System and method for learning from the images of raw data

Aspects of the present disclosure involve a system and method for learning from images of transactional data. In one embodiment, a system is introduced that can learn from the images of transactional data. In particular, machine learning is implemented on images in order to classify information in a more accurate manner. The images are created from raw data deriving from a user account.

TECHNICAL FIELD

The present disclosure generally relates to communication devices for data classification using machine learning, and more specifically, to communication devices that learn using the images of transaction data.

BACKGROUND

Nowadays merchants, service providers, financial institutions and the like use consumer account data to tailor advertisements, make credit determinations, and determine other relevant information. However, consumer transactions and even payments are often volatile and sparse. Further, the products and services provided by the merchant, service provider, or payment provider may vary widely. For example, a payment provider like PayPal who may have upwards of 200 million active accounts, may have a part of those active accounts belong to causal consumers that make few and infrequent payments. As another example, a consumer with an account at a financial institution may complete transactions using the account at a bakery in the morning and a purchase for tires in the afternoon. These particular traits, where sparse data exists, create significant hurdles in statistical learning-based solutions such as transaction risk models (classification) and abnormality detection models (clustering). This is because the traditional statistical learning algorithms (e.g., linear regression) are less effective in summarizing cases that are abundant in sparse transaction data. Further, current statistical models cannot handle the big data and thus do not converge in a timely manner. Therefore it would be beneficial to create a model that enables the classification of large data using deep machine learning-based techniques.

DETAILED DESCRIPTION

Aspects of the present disclosure involve systems, methods, devices, and the like for determining information from the images of transactional data. In one embodiment, a system is introduced that can learn from the images of transactional data. In particular, machine learning is implemented on images in order to classify information in a more accurate manner. The images are created from raw data deriving from a user account.

Data gathered by communication devices is now being used to provide tailored advertisements, determined product and services to offer users, and even used to understand the demographics of the consumers. However, the vast amount of information being gathered needs to be organized and categorized in order to obtain the details desired. For example, a financial institution may want to determine what group of customers a new bank card should be advertised to, given that the new card offers loyalty points and comes with an annual fee. To identify the group of customers to advertise the new bank card to, the customer account information needs to be organized and categorized.

Conventionally, statistical learning models have been used to obtain this information. Statistical learning models including clustering and classification models which can provide transaction risk and abnormality detection information have been used. However, in instances where the information is too large and sparse, these models cannot perform effectively. That is to say, with the non-linearities encountered in sparse and volatile data, these conventional algorithms may not converge in a timely manner.

In one embodiment, an image transformation model is introduced that enables the classification of data using image processing which is can accurately classify sparse and volatile date. Using the image transformation model, raw data received can be normalized and mapped/projected to create an image in the form of a polytope. The polytope can then be categorized using image classification techniques such as convolutional neural networks to accurately produce outputs.

FIG. 1is a block diagram of a networked system100for implementing the processes described herein, according to an embodiment. In particular,FIG. 1illustrates a block diagram of a system100for classifying and learning from images of transactional data. As shown, system100may include or implement a plurality of devices, computers, servers, and/or software components that operate to perform various methodologies in accordance with the described embodiments. It will be appreciated that the devices, computers, and/or servers illustrated inFIG. 1may be deployed differently and that the operations performed and/or the services provided by such devices, computers, and/or servers may be combined or separated for a given embodiment and may be performed by a greater number or fewer number of devices, computers, and/or servers. Furthermore, one or more of the devices, computers, and/or servers may be operated and/or maintained by the same or different entities.

System100includes a merchant device102, a primary user device132, a third-party service provider computer112, and a secondary user device136in communication over a network150. The merchant device102, primary user device132, third-party service provider computer112, and the secondary user device136may each include one or more processors, memories, and other appropriate components for executing computer-executable instructions such as program code and/or data. The computer-executable instructions may be stored on one or more computer readable mediums or computer readable devices to implement the various applications, data, and steps described herein. For example, such instructions may be stored in one or more computer readable media such as memories or data storage devices internal and/or external to various components of system100, and/or accessible over network150.

The merchant device102may be implemented as a communication device that may utilize appropriate hardware and software configured for wired and/or wireless communication with the primary user device132, third-party service provider computer112, and/or secondary user device136. For example, the merchant device102may be implemented as a personal computer (PC), a smart phone, laptop/tablet computer, point-of-sale device, wristwatch with appropriate computer hardware resources, eyeglasses with appropriate computer hardware, other type of wearable computing device, implantable communication devices, and/or other types of computing devices capable of transmitting and/or receiving data. The merchant device102may correspond to and be utilized by a user, such as an employee of a merchant and/or another person authorized by the merchant.

The merchant device102may include one or more payment applications104, other applications106, a database108, and a network interface component110. The payment applications104and other applications106may correspond to executable processes, procedures, and/or applications with associated hardware. In other embodiments, merchant device102may include additional or different components having specialized hardware and/or software to perform operations associated with the payment applications104and/or the other applications106.

The payment application104may facilitate financial transactions corresponding to the sale of goods and/or services offered by the merchant. For example, the payment application104may provide an interface for customers to purchase the goods or services and to receive customer payment information (e.g., customer credit card information). The payment application104may further transmit customer payment information to a payment processor (e.g., such as a payment processor corresponding to the third-party service provider computer112or the secondary user device136) to process the customer payment information. The payment application104may also facilitate other types of financial transactions such as banking, online payments, money transfer, and/or the like.

The merchant device102may execute the other applications106to perform various other tasks and/or operations corresponding to the merchant device102. For example, the other applications106may include security applications for implementing client-side security features, programmatic client applications for interfacing with appropriate application programming interfaces (APIs) over network150, or other types of applications. In various embodiments, the other applications106may include social networking applications. Additionally, the other applications106may include device interfaces and other display modules that may receive input and/or output information. For example, the other applications106may include a graphical user interface (GUI) configured to provide an interface to the user.

The merchant device102may further include a database108, which may be stored in a memory and/or other storage device of the merchant device102. The database108may include, for example, identifiers (IDs) such as operating system registry entries, cookies associated with the payment application104and/or other applications106, IDs associated with hardware of the network interface component110, IDs used for payment/user/device authentication or identification, and/or other appropriate IDs. The database108may also include information corresponding to one or purchase transactions of customers who have purchased goods or services from the merchant, browsing histories of the customers, or other types of customer information. In certain embodiments, the merchant device102may also include information corresponding to payment tokens, such as payment tokens generated by the third-party service provider computer112and/or generated by the secondary user device136.

The merchant device102may also include at least one network interface component110configured to communicate with various other devices such as the primary user device132, the third-party service provider computer112, and/or the secondary user device136. In various embodiments, network interface component210may include a Digital Subscriber Line (DSL) modem, a Public Switched Telephone Network (PTSN) modem, an Ethernet device, a broadband device, a satellite device and/or various other types of wired and/or wireless network communication devices including microwave, radio frequency, infrared, Bluetooth®, Bluetooth low-energy, near field communication (NFC) devices, and/or the like.

The third-party service provider computer112may be maintained, for example, by a third-party service provider, which may provide payment processing services for the merchant. In one example, the third-party service provider may be provided by PAYPAL™ Inc. of San Jose, Calif., USA. Alternatively, the third-party service provider computer112may be associated with a user of the primary and secondary device132,136. As such, the third-party service provider computer112includes one or more payment processing applications114, which may be configured to process payment information received from the merchant device102or from a selection at the primary or secondary user device132,136. For example, the payment application104of the merchant device102may receive payment information from a customer to purchase a service or good offered by the merchant. Upon receipt of the payment information, the payment application104may transmit the payment information to the third-party service provider computer112. The payment processing application114of the third-party service provider computer112may receive and process the payment information. As another example, the payment application104can present a payment code on a display of the user device associated with the merchant The payment code can be scanned or transmitted to the merchant device102for payment processing.

The third-party service provider computer112may execute the other applications116to perform various other tasks and/or operations corresponding to the third-party service provider computer112. For example, the other applications116may include security applications for implementing client-side security features, programmatic client applications for interfacing with appropriate APIs over the network150, or other types of applications. The other applications116may also include additional communication applications, such as email, texting, voice, and IM applications that enable communication of emails, calls, texts, and other notifications through the network150. In various embodiments, the other applications116may include location detection applications, such as a mapping, compass, and/or GPS applications, which may be used to determine a location of the third-party service provider computer112. Additionally, the other applications116may include device interfaces and other display modules that may receive input and/or output information. For example, the other applications116may include a GUI configured to provide an interface to one or more users.

The third-party service provider computer112may further include a database118, which may be stored in a memory and/or other storage device of the third-party service provider computer112. The database118may include, for example, IDs such as operating system registry entries, cookies associated with the payment processing application114and/or other the applications116, IDs associated with hardware of the network interface component122, IDs used for payment/user/device authentication or identification, and/or other appropriate IDs.

According to a particular embodiment, the third-party service provider computer112may include a set of payment profiles120corresponding to past sales transactions executed by the merchant device102with respect to one or more customers of the merchant. Alternatively, the third-party service provider computer112may include a set of merchant payment profiles corresponding to the payment sources associated to a corresponding merchant. For example, a particular payment profile from the set of payment profiles120may include payment information corresponding to a particular customer of the merchant and/or a merchant associated with a user. The payment information may include credit card information (e.g., encrypted card number, expiration date, security code, card issuer, and/or the like), Automated Clearing House (ACH) information (e.g., encrypted account number, routing number, and/or the like), identification information associated with the particular customer/user (e.g., a customer identifier, name, address, phone number, date of birth, and/or the like), billing information, credit score, and/or any other type of payment information associated with the particular customer. Furthermore, other payment profiles of the set of payment profiles120may include payment information corresponding to other customers of the merchant and/or other merchants associated with the user. In addition, the third-party service provider computer112may store the set of payment profiles120according to a first file format.

The third-party service provider computer112may also store a set of payment tokens corresponding to the set of payment profiles120. For example, each payment profile of the set of payment profiles120may be associated with a corresponding payment token from the set of payment tokens. In some embodiments, each payment profile may include a corresponding payment token from the set of payment tokens. The set of payment tokens may be particular to the third-party service provider computer112(e.g., computers from other service providers may be unable to use the set of payment tokens) and may enable the merchant device102to more securely process payment transactions with the third-party service provider computer112. For example, in order to process a payment transaction that involves a credit card number associated with a particular payment profile, the third-party service provider computer112may provide the merchant device102with a particular payment token that is different from the credit card number. The merchant device102may use the particular payment token to process the payment transaction instead of the credit card number. Further, the merchant device may store and associate the particular payment token with the particular payment profile instead of the credit card number, thereby protecting the credit card number from being stolen in a potential security breach of the merchant device102.

In various embodiments, the third-party service provider computer112also includes at least one network interface component122that is configured to communicate with the merchant device102, the primary user device132, and/or the secondary user device136via the network150.

The third party provider computer112, may also include a data classification component124that may be used for raw data classification. In one embodiment, the raw data received by the third-party service provider computer112and/or stored in database118can be analyzed to identify correlations in the data. For the analysis, the raw data can be converted into images that may be used in conjunction with image classification techniques for identifying the correlations. In some instances, the raw data may derive from user transactions and account information received at a merchant device102, with a financial institution, a payment provider, at a secondary user device136, at a primary user device132, etc. As an example, raw data/information can be extracted from the user accounts stored in database118at a third-party service provider computer (e.g., PayPal). The user account information can be used for example, to identify high risk customers vs. low risk customers. For the identification, relevant account information (e.g., credit score, user name, etc.) for each customer can be mapped to an image.

Alternatively, the entire account information of the customer can be mapped to an image. Image mapping can occur using a data transformation technique that maps customer features onto a circle to create a polytope. The image can then be analyzed using image classification techniques such that the output of the image classification provides the data required to determine whether the customer associated with the account information analyzed is a high risk or a low risk customer. Note, further details on transformation and classification will be described in more detail below and in conjunction withFIGS. 3A-3D and 4. In addition, the data classification component124may also be implemented at the merchant device102, primary user device132, and secondary user device136for learning and identifying information. In some instances, consumer information may be shared between the devices102,124,132,136for classifying and converting the transactional data.

The primary user device132may be implemented as a communication device that may utilize appropriate hardware and software configured for wired and/or wireless communication with the merchant device102, third-party service provider computer112, and/or the secondary user device136. The primary user device132, may be a personal computer (PC), a smart phone, laptop/tablet computer, wristwatch with appropriate computer hardware resources, eyeglasses with appropriate computer hardware (e.g. GOOGLE GLASS®), other type of wearable computing device, implantable communication devices, and/or other types of computing devices capable of transmitting and/or receiving data. In one embodiment, the primary user device132may be mobile device communicating with wearable device (secondary user device136), merchant device102, or directly with the third-party service provider system112.

The primary user device132may include a payment processing application126that may be used as a digital wallet that can communicate with a merchant device102, secondary user device136, and/or third party service provider112for purchasing and transacting. The payment processing application126, can work jointly with database130for retrieving bank account information, user accounts, security codes, tokens that may be associated with various merchant locations. Similarly, the payment processing application, can also provide access the user profiles for determining which payment method, processing code, to use at a merchant location.

The primary user device132may also include other applications128to perform various other tasks and/or operations corresponding to the primary user device132. For example, the other applications128may facilitate communication with the merchant device102, such as to receive an indication, from the merchant device102, to switch payment processing services from the third-party service provider to the service provider. As another example, the other applications128may include security applications, application that enable designation of a primary interactive device, and applications that allow for web site searches (including access to merchant websites). The other applications128may also include additional communication applications, such as email, texting, voice, and IM applications that enable communication of emails, calls, texts, and other notifications through the network150. In various embodiments, the other applications128may include location detection applications, such as a mapping, compass, and/or GPS applications, which may be used to determine a location of the primary user device132. The other applications128may include social networking applications. Additionally, the other applications128may include device interfaces and other display modules that may receive input and/or output information. For example, the other applications128may include a GUI configured to provide an interface to one or more users.

The primary user device132may further include a database130, which may be stored in a memory and/or other storage device of the primary user device132. The database130may include, for example, identifiers (IDs) such as operating system registry entries, cookies associated with a web browser and/or the other applications128, IDs associated with hardware of the network interface component134, IDs used for payment/user/device authentication or identification, bank information, merchant information, user accounts, and/or other appropriate IDs.

The primary user device132may also include at least one network interface component134configured to communicate with various other devices such as the merchant device202, the third-party service provider computer112, and/or the secondary user device136.

As indicated above, a secondary user device136may be presently available and used by a consumer. The secondary user device136, much like the primary user device may be equipped with payment processing applications138as well as other applications140. The payment processing applications138and other applications140that include social networking applications and device interfaces and other display modules that may receive input and/or output information.

The secondary user device136may also include a network interface component142for connecting and interacting with at least primary user device132, merchant device102, and/or third-party service provider computer112over network150.

The network150may be implemented as a single network or a combination of multiple networks. For example, in various embodiments, the network150may include the Internet or one or more intranets, landline networks, wireless networks, and/or other appropriate types of networks. Thus, the network150may correspond to small scale communication networks, such as a private or local area network, or a larger scale network, such as a wide area network or the Internet, accessible by the various components of system100.

FIG. 2illustrates a block diagram of a system for the overall methodology in classifying and learning from images of transactional data. In particular,FIG. 2illustrates a block diagram of a methodology200for converting raw data into images and then classifying or clustering the images to obtain information from the raw data received. The methodology200for classifying information begins with the raw data202. As previously indicated and with reference to the third-party service provider computer112, a payment provider will be used for exemplary purposes. The payment provider has many customer accounts which contain a vast amount of information about the customer. In the data classification process, each customer may be represented by a sequence of data/features. For example, the customer account may have information about the customer including age, zip code, transaction history, credit score, etc. This information may be represented as a feature associated with each of the customers. So that the raw data can, for example, be organized as an N×K matrix in which every row represents an account with a financial institution or a consumer transaction and each column represents the corresponding K features associated with each of the N accounts/customers/transactions associated with the accounts.

The organized data is then input into image transformation unit204that can convert the raw input data into images. The image transformation unit204can process the raw data on a row-by-row basis such that each customer account is represented by an image. Therefore, up to N images can be output from the image transformation unit204. The images can then be analyzed by image classification unit206so that relevant information can be extracted from the features of the various customers. For example, as part of a marketing campaign, a payment provider would like to determine which users are over50years old and have made over100purchases in the last year with a specific merchant. The classified image information can provide such information by presenting those images that fit the criteria. Alternatively, the desired criteria can be identified during the image transformation such that output208from the image classification unit206are those images that match the criteria. Image classification can occur one of the various machine learning models available. For example, transactional risk models and clustering models have been previously used. In one embodiment, a convolutional model, such as a convolutional neural network model is used.

Note that various other implementations of the image transformation and classification may be contemplated.FIG. 2illustrates but one example of data transformation for simplicity.

FIGS. 3A-3Dillustrate a graphical diagram of a system for converting raw data into an image. In particular,FIGS. 3A-3Dillustrate a methodology for generating images from the raw data. As indicated above, each customer may be represented by an image and each customer account may include various features that describe the customer. Therefore, to begin with the transformation, the features are mapped to a circle, which is used to create a polygon representing the raw data as an image.FIG. 3Abegins with circle300, which represents the customer. The circle can then be divided into radii310, where each radius310represents a feature. Note that in some instances, all customer features may be mapped on the image, while in other instances, a lesser and more relevant number of features are mapped.

Therefore, a customer u (represented by circle300) may have k features (each represented by a radius310), such that the circle300is divided into k partitions.FIG. 3Aillustrates a circle300with k=8 partitions. To provide an illustration of how this would map to a customer, assume a payment provider has John as a customer. John's account with the payment provider may include user identification features including, but not limited to, John's age, zipcode, credit score, and purchase history. Therefore, if John's raw information was mapped to a circle u, John's circle would include 4 equally spaced partitions created by the 4 radii, where each radius would represent a feature (e.g., age, zipcode, credit score, purchase history).

Once, the customer circle300has been partitioned, each feature is normalized to fit on the unit circle300, where the origin is 0 and the circumference of the circle300is 1 as illustrated inFIG. 3B. For example, returning to John's example, if John is 50 years old, and the ages of customers are considered to lie between 0 and 150 year old, then John's age could be normalized to a ⅓ value. Therefore, one of the radii310representing age could be used to describe the age feature. For example, John's age (normalized to ⅓) can be projected onto the age radius, denoted by unit312, which lies about a ⅓ of the way from the center. Similarly, another feature which lies about ⅔ of the way from the center after being normalized can be projected onto the corresponding feature radius using unit314.

This process can be generalized for any application such that the mapping can be generally described as having a set of n instances {I1, I2, . . . I3}, each having k variables (vi,1, vi,2, . . . vi,k),1≤i≤n. The variables (e.g., features) can be normalize such that vi,j∈[0,1] for 1≤i≤n and 1≤j≤k. For numerical variables such as transaction amount, transaction velocity, account days on file and payment history, the normalization process may be straight forward. However, an appropriate treatment of the missing values is desired to ensure data integrity. For categorical variables such as consumer primary residence, account type, geolocation, and product category, the value of such numbers (e.g., {0, 1, 2 . . . } may be mapped based on the alphabetical ordering of the values before performing the normalization. For example, for the product color variable taking values from “black”, “blue”, “green”, and “grey”, the values may be normalized to {0, ⅓, ⅔, 1} respectively. The closer a variable is to zero, the closer the point is to the center.

Once the points (e.g., units representing the features) have all been identified on the radius of the circle, the center of the circle and each pair of adjacent points may be connected to each of the points in order to form a polygon316as illustrated inFIG. 3C. The polygon316as illustrated inFIG. 3Cbecomes an image representation of the raw data.

To determine what a particular value of a pixel318on the polygon316, the polygon316is converted into a three dimensional (3D) polytope308. For the conversion, the center of the polygon is given a height of 1, while the boundaries of the polygon lie at zero. A polytope is a geometric object with flat sides and the grey scale value of a pixel can be represented using the polytope ofFIG. 3D. By regarding the polygon as a 3-D object, a point of interest (represented by pixel at point318) can be determined by drawing a vertical line on the projected point. For simplicity, the 3D polytope308can represent a triangle which has an edge on the paper and the center point on the polygon center.

As indicated inFIG. 2, once raw data has been transformed into an image, the image may be classified to extract correlations that can be achieved with high precision.FIG. 4illustrates a block diagram of a system for image classification. The intent of image classification is to categorize all pixels into several classes such that all pixels fall into or may be represented by a class. The classes can be used to produce a map of the land covered by the image. The objective is to identify the type of land covered by the greyscale pixel.

Generally, deep machine learning algorithms can be used to perform this categorization of pixels. In image processing, modern image recognition models such as convolutional neural networks (CNNs, ConvNet, LeNet, etc) have be used.FIG. 4, illustrates an exemplary image classification that may be used on polytope320. Image processing algorithms or software that may be included in data classification component124can identify classes of interest in the polytope image308. In some instances, a Cifar-10 dataset may be used for object recognition, which has been recognized to achieve rates upwards of 95.53% over 10 classes. Therefore, pixel318can be represented by a class by iterating and comparing the pixel grey value signature to land signatures/classes that produce the map the cover the image (e.g., polytope320). As illustrated, the classification can occur over numerous iterations that include the convolution and pooling of the data until all pixels are classified and the desired output is acquired.

Note that in some instances, in image processing in order to obtain more accurate results, it may be useful to increase resolution. Higher image resolution leads can lead to a higher demand on computational power and storage space. Therefore, in one embodiment, the features/variables can be mapped to a logarithmic space after normalizing. In some instances, normalizing multiple times can provide an improved image resolution (e.g., improve polytope shape) such that more accurate results may be achieved. In other embodiments, the features/variables can be mapped to an exponential space after normalizing. Therefore, after the raw data is gathered and organized as illustrated inFIG. 2, the data may be organized, mapped and then normalized again to achieve an increase image resolution.

FIG. 5illustrates example process500for classifying and learning from images of transaction data that may be implemented by a system, such as system100ofFIG. 1. In particular,FIG. 5illustrates a flow diagram illustrating operations for learning from images of raw data. According to some embodiments, process500may include one or more of operations502-514, which may be implemented, at least in part, in the form of executable code stored on a non-transitory, tangible, machine readable media that, when run on one or more hardware processors, may cause a system to perform one or more of the operations502-514.

Process500may begin with operation502, where raw data is gathered. The raw data gather will be transformed to enable classification and statistical modeling with high precision. The raw data can originate from any information gathering mechanisms that collect data for the purpose of making correlations or learning from the information. For example, the raw data can derive from sensors on an automobile used for autonomous driving, raw data can originate from crowdsourced information that is received for testing a new product/application or the raw data can originate from users inputting information when creating a user account. Note that numerous other sources of data exist and may be contemplated.

In one embodiment, the raw data can be collected from user accounts associated with a financial institution, service provider, and/or payment provider. The raw data can correspond to the user information associated with the account. For example, a user may have an account with a service provider and the raw data for that account may include zip code, purchase history, credit score, account number, etc.

Once the information or raw data has been gathered, the process continues to operation504, where the data is organized in matrix form. For example, the data can be organized such that instances (e.g., customers/user accounts) are organized into N rows and the corresponding features (e.g., account information) are organized into K columns, creating an N×K matrix of information. The organization of the raw data in this format, allows for individual instances or user accounts to be transformed, classified, and analyzed. For example, in process500, the organized data arriving at decision block506for determining relevant features can be on a row-by-row basis, such that a single account is analyzed at a time. Alternatively, all raw data can be manipulated simultaneously.

In addition, some or all relevant features may be selected for analyzing to obtain the information desired in operation506. For example, if a payment provider is trying to understand the demographics of its account holders in terms of how many fall within a given age group and have a credit score above a predetermined value, then zip codes of each account holder example may not be that relevant. Alternatively, if the information desired includes identifying those customers above a given age group, a credit score, and further the geography of those customers, then zip codes as well as other features may be relevant in the analysis. Therefore, in operation506a determination is made as to whether the current feature is needed. If the feature is relevant, then the process can continue to operation510where the data associated with the relevant feature(s) is transformed into an image in operation510. However, if the feature (e.g., date of last purchase) is not relevant, then process continues to operation508where the feature is skipped. Note that each feature may be considered by the decision block in operation506until all features associated with the current customer account have been considered.

Once the data corresponding to the relevant features has been determined, process500continues to operation510where the raw data is transformed into an image. In operation510, a model is introduced that can take the raw data gathered and transform it into another dimension (e.g. an image) which can leverage deep machine learning technology to provide more detailed information. Deep machine learning technology implements techniques that allow the data to be seen and analyzed from a different prospective. By converting raw data into images, image classification techniques can be used that allow the classification of information that is not possible by other models. For example, transactional data can be highly volatile and not uniformly distributed, as such, conventional statistical learning-based solutions (e.g., transaction risk models—classification, abnormality detection-cluster) are less effective in summarizing the transactional data.

In one embodiment, an image transformation model is introduced that utilizes the power of image processing, computer vision, and GPU computing techniques to obtain highly accurate output predictions. Generally, the higher image resolution the more accurate the results. In particular, this model as described in greater detail above and in conjunction withFIG. 3, transforms the raw data received into an image by mapping the raw data onto a circle. Mapping the raw data onto a circle may be accomplished by normalizing the features (e.g., vi,j∈[0,1] for 1≤i≤n) identified in operation506such that they can be represented by a radius on the circle enables the representation of the relevant features on the circle. For example, a credit scores can range between 500-850 and a 750 credit score is to be mapped on the unit circle. The credit score can be represented such that the center of the circle is 500 and the perimeter of the circle is 850, thus 750 would be mapped about 0.71 (e.g., ((750-500)/(850-500))) from the center. This process continues until the relevant features are mapped as a corresponding unit. Once the raw data has been represented as units on the circle, the units are connected to create a polygon. The closer the feature lies to zero, the close it is to the center of the circle. Alternatively, the further from the center, the further the feature is from zero and closer to unity. Next, in order to determine what a particular value of a pixel on the polygon, the polygon is converted into a three dimensional (3D) polytope. For the conversion, the center of the polygon is given a height of 1, while the boundaries of the polygon lie at zero.

Once an image representation of the raw data is achieved, then process500continues to the image classification in operation514. Image classification enables the identification of the information desired, that may otherwise not be captured by optimization-based conventional algorithms due to the non-linearities in the information. Instead, an image classification technique such as conventional neural networks can be used which can provide high accuracy. For example, Cifar-10, Mixed National Institute of Standards and Technology (MNIST), NORB, HWDB1.0, ImageNet, etc. datasets may be used. As indicated, CIFAR-10 is an established computer-vision dataset used for object recognition which is a Cifar-10 has achieved an accuracy rate of 95.53% over 10 classes. As indicated inFIG. 4, image classification categorizes pixels into classes, such that all pixels fall into a class. The classes can be used to produce a map of the land covered by the image with the objective being that a type of land is identified for each greyscale pixel. Once the image classification is complete, the process concludes with an output of the desired information.

To ensure correct functionality of the data transformation model presented as well as the image classification method proposed methodology was tested on a classification problem with 50,000 instances (e.g., consumer accounts) with two years of transactional history data. The goal of the classification was to separate high credit risk account from the rest of the population. The experiment was conducted over a 120 hour period using a GeFroces GTX 780 (e.g., Nvidia GPU) machine running a Linux OS. The results, as illustrated inFIG. 7, provided proof of concept as the images associated with high credit risk were distinguished from the other accounts. In particular,FIG. 7illustrates exemplary images derived for customers with bad credit. In addition, the system also provided numeric scores as widely used by risk models. Note that this type of classification would not be possible using conventional models due to the non-linearities in the data and the extent of information as conventional algorithms can manage data volumes normally under 1 MM rows with hundreds of variables at most.

FIG. 6illustrates an example computer system600in block diagram format suitable for implementing on one or more devices of the system inFIG. 1. In various implementations, a device that includes computer system600may comprise a personal computing device (e.g., a smart or mobile device, a computing tablet, a personal computer, laptop, wearable device, PDA, etc.) that is capable of communicating with a network626. A service provider and/or a content provider may utilize a network computing device (e.g., a network server) capable of communicating with the network. It should be appreciated that each of the devices utilized by users, service providers, and content providers may be implemented as computer system600in a manner as follows.

Additionally, as more and more devices become communication capable, such as new smart devices using wireless communication to report, track, message, relay information and so forth, these devices may be part of computer system600. For example, windows, walls, and other objects may double as touch screen devices for users to interact with. Such devices may be incorporated with the systems discussed herein.

Computer system600may include a bus610or other communication mechanisms for communicating information data, signals, and information between various components of computer system600. Components include an input/output (I/O) component604that processes a user action, such as selecting keys from a keypad/keyboard, selecting one or more buttons, links, actuatable elements, etc., and sending a corresponding signal to bus610. I/O component604may also include an output component, such as a display602and a cursor control608(such as a keyboard, keypad, mouse, touchscreen, etc.). In some examples, I/O component604may include an image sensor for capturing images and/or video, such as a complementary metal oxide semiconductor (CMOS) image sensor, and/or the like. An audio input/output component606may also be included to allow a user to use voice for inputting information by converting audio signals. Audio I/O component606may allow the user to hear audio. A transceiver or network interface622transmits and receives signals between computer system600and other devices, such as another user device, a merchant server, an email server, application service provider, web server, a payment provider server, and/or other servers via a network. In various embodiments, such as for many cellular telephone and other mobile device embodiments, this transmission may be wireless, although other transmission mediums and methods may also be suitable. A processor618, which may be a micro-controller, digital signal processor (DSP), or other processing component, that processes these various signals, such as for display on computer system600or transmission to other devices over a network626via a communication link624. Again, communication link624may be a wireless communication in some embodiments. Processor618may also control transmission of information, such as cookies, IP addresses, images, and/or the like to other devices.

Components of computer system600also include a system memory component614(e.g., RAM), a static storage component614(e.g., ROM), and/or a disk drive616. Computer system600performs specific operations by processor618and other components by executing one or more sequences of instructions contained in system memory component612. Logic may be encoded in a computer readable medium, which may refer to any medium that participates in providing instructions to processor618for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and/or transmission media. In various implementations, non-volatile media includes optical or magnetic disks, volatile media includes dynamic memory such as system memory component612, and transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus610. In one embodiment, the logic is encoded in a non-transitory machine-readable medium. In one example, transmission media may take the form of acoustic or light waves, such as those generated during radio wave, optical, and infrared data communications.

Some common forms of computer readable media include, for example, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer is adapted to read.

Components of computer system600may also include a short range communications interface620. Short range communications interface620, in various embodiments, may include transceiver circuitry, an antenna, and/or waveguide. Short range communications interface620may use one or more short-range wireless communication technologies, protocols, and/or standards (e.g., WiFi, Bluetooth®, Bluetooth Low Energy (BLE), infrared, NFC, etc.).

Short range communications interface620, in various embodiments, may be configured to detect other devices (e.g., primary user device132, secondary user device136, merchant device102, etc.) with short range communications technology near computer system600. Short range communications interface620may create a communication area for detecting other devices with short range communication capabilities. When other devices with short range communications capabilities are placed in the communication area of short range communications interface620, short range communications interface620may detect the other devices and exchange data with the other devices. Short range communications interface620may receive identifier data packets from the other devices when in sufficiently close proximity The identifier data packets may include one or more identifiers, which may be operating system registry entries, cookies associated with an application, identifiers associated with hardware of the other device, and/or various other appropriate identifiers.

In some embodiments, short range communications interface620may identify a local area network using a short range communications protocol, such as WiFi, and join the local area network. In some examples, computer system600may discover and/or communicate with other devices that are a part of the local area network using short range communications interface620. In some embodiments, short range communications interface620may further exchange data and information with the other devices that are communicatively coupled with short range communications interface620.

In various embodiments of the present disclosure, execution of instruction sequences to practice the present disclosure may be performed by computer system600. In various other embodiments of the present disclosure, a plurality of computer systems600coupled by communication link624to the network (e.g., such as a LAN, WLAN, PTSN, and/or various other wired or wireless networks, including telecommunications, mobile, and cellular phone networks) may perform instruction sequences to practice the present disclosure in coordination with one another. Modules described herein may be embodied in one or more computer readable media or be in communication with one or more processors to execute or process the techniques and algorithms described herein.

A computer system may transmit and receive messages, data, information and instructions, including one or more programs (i.e., application code) through a communication link624and a communication interface. Received program code may be executed by a processor as received and/or stored in a disk drive component or some other non-volatile storage component for execution.

Software, in accordance with the present disclosure, such as program code and/or data, may be stored on one or more computer readable media. It is also contemplated that software identified herein may be implemented using one or more computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.

The foregoing disclosure is not intended to limit the present disclosure to the precise forms or particular fields of use disclosed. As such, it is contemplated that various alternate embodiments and/or modifications to the present disclosure, whether explicitly described or implied herein, are possible in light of the disclosure. For example, the above embodiments have focused on merchants and customers; however, a customer or consumer can pay, or otherwise interact with any type of recipient, including charities and individuals. Thus, “merchant” as used herein can also include charities, individuals, and any other entity or person receiving a payment from a customer. Having thus described embodiments of the present disclosure, persons of ordinary skill in the art will recognize that changes may be made in form and detail without departing from the scope of the present disclosure. Thus, the present disclosure is limited only by the claims.