicator component720may cause the transaction to be processed using rules of the selected libraries610. For example, rule applicator component720may provide information regarding the transaction to rule engines620corresponding to the selected libraries610. Each rule engine620may process the transaction in parallel and may process the transaction using all or a subset of the rules in the corresponding library610. The transaction may be concurrently processed by different sets of rules (of the selected libraries610and/or within each of the selected libraries610). The output, of each of the selected libraries610, may include zero or more alarms. As explained above, an alarm may be generated when a particular rule is triggered (or satisfied).

Alarm combiner and analyzer component730may aggregate and correlate the alarms. For example, alarm combiner and analyzer component730may analyze attributes of the transaction(s) with which the alarms are associated (e.g., attributes relating to a form of payment, an IP address, a travel destination, etc.). Alarm combiner and analyzer component730may sort the alarms, along with alarms of other transactions (past or present), into groups (called “cases”) based on values of one or more of the attributes of the transactions associated with the alarms (e.g., credit card numbers, IP addresses, geographic locations, consumer names, etc.). The transactions, included in a case, may involve one merchant or multiple, unaffiliated merchants and/or one consumer or multiple, unrelated consumers.

Alarm combiner and analyzer component730may separate alarms (for all transactions, transactions sharing a common transaction attribute, or a set of transactions within a particular window of time) into one or more cases based on transaction attributes. For example, alarm combiner and analyzer component730may place alarms associated with a particular credit card number into a first case, alarms associated with another particular credit card number into a second case, alarms associated with a particular IP address into a third case, alarms associated with a consumer or consumer device210into a fourth case, alarms associated with a particular merchant into a fifth case, alarms associated with a particular geographic location into a sixth case, etc. A particular alarm may be included in multiple cases.

FIG. 8is a diagram of example cases into which alarms may be placed by alarm combiner and analyzer component730. As shown inFIG. 8, assume that fraud detector component540receives four transactions T1-T4. By processing each of transactions T1-T4using rules in select libraries610, zero or more alarms may be generated. As shown inFIG. 8, assume that three alarms A1-A3are generated. An alarm may be an aggregation of one or more transactions (e.g., alarm A1is the aggregation of transactions T1and T2; alarm A2is the aggregation of transaction T3; and alarm A3is the aggregation of transactions T3and T4) that share a common attribute. The alarms may be correlated into cases. As shown inFIG. 8, assume that two cases C1and C2are formed. A case is a correlation of one or more alarms (e.g., case C1is the correlation of alarms A1and A2; and case C2is the correlation of alarms A2and A3) that share a common attribute.

An individual alarm may not be sufficient evidence to determine that a transaction is fraudulent. When the alarm is correlated with other alarms in a case, then a clearer picture of whether the transaction is fraudulent may be obtained. Further, when multiple cases involving different attributes of the same transaction are analyzed, then a decision may be made whether a transaction is potentially fraudulent.

Returning toFIG. 7, fraud score generator component740may generate a fraud score. Fraud score generator component740may generate a fraud score from information associated with one or more cases (each of which may include one or more transactions and one or more alarms). In one implementation, fraud score generator component740may generate an alarm score for each generated alarm. For example, each of the transaction attributes and/or each of the rules may have a respective associated weight value. Thus, when a particular transaction attribute causes a rule to trigger, the generated alarm may have a particular score based on the weight value of the particular transaction attribute and/or the weight value of the rule. When a rule involves multiple transactions, the generated alarm may have a particular score that is based on a combination of the weight values of the particular transaction attributes.

In one implementation, fraud score generator component740may generate a case score for a case by combining the alarm scores in some manner. For example, fraud score generator component740may generate a case score (CS) by using a log-based Naïve Bayesian algorithm, such as:

CS=∑i⁢ASi×AWiAMi∑i⁢AWi×1000,
where CS may refer to the score for a case, ASimay refer to an alarm score for a given value within an alarm i, AWimay refer to a relative weight given to alarm i, and AMimay refer to a maximum score value for alarm i. The following equation may be used to calculate ASiwhen the score for the alarm involves a list (e.g., more than one alarm in the case):
ASi=1−(1−si)×(1−s2)×(1−sn).
Alternatively, fraud score generator component740may generate a case score using an equation, such as:

CS=∑k=1m⁢ASk,orCS=∑k=1m⁢ASk×AWk
(as shown inFIG. 8, alarm A1has an alarm score AS1and a weight value AW1, alarm A2has an alarm score AS2and a weight value AW2, and alarm A3has an alarm score AS3and a weight value AW3).

Fraud score generator component740may generate a fraud score for a transaction by combining the case scores in some manner. For example, fraud score generator component740may generate the fraud score (FS) using an equation, such as:

In another implementation, each case may have an associated weight value (as shown inFIG. 8, case C1has a case score CS1and a weight value CW1, and case C2has a case score CS2and a weight value CW2). In this situation, fraud score generator component740may generate the fraud score using an equation, such as:

FS=∑k=1n⁢CSk×CWk,
where CW may refer to a weight value for a case.

Alert generator component750may generate an alert and/or a trigger based, for example, on the fraud score. In one implementation, alert generator component750may classify the transaction, based on the fraud score, into: safe, unsafe, or for review. As described above, fraud detection unit410may store policies for a particular merchant that indicate, among other things, the thresholds that are to be used to classify a transaction as safe, unsafe, or for review. When the transaction is classified as safe or unsafe, alert generator component750may generate and send the fraud score and/or an alert (e.g., safe/unsafe or accept/deny) to the merchant so that the merchant can make an intelligent decision as to whether to accept, deny, or fulfill the transaction. When the transaction is classified as for review, alert generator component750may generate and send a trigger to fraud operations unit420so that fraud operations unit420may perform further analysis regarding a transaction or a set of transactions associated with a case.

As described briefly above and in more detail below, fraud detector component540may analyze a particular transaction at multiple, different times. For example, fraud detector component540may initially process the transaction to generate a fraud score and/or an alert, as described above. As additional information is obtained relating to one or more of the transaction attributes, fraud detector component540may subsequently process the transaction one or more additional times to update the fraud score and/or the alert.

FIG. 9is a diagram of example functional components of fraud operations unit420. In one implementation, the functions described in connection withFIG. 9may be performed by one or more components of device300(FIG. 3) or one or more devices300, unless described as being performed by a human. As shown inFIG. 9, fraud operations unit420may include a human analyzer910and a set of research tools920. In another implementation, fraud operations unit420may include fewer, additional, or different functional components.

Human analyzer910may include a person, or a set of people, trained to research and detect fraudulent transactions. Human analyzer910may analyze for review transactions (e.g., transactions included in consolidated cases) and perform research to determine whether the transactions are fraudulent. Additionally, or alternatively, human analyzer910may perform trending analysis, perform feedback analysis, modify existing rules, and/or create new rules. Human analyzer910may record the results of transaction analysis and may present the results to fraud detection unit410and/or one or more merchant devices220. Human analyzer910may cause modified rules and/or new rules to be stored in appropriate libraries610.

Research tools920may include financial information922, case history924, chargeback information926, and other research tools928. Financial information922may include financial data and tools. Case history924may include a repository of previously analyzed cases. In one implementation, case history924may store a repository of cases for some period of time, such as six months, a year, two years, five years, etc. Chargeback information926may include information regarding requests for reimbursements (commonly referred to as “chargebacks”) from a financial institution when the financial institution identifies a fraudulent transaction. When the financial institution identifies a fraudulent transaction, the financial institution may contact the merchant that was involved in the transaction and indicate, to the merchant, that the merchant's account is going to be debited for the amount of the transaction and perhaps have to pay a penalty fee. Other research tools928may include reverse telephone number look up tools, address look up tools, white pages tools, Internet research tools, etc. which may facilitate the determination of whether a transaction is fraudulent.

FIG. 10is a diagram of example functional components of portal unit430. In one implementation, the functions described in connection withFIG. 10may be performed by one or more components of device300(FIG. 3) or one or more devices300. As shown inFIG. 10, portal unit430may include a report generator component1010and a front end component1020. In another implementation, portal unit430may include fewer functional components, additional functional components, different functional components, or differently arranged functional components.

Report generator component1010may generate reports for merchants. For example, a merchant may request (e.g., via front end component1020) a particular report relating to transactions that the merchant sent to fraud management system230. The report may provide information regarding the analysis of various transactions and may be tailored, by the merchant, to include information that the merchant desires. Report generator component1010may be configured to generate reports periodically, only when prompted, or at any other interval specified by a merchant. Report generator component1010may automatically send reports to merchants or may make the reports available to the merchants via front end component1020.

In one implementation, report generator component1010may segregate information prior to generating a report. As explained above, a case may include information regarding transactions of multiple, unaffiliated merchants. For business reasons, when generating a report for a particular merchant, report generator component1010may remove information regarding transactions from other merchants (“other transactions”), including, for example, the influence that the other transactions had in generating fraud scores and in triggering particular rules. Report generator component1010may adjust scores (alarm, case, and/or fraud scores) to remove the effects from the other transactions.

Front end component1020may present a user interface accessible to merchants. Via front end component1020, merchants may request reports, access previously-generated reports, interact with a human analyzer, or interact with fraud detection unit410and/or fraud operations unit420in another manner. In one implementation, front end component1020may automatically send generated reports to merchants (e.g., via email, facsimile, etc.) or may store generated reports in a memory to await retrieval by the merchants.

FIG. 11is a flowchart of an example process1100for analyzing instances of fraud. In one implementation, process1100may be performed by one or more components/devices of fraud management system230. In another implementation, one or more blocks of process1100may be performed by one or more other components/devices, or a group of components/devices including or excluding fraud management system230.

Process1100may include receiving a transaction (block1110). For example, fraud detector component540may receive a transaction from a merchant device220. Merchant device220may use secure communications, such as encryption or a VPN, to send the transaction to fraud management system230. In one implementation, merchant device220may send the transaction to fraud management system230in near real time (e.g., when a consumer submits money to the merchant for the transaction) and perhaps prior to the money being accepted by the merchant. In another implementation, merchant device220may send the transaction to fraud management system230after the money, for the transaction, has been accepted by the merchant (e.g., after the money has been accepted but prior to a product or service, associated with the transaction, being fulfilled, or possibly after the money has been accepted and after the product or service, associated with the transaction, has been fulfilled). In practice, fraud management system230may simultaneously receive information regarding multiple transactions from one or more merchant devices220.

Rules may be selected for the transaction (block1120). For example, fraud detector component540may generate a profile for the transaction based on transaction attributes (e.g., information in the transaction itself, meta information associated with the transaction, third party information associated with the transaction, and/or historical information associated with one or more attributes of the transaction). Fraud detector component540may use the profile and relevant information in a black or white list (if any information, relevant to the transaction, exists in a black or white list) to select a set of libraries610and/or a set of rules within one or more libraries610in the selected set of libraries610. For example, fraud detector component540may select libraries610having single transaction rules, multi-transaction rules, merchant-specific rules, industry-specific rules, consumer-specific rules, or the like, based on information in the profile and/or information (if any) in a black or white list. As described above, some rules may be selected for every transaction.

The transaction may be processed using the selected rules (block1130). For example, fraud detector component540may provide the transaction to rule engines620corresponding to the selected set of libraries610for processing. In one implementation, fraud detector component540may provide the transaction for processing by multiple rule engines620in parallel. The transaction may also be processed using two or more of the rules, in the selected set of rules of a library610, in parallel. By processing the transaction using select rules, the accuracy of the results may be improved over processing the transaction using all of the rules (including rules that are irrelevant to the transaction). When a rule triggers (is satisfied), an alarm may be generated. The output of processing the transaction using the selected rules may include zero or more alarms.

The alarms may be collected and sorted (block1140). For example, fraud detector component540may aggregate the alarms and may analyze attributes of the transactions with which the alarms are associated (e.g., attributes relating to a particular form of payment, a particular geographic area, a particular consumer, etc.). Fraud detector component540may correlate the alarms, along with alarms of other transactions (past or present associated with the same or different (unaffiliated) merchants), into cases based on values of the attributes of the transactions associated with alarms. For example, fraud detector component540may include one or more alarms associated with a particular credit card number in a first case, one or more alarms associated with a particular travel destination in a second case, one or more alarms associated with a particular country in a third case, etc. As described above, a particular alarm may be included in multiple cases.

The alarms, in one or more cases, may be analyzed across one or more transactions (block1150). For example, fraud detector component540may analyze the alarms in a case (where the alarms may be associated with multiple transactions possibly from multiple, unaffiliated merchants and/or possibly from multiple, different industries) to determine whether the alarms justify a determination that the transaction is potentially fraudulent. By analyzing alarms in multiple cases, fraud detector component540may get a good picture of whether fraudulent activity is occurring.

A fraud score may be generated (block1160). For example, fraud detector component540may generate a case score for each of the cases using a technique, such as a technique described previously. Fraud detector component540may combine the case scores, associated with the transaction, to generate a fraud score for the transaction. In one implementation, as described above, the case scores, associated with the different cases, may be weighted differently. For example, the fraud score of case1may have an associated weight of CW1, the fraud score of case2may have an associated weight of CW2, the fraud score of case3may have an associated weight of CW3, etc. Thus, in this implementation, the different case scores may not contribute equally to the fraud score. The fraud score may reflect a probability that the transaction is fraudulent.

In one implementation, the fraud score may include a value in the range of 0 to 100, where “0” may reflect a 0% probability that the transaction is fraudulent and “100” may reflect a 100% probability that the transaction is fraudulent. It may be possible for the case score of a particularly important case (with a high weight value) to drive the fraud score to 100 (even without any contribution from any other cases).

An alert may be generated (block1170). For example, fraud detector component540may generate an alert based on the fraud score and policies associated with the merchant. For example, the merchant may specify policies that indicate what fraud scores constitute a safe transaction, what fraud scores constitute an unsafe transaction, and what fraud scores constitute a for review transaction. Fraud detector component540may generate an alert that indicates, to the merchant, that the transaction should be permitted or that the transaction should be denied.

Fraud detector component540may send the alert and/or the fraud score to the merchant so that the merchant can process the transaction accordingly. In one implementation, fraud detector component540may send the alert and/or fraud score while the merchant is still processing the transaction (e.g., before the merchant has approved the transaction). In another implementation, fraud detector component540may send the alert and/or fraud score after the merchant has completed processing the transaction (e.g., after the merchant has approved the transaction). In the latter implementation, when the transaction is determined to be potentially fraudulent, the merchant may take measures to minimize its loss (e.g., by canceling the airline tickets, by canceling shipping of the ordered product, by canceling performance of the ordered service, by canceling the payment of a medical claim, etc.).

Alarm information and/or the fraud score, for the transaction, may be stored (block1180). For example, fraud detector component540may store the alarms that were generated for the transaction and/or the alarm scores associated with those alarms. Additionally, or alternatively, fraud detector component540may store the fraud score that was calculated for the transaction.

Additional information, relating to the transaction, may be received (block1190). For example, at some later point in time after calculating the fraud score for the transaction, fraud detector component540may receive additional information that is relevant to the transaction (e.g., information relating to an attribute of the transaction). The additional information may include, for example, information regarding another transaction from this consumer, information regarding a transaction from another consumer, information regarding another transaction from the merchant, information regarding a transaction from another merchant that is affiliated with the merchant, information regarding a transaction from another merchant that is unaffiliated with the merchant, information regarding a transaction from another merchant that is associated with the same industry as the merchant, information regarding a transaction from another merchant that is associated with a different industry than the merchant, user behavior data associated with the consumer with regard to the merchant's website, user behavior data associated with the consumer with regard to another merchant's website, information regarding a human analyst's analysis of the transaction or other transactions from the consumer, and/or other information that might be useful in determining whether the transaction is fraudulent.

Rules may be selected again for the transaction (block1120). For example, fraud detector component540may generate a profile for the transaction based on transaction attributes (e.g., information in the transaction itself, meta information associated with the transaction, third party information associated with the transaction, and/or historical information associated with one or more attributes of the transaction). In this case, the transaction attributes may include the additional information that was received and that relates to the previously-processed transaction.

As explained above, fraud detector component540may use the profile and relevant information in a black or white list (if any information, relevant to the transaction, exists in a black or white list) to select a set of libraries610and/or a set of rules within one or more libraries610in the selected set of libraries610.

The transaction may be processed using the selected rules (block1130). For example, fraud detector component540may provide the transaction to rule engines620corresponding to the selected set of libraries610for processing, as described above. The output of processing the transaction using the selected rules may include zero or more alarms.

The alarms may be collected and sorted (block1140). For example, fraud detector component540may aggregate the alarms and may analyze attributes of the transactions with which the alarms are associated (e.g., attributes relating to a particular form of payment, a particular geographic area, a particular consumer, etc.). Fraud detector component540may correlate the alarms, along with alarms of other transactions (past or present associated with the same or different (unaffiliated) merchants), into cases based on values of the attributes of the transactions associated with alarms.

The alarms, in one or more cases, may be analyzed across one or more transactions (block1150). For example, fraud detector component540may analyze the alarms in a case to determine whether the alarms justify a determination that the transaction is potentially fraudulent. By analyzing alarms in multiple cases, fraud detector component540may get a good picture of whether fraudulent activity is occurring.

A fraud score may be generated (block1160). For example, fraud detector component540may generate a case score for each of the cases using a technique, such as a technique described previously. Fraud detector component540may combine the case scores, associated with the transaction, to generate a fraud score for the transaction.

An alert may be generated (block1170). For example, as explained above, fraud detector component540may generate an alert based on the fraud score and policies associated with the merchant. In this case, fraud detector component540may determine whether the fraud score and/or the alert differs from the fraud score and/or alert that was previously generated. If the fraud score and/or the alert differs, fraud detector component540may send the alert and/or the fraud score to the merchant so that the merchant can process the transaction accordingly. For example, if the alert and/or fraud score indicates that the transaction, which was previously identified as not fraudulent, is now determined to be potentially fraudulent, the merchant may take measures to minimize its loss (e.g., by canceling the airline tickets, by canceling shipping of the ordered product, by canceling performance of the ordered service, by canceling the payment of a medical claim, etc.).

Blocks1120-1190, of process1100, may be repeated one or more times. Each iteration of blocks1120-1190may be based on additionally received information and, thus, improve the calculation of the fraud score.

FIGS. 12-14are diagrams illustrating an example for identifying a fraudulent transaction. As shown inFIG. 12, assume that a first consumer uses a particular credit card on a merchant's website (shown as FlyToday.com inFIG. 12) to purchase a trip. For example, assume that, on October 1st, the first consumer purchases a trip that leaves Phoenix on November 1st for Mexico City and returns to Phoenix on November 10th.

The transaction, associated with this trip, may be processed by fraud management system230. For example, fraud management system230may receive the transaction associated with the first consumer, select rules for the transaction, such as travel industry rules, FlyToday.com-specific rules, credit card rules, IP address rules, Mexico City rules, single transaction rules, multi-transaction rules, etc., and process the transaction, in parallel, using the selected rules. Assume that a set of the selected rules trigger and generate corresponding alarms. For example, a rule may generate an alarm because the travel is destined for the hot destination of Mexico City (a hot destination may refer to a destination known to be associated with fraudulent activity).

Fraud management system230may process the alarms, correlate the alarms into cases, and determine, for example, that the transaction is likely not fraudulent based on the information known to fraud management system230at the time of processing the October 1st transaction associated with the first consumer. Fraud management system230may notify the FlyToday.com merchant that the transaction is not fraudulent. In other words, based on the totality of information available to fraud management system230at the time of processing the transaction associated with the first consumer, fraud management system230may determine that the transaction is not fraudulent and may notify the FlyToday.com merchant to accept the transaction, as shown inFIG. 12.

As shown inFIG. 13, assume that second, third, and fourth consumers use credit cards to purchase trips on another merchant's website (shown as TravelToday.com inFIG. 13). For example, assume that, on October 8th, the second consumer purchases a trip, for the same traveler as the trip purchased by the first consumer, that leaves Las Vegas on November 8th for Rio de Janeiro and returns to Las Vegas on November 15th; that, on October 16th, the third consumer, using the same IP address as the first consumer, purchases a trip that leaves San Francisco on November 12th for Rio de Janeiro and returns to San Francisco on November 15th; and that, on October 21st, the fourth consumer, also using the same IP address as the first consumer, purchases a trip that leaves Miami on November 11th for Rio de Janeiro and returns to Miami on November 15th.

Fraud management system230may receive the transactions associated with the second, third, and fourth consumers, select rules for the transactions, such as travel industry rules, TravelToday.com-specific rules, credit card rules, IP address rules, Rio de Janeiro rules, Miami rules, single transaction rules, multi-transaction rules, etc., and process the transactions, in parallel, using the selected rules. Assume that, for each of the transactions, a set of the selected rules trigger and generate corresponding alarms. For example, one rule may generate an alarm because the travel is destined for the hot destination of Rio de Janeiro; another rule may generate an alarm because the travel originated in the hot location of Miami; another rule may generate an alarm because there is overlapping travel (e.g., the travel itineraries overlap—one leaves on November 1st and returns on November 10th, and the other leaves on November 8th and returns on November 15th); and another rule may generate an alarm because the travel from the first, third, and fourth consumers originate from the same IP address.

Fraud management system230may process the alarms, correlate the alarms into cases, and determine, for example, that one or more of the transactions, from the second, third, and fourth consumers, is potentially fraudulent based on the information known to fraud management system230at the time of processing the transactions associated with the second, third, and fourth consumers. In other words, based on the totality of information available to fraud management system230at the time of processing the transactions associated with the second, third, an fourth consumers, fraud management system230may determine that one or more of the transactions is potentially fraudulent and may notify the TravelToday.com merchant to deny, or not fulfill, the transaction(s).

Based on the transactions from the second, third, and fourth consumers, among other things, fraud management system230may determine that the transaction from the first consumer should be re-processed. For example, fraud management system230may determine that the transactions, from the second, third, and fourth consumers, share at least one attribute with the transaction from the first consumer. As a result, fraud management system230may re-process the transaction from the first consumer.

Assume that when fraud management system230re-processes the transaction, fraud management system230determines that the transaction is potentially fraudulent. For example, fraud management system230may determine that the overlapping travel (e.g., the first and second consumers purchase trips for the same traveler, where one of the trips leaves Phoenix on November 1st for Mexico City and returns to Phoenix on November 10th, and the other one of the trips leaves Las Vegas on November 8th for Rio de Janeiro and returns to Las Vegas on November 15th); the multiple purchases of travel to the hot destination of Rio de Janeiro (e.g., the second, third, and fourth consumers purchase travel to Rio de Janeiro); the multiple purchases of travel from the same IP address (e.g., the first, third, and fourth consumers purchase travel from the same IP address); etc. contribute to a determination that the transaction, associated with the first consumer, is potentially fraudulent. In other words, based on the totality of information available to fraud management system230at the time of re-processing the transaction associated with the first consumer, fraud management system230may determine that the transaction is potentially fraudulent and may notify the FlyToday.com merchant to deny, or not fulfill, the transaction, as shown inFIG. 14. As a result, the FlyToday.com merchant may take measures to minimize its loss, such as canceling the tickets purchased by the first consumer (provided that the travel had not already occurred).

An implementation, described herein, may determine potentially fraudulent transactions by re-processing transactions when additional information, relating to one or more attributes associated with the transactions, becomes available. As described above, the transactions and the additional information may be associated with a single merchant, multiple, unaffiliated merchants associated with a particular industry, or multiple, unaffiliated merchants associated with multiple, different industries. By processing the transactions in such a manner, better fraud detection results may be obtained over prior, existing fraud detection systems.

For example, while a series of blocks has been described with regard toFIG. 11, the order of the blocks may be modified in other implementations. Further, non-dependent blocks may be performed in parallel.

It will be apparent that different aspects of the description provided above may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement these aspects is not limiting of the invention. Thus, the operation and behavior of these aspects were described without reference to the specific software code—it being understood that software and control hardware can be designed to implement these aspects based on the description herein.