may begin at operation 202 by generating a number of machine learning targets. Each machine learning target may be associated with a user 104 and/or an account in some embodiments. As indicated above, the targets may pertain to any desired outcome. For example, the targets may indicate and/or predict first party fraud, third party fraud, whether a user 104 is a money launderer, whether a user 104 is a money mule, whether a user 104 is a synthetic, whether a user 104 and/or account has been associated with one or more scams, whether a user 104 has died, whether a user 104 is in debt, whether a user 104 has specific hobbies (e.g., cars, swimming pools, shoes, etc.), whether a user 104 collects unemployment, whether a user 104 has one or more jobs, what type of phone a user 104 has, whether a user 104 utilizes a particular peer to peer payment application and/or other mobile application, whether a user 104 and/or account has been associated with undesirable behavior by a bank (e.g., as identified in a hot file), whether a user 104 has written bad checks, whether a user 104 pays fees that are due, whether a user 104 is married, whether a user 104 will commit fraud, whether a user 104 will enroll with a particular peer to peer payment service and/or other banking service, whether a user 104 will save money by enrolling in a particular peer to peer payment service and/or other banking service, whether a user 104 has had his identity stolen, whether a user 104 has passed counterfeit checks, marketing information (e.g., whether a user 104 is interested in a particular product, service, brand, etc.), and/or other financial or non-financial targets. In a particular embodiment, at least some of the targets may be generated by performing string matching on data files (such as hot files) provided by a financial institution. Oftentimes, the targets are provided directly from the banks (or other data source). For example, banks may provide files that directly indicate accounts and/or people that have committed fraud or are associated with some other target.

At operation 204, each of the machine learning targets may be stored in a target factory 100 in the form of a network-attached database. As indicated above, the target factory 100 may be communicatively coupled with a machine learning network 108 and/or machine learning computing system 110 via one or more networks 104, which enables the targets to be accessed and used by other machine learning models. For example, at operation 206 the target factory 100 may receive a call or request for at least one machine learning target for a machine learning model. As just one example, the request may be for all users 104 and/or accounts that match a particular target, such as being identified as being or associated with a money launderer. In some embodiments, the request may include a date range for which users 104 and/or accounts associated with a given target should be returned. For example, the request may query the target factory 100 for all users 104 who have committed fraud or likely committed fraud over the last year. The identified machine learning targets may be provided to the machine learning model at operation 208. In some embodiments, a confidence indicator (which may be a numerical score in some embodiments) associated with a given user 104 and/or account may be returned along with the target. The requesting machine learning model may then use the received targets as part of an analysis for a different target or other outcome.

A computer system as illustrated in FIG. 3 may be incorporated as part of the previously described computerized devices. For example, computer system 300 can represent some of the components of computing devices, such as target factory 100, data sources 102, devices of the users 104, machine learning network 108, machine learning computing system 110, and/or other computing devices described herein. FIG. 3 provides a schematic illustration of one embodiment of a computer system 300 that can perform the methods provided by various other embodiments, as described herein. FIG. 3 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. FIG. 3, therefore, broadly illustrates how individual system elements may be implemented in a relatively separated or relatively more integrated manner.

The computer system 300 is shown comprising hardware elements that can be electrically coupled via a bus 305 (or may otherwise be in communication, as appropriate). The hardware elements may include a processing unit 310, including without limitation one or more processors, such as one or more central processing units (CPUs), graphical processing units (GPUs), special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like); one or more input devices 315, which can include without limitation a keyboard, a touchscreen, receiver, a motion sensor, a camera, a smartcard reader, a contactless media reader, and/or the like; and one or more output devices 320, which can include without limitation a display device, a speaker, a printer, a writing module, and/or the like.

The computer system 300 may further include (and/or be in communication with) one or more non-transitory storage devices 325. The one or more non-transitory storage devices 325 can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an optical storage device, a solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”). The one or more non-transitory storage devices 325 can be programmable, flash-updateable and/or the like. Such storage devices may be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, and/or the like.

The computer system 300 might also include a communication interface 330, which can include without limitation a modem, a network card (wireless or wired), an infrared communication device, a wireless communication device and/or chipset (such as a Bluetooth™ device, an 502.11 device, a Wi-Fi™ device, a WiMAX™ device, an NFC device, cellular communication facilities, etc.), and/or similar communication interfaces. The communication interface 330 may permit data to be exchanged with a network (such as the network 106, to name one example), other computer systems, and/or any other devices described herein. Data transmitted across the networks 106 may be secured using encryption techniques, hypertext transfer protocol secure (HTTPS), secure sockets layer (SSL), transport layer security (TLS), and/or other security protocol. In many embodiments, the computer system 300 will further comprise a non-transitory working memory 335, which can include a RAM or ROM device, as described above.

The computer system 300 also can comprise software elements, shown as being currently located within the working memory 335, including an operating system 340, device drivers, executable libraries, and/or other code/instructions, such as one or more application programs 345 and/or a machine learning algorithm. Such code/instructions may comprise computer programs provided by various embodiments, and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer). In an aspect, then, such special/specific purpose code and/or instructions can be used to configure and/or adapt a computing device to a special purpose computer that is configured to perform one or more operations in accordance with the described methods.