Patent Publication Number: US-2022230173-A1

Title: Self-service terminal (sst) item return anti-fraud processing

Description:
BACKGROUND 
     Retailers struggle managing the returns process in their stores. Key challenges include staffing the customer returns desk to ensure a good customer experience; providing a favorable customer experience during returns since it is one of the longest and most important interactions between stores and their customers with a major impact on brand loyalty; handling the disposition of the returned item; and limiting retail fraud (which is substantial with returned items). 
     Furthermore, item return procedures are typically the same for any returned item regardless as to whether some items are more likely to being associated with fraudulent returns than other items and regardless as to whether an expense associated with the staff processing a particular item return is greater than just accepting the corresponding item with or without a modified returns procedure by the retailer. 
     In fact, hassling a customer over an item priced at only a few dollars in order to follow the existing returns process when the customer is a loyal customer can be very detrimental to the reputation of the retailer. Staff time in processing the return can cost the retailer more than just simply accepting the return with little to no questions asked. However, staff often lack any authority to override the existing process, which usually requires a manager&#39;s approval. Long customer queues are not uncommon at returns desks, which can require additional staff to address and which does frustrate the retailer&#39;s customers. 
     There is no existing technology available in the industry that addresses these challenges even though item returns consume a substantial amount of a retailer&#39;s resources and even though item returns are associated with significant retail fraud. 
     SUMMARY 
     In various embodiments, methods a system for Self-Service Terminal (SST) item return anti-fraud processing are presented. 
     According to an embodiment, a method for SST item return anti-fraud processing is presented. An item associated with an item return transaction is identified and an item image is obtained for the item as provided by a transaction terminal that is processing the item return transaction. The item is authenticated based on an item code for the item and the item image. Transaction history data associated with the item, a customer associated with the item return transaction, and a store associated with the item return transaction are obtained. A fraud score is calculated based on the transaction history data and an item return rating for the customer is obtained. A determination is made based on the fraud score and the item return rating whether the item return transaction can complete without assistance at the transaction terminal or whether the item return transaction is to be suspended for assistance and an audit. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is a diagram of a system for SST item return anti-fraud processing, according to an example embodiment. 
         FIG. 1B  is a diagram of a method using the system of  FIG. 1A  for SST item return anti-fraud processing, according to an example embodiment. 
         FIG. 1C  is a diagram of another method using the system of  FIG. 1A  for SST item return anti-fraud processing, according to an example embodiment. 
         FIG. 2  is a diagram of a method for SST item return anti-fraud processing, according to an example embodiment. 
         FIG. 3  is a diagram of another method for SST item return anti-fraud processing, according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1A  is a diagram of a system for SST item return anti-fraud processing, according to an example embodiment. It is to be noted that the components are shown schematically in greatly simplified form, with only those components relevant to understanding of the embodiments being illustrated. 
     Furthermore, the various components (that are identified in architecture  100 ) are illustrated and the arrangement of the components are presented for purposes of illustration only. It is to be noted that other arrangements with more or less components are possible without departing from the teachings of SST item return anti-fraud processing, presented herein and below. 
     System  100  provides techniques by which a customer can perform an item return at an SST without requiring any assistance. The returned item(s) can be deposited in a bin adjacent or affixed to the SST. The item is inspected using computer vision analysis and sensor data provided from sensors associated with the SST (such as item weight). A plethora of data is analyzed to determine in real-time whether the returned item is legitimate and whether the item return itself is legitimate. The data can include transaction and return data for the local store associated with the SST, retail transaction data and return data for the retailer as a whole who is associated with the local store, feature attributes of the images taken of the item (including size, dimension, predominate color, etc.), customer transaction and return data, most-recent customer transaction and return data, a fraud history associated with the returned item, a weight of the returned item as noted by a weigh scale of the SST, and any existing customer item return score or grade maintained for the customer. The data is analyzed to produce a fraud score and the fraud score is used to determine whether the item return can proceed through self-service operation of the SST by the customer or whether a notification needs to be sent for intervention and assistance during the item return transaction. Based on the fraud score for the item return, a maintained customer item return score or grade is updated (or created if this was a first item return being performed by the customer with the retailer). 
     Many different embodiments of the above-noted techniques are presented herein and below. 
     As used herein and below, the term “operator” refers to a customer or retail staff interacting with interfaces of a device. A “device” refers to any device processing the interfaces for which an operator interacts with during an item return. 
     The terms “staff,” “staff member,” “user,” “customer,” “consumer,” “personnel,” “actor,” “person,” “individual,” and “clerk” may be used interchangeably and synonymously with the term “operator.” 
     A “transaction terminal” is a type of device that is used to perform transactions (purchases and returns), which includes a variety of integrated peripheral devices or which is interfaced to peripheral devices. The peripheral devices comprise card readers, currency/coin acceptors and dispensers, scanners, weigh scales, integrated scanners with weigh scales, touch displays, cameras, etc. 
     In an embodiment, when the operator is a staff member of a store, the transaction terminal is a Point-Of-Sale (POS) terminal. 
     In an embodiment, when the operator is a customer, the transaction terminal is an SST. 
     It is noted that in some cases, the transaction terminal may be a mobile device that is interfaced to over a wireless connection peripheral devices, such as integrated scanners with weigh scales, scanners, weigh scales, card readers, etc. The mobile device may be a specialized device provided by the store to the customer, a customer device with a specialized mobile application, or a staff member device. The mobile device may be a phone, a laptop, a tablet, or a wearable processing device. 
     For purposes of the discussion that follows, the transaction terminal  120  is described within the context of an SST operated by the customer during a self-item return transaction with a retailer at the retailer&#39;s store. However, it is to be noted that in other embodiments, the transaction terminal  120  may be any of the above-noted devices. 
     System  100  comprises a cloud/server  110  and a transaction terminal  120 . 
     Cloud/Server  110  comprises at least one processor  111  and a non-transitory computer-readable storage medium  112 . Medium  112  comprises executable instructions for transaction manager  112 , item return manager  114 , fraud manager  115 , customer grade manager  116 , and alter/notification manager  117 . The executable instructions when provided to processor  111  cause processor  111  to perform the processing described herein and below with respect to  113 - 117 . 
     Transaction terminal  120  comprises at least one processor  121  and a non-transitory computer-readable storage medium  122 . Medium  122  comprises executable instructions for transaction agent  123  and item return agent  124 . The executable instructions when provided to processor  121  cause processor  121  to perform the processing described herein and below with respect to  123 - 124 . 
     Optionally, each or some of transaction terminals  120  may include integrated peripheral devices, such as camera  125 , scanner  126 , weigh scale  127 , payment/gift/loyalty card reader  128 , and microphone  129 . 
     In some embodiments, camera  125  and microphone  129  may be separate stand-alone network appliances that provide images and audio from objects and persons during item returns at a given transaction terminal  120 . So, cameras  125  and microphones  129  need not be integrated into any given transaction terminal  120 . But there may be a plurality of different types of transaction terminals  120 , such that some devices  120  include integrated cameras  125  and microphones  129  while other devices  120  lack  125  and  129  and are optionally being monitored by a network-based cameras  128  and/or microphones  129 . 
     Additionally, scanner  126  may be integrated with weigh scale  127  for a combined peripheral device. 
     Furthermore, as referenced above, terminal  120  may be a mobile device that interacts with standalone peripheral devices over wireless connections. 
       FIG. 1B  is a diagram of a method  100 A using the system of  FIG. 1A  for SST item return anti-fraud processing, according to an example embodiment. 
     During operation of system  100  (beginning at the left in  FIG. 1B  and continuing from left to right), a customer enters a store and proceeds with one or more items to return to an SST  120 . Transaction agent  123  identifies the customer through a variety of mechanisms using one or more peripheral devices  125 - 129  (various options for identification are discussed below with the  FIG. 1C ). A user-facing transaction interface is presented on a touch display of SST  120 , and the user selects a self-return transaction, this causes transaction agent  123  to provide control for the self-return transaction to item return agent  124 . Item return agent then renders various item return screens with guided instructions to the customer and the customer is asked to scan on item code on the item being returned using scanner  126  (not for a mobile device embodiment of terminal  120  the scanner may be an integrated camera of the mobile device). 
     Item return agent  124  contacts item return manager  114  over a network connection and requests that the item code be looked up and identified. The item information along with item pricing is returned from item return manager  114  to item return agent  124 . Item return manager  114  may also interact via Application Programming Interface (API) calls with an inventory system for purposes of determining the total number of items in stock or whether the item is out of stock (it&#39;s also possible that the item is available in the store but there are no recorded sales for the item). If the item code is not found or there is no indication of the store having sold any of the items, Item return manager  114  sends a notice to item return agent  124  causing item return agent  124  to suspend the item return transaction. Item return manager  114  also sends a notice to a staff member to go to the SST  110  for inspection of the item being returned (identified as “Raise Remote Intervention” in  FIG. 1B ). 
     Assuming item return manager  114  identifies the item being returned and sends the item information and pricing to item return agent  124 . Item return agent  124  may instruct the customer through the transaction interface to place the item on the weigh scale  127  for security checks. 
     Item return agent  124  notes the recorded weight provided by the weigh scale  127  and obtains images taken by one or more cameras  125  situated at different angles relative to the item on the weigh scale  127 . The weight and images are obtained by item return manager  114  and Image analysis is performed on the image to calculate the item&#39;s shape, dimensions, size, edges, height, length, and/or width. The image analysis also provides a color attributes for the item, which may be a predominate color or a color signature calculated from the colors recorded in the images. 
     The weight, dimensions, and color signature of the item are compared against expected and known weight ranges, dimension ranges, and color signature for a model item. Item return manager  114  then determines based on this, whether the item is damaged or counterfeit (not what it was purported to be according to the scanned item code). Assuming the item is either badly damaged or is counterfeit, Item return manager  114  sends the self-item return transaction data, customer information, item information, customer item return grade, and calculated fraud score to alert/notification manager  117 . Alert/notification manager  117  sends a notification or an alert to item return agent  124  and to a staff-operated devices or local-store systems. Responsive to the alert, item return agent  124  suspends the self-item return transaction preventing the customer from completing the self-item return transaction at the SST  120 . A staff member is also dispatched to the SST  120  to intervene with customer assistance and to inspect/audit the item and the self-item return transaction. 
     Assuming the item being returned passes the weight, dimensions, and color signature security checks. Fraud manager  115  may perform additional data-driven fraud analysis utilizing customer transaction and return data, item transaction and return data associated with the store, item transaction and return data associated with the retailer as a whole, and/or an existing (if available item return grade associated with the customer). The item weight values, dimensions, and color signature along with the additional data is then processed to derive a fraud score by fraud manager  115 . Item return manager  114  compares the calculated fraud score against a threshold or against a predefined range of values and item return manager  114  determines whether the item should be accepted for the self-item return transaction or whether intervention by a staff member is warranted to audit the self-item return transaction and the item being returned. 
     When the item manager  114  determines that the calculated fraud score authorizes the self-item return to proceed to completion by the customer at the SST  120 , item return manager  114  notifies sends an authorization message of instruction to agent  124  and agent  124  renders in the transaction interface instructions where the customer is to place the item or where the item is to be taken if not at the SST  120 . 
     The customer may be monitored via in-store cameras to ensure that the item was placed in a bin or a shoot associated with the SST  120  or was carried by the customer to the designated drop-off area of the store. At any time, through video analysis, it is determined that the customer attempts to exit the store with the item, a security system alert is raised, and security protocols are enforced according to store policy. 
     Item return manager  114  provides the fraud score and the self-item return transaction data to customer grade manager  116  for creating (if this is a first item that the customer has ever returned) or for updating (if this is at least a second item that the customer has returned) a customer item return grade/score. The updated grade/score is maintained with a customer profile of the customer. 
     Store rules may indicate that customers with a failing grade are not authorized to do self-item returns or in some cases no item returns at all. An okay grade may indicate that the customer is to be monitored during a self-item return transaction or that a threshold for the fraud score is to be lowered during self-item return transactions by the customer. An excellent grade may indicate that the threshold for the fraud score is high, such that if the item passes the weight, dimensions, and color signature tests/checks, it is unlikely evaluation of the additional transaction data would warrant a calculated fraud score requiring manual intervention for this type of customer. The item-return grade is a scalar value calculated by customer grade manager  116  along a predefined range of values and the item-return grade can be referenced within and processed within rules by item return manager  114  purposes of determining in real time whether a self-item return transaction can complete without staff assistance being required or whether a self-item return transaction requires staff intervention and audit. 
     Once the customer item-return grade is update or created (as discussed above), the self-item return transaction data for the self-item return transaction, the calculated fraud score (along with the item recorded weight, dimensions, and color signature), the updated customer item-return grade are stored in an audit record, which is linked to the transaction histories and the customer&#39;s transaction history. Video of the self-item return transaction may also be indexed to the audit record, such that short video clips can be accessed via links embedded in the audit record. The audit record may then be referenced if subsequent fraud was determined to have actually been present in the authorized self-time return transaction. 
     In an embodiment, the audit records determined subsequently to have been associated with fraudulent item returns can be used as input data to train a Machine-Learning Algorithm. The MLA can be trained on both known fraudulent returns and known non-fraudulent returns. The output of the trained MLA (once it has reached a desired accuracy level) may be used to determine whether a in-progress self-item return transaction is fraudulent requiring audit or whether the in-progress self-item return transaction is non-fraudulent and does not require any staff intervention. 
     In an embodiment, the MLA can be used as another data point by item return manager  114  to determine whether to halt or permit a self-item return transaction; so, manager  114  may utilize its configured thresholds, the calculated fraud score provided by fraud manager  115 , the current item-return grade provided by customer grade manager  116 , and a fraud confidence value provided as output from the MLA to determine whether a self-item return transaction should be suspended for staff audit or permitted to proceed without any staff assistance or review. 
     In an embodiment, a first MLA is trained to output the customer item-return grade and a second MLA is trained to output a fraud score, such that customer grade manager  116  is a first MLA and fraud manager  116  is a second different MLA. In an embodiment of this embodiment, a third MLA is trained to receive as input the customer grade outputted for a given item-return transaction by the first MLA and the fraud score outputted by the second MLA and produce as output a decision or a value to item return manager  114  for item return manager  114  to compare against a model value in determining whether a given item-return transaction can proceed without audit intervention or whether audit intervention is needed. In this manner, multiple trained MLAs may be chained together for purposes of making fraud determinations in real time for a self-item return transaction of the customer. 
       FIG. 1C  is a diagram of another method  100 B using the system of  FIG. 1A  for SST item return anti-fraud processing, according to an example embodiment. 
     Method  100 B provides a variety of embodiments and enhancements from that which was discussed above with method  100 A of  FIG. 1B . 
     Again, the process flow starts with the customer being identified by transaction agent  123  on terminal  120  when the customer is at the terminal  120 . A variety of mechanism permit identification of the customer and/or the transaction receipt associated with the item being returned. Scanner  126  and/or camera  125  may be used to scan a barcode or Quick Response (QR) code present on any customer-provided receipt. Transaction agent  123  can identify that the encoded information is associated with an already completed transaction, which indicates that the customer wants to perform an item-return transaction at terminal  120 . Transaction agent  123  interacts with transaction manager  113  and identifies the original transaction record and passes control to item return agent  124  and item return manager  114 . A customer identifier is associated with the transaction record, such that the customer is identified with scanning of the transaction receipt (at least it is assumed to be the customer). 
     In addition to as an alternative to original transaction receipt scanning, camera  125  may be used to identify facial features of the customer. Item return manager  114  calculates a hash value on the facial features and uses the hash value to search known hash values for those facial features in a registered customer facial features template data store for purposes of uniquely identifying the customer at terminal  120 . Note that for performance enhancement in response times, item return agent  124  may calculate the hash value and provided to item return agent  124 , such that the facial image does not have to be transmitted over a network connection to item return manager  114 . It is also to be noted that other biometric identifications can be used in addition to or as an alternative to facial recognition, such as fingerprint recognition, palm recognition, digit lengths for digits of customer&#39;s hand, and/or a voice print. When voice identification is used, microphone  129  captures an audio sample (presented on a screen as instructions for the customer to repeat or audibly requested of the customer through a speaker of terminal  110 ) and similar to what was discussed above a hash or range of hash values are compared against voice templates to identify the customer. 
     In addition to or as an alternative to the original transaction receipt scanning and biometric identification, transaction agent  123  may read card data for a card presented by the customer at the terminal and provided to item return agent  124 . The read card data comprises the customer name, which can be searched on my item return manager  114  for locating a customer profile or customer transaction records. 
     In addition to or an alternative to the original transaction receipt scanning, the biometric authentication, or the card-based authentication, item return agent  124  may uses a combination of biometric authentication along with scanning of an item bar code performed by the customer using scanner  126  and/or camera  125 . 
     After identification is made of the customer and of the original transaction record associated with the item being returned, item return manager  114  may require (according to rules associated with the item, store, and/or customer) further identification as to the customer. This may entail utilizing a store authentication or security system which is provided the data gathered during identification and submitting challenges to the customer to respond with an appropriate credential (such as password, phrase, etc.). 
     The entire time period of the customer within the store and at terminal  120  is recorded and monitored by computer-vision and any abnormal behavior or unexpected behavior causes video events to be raised and recorded. For example, the customer may during the item return transaction pick the item off a surface of the terminal  120  and place the item in his/her pocket, bag, or purse instead of placing the item where instructed. 
     So, not only is the item authenticated via visual measuring of the item/object from images and through weight determination, but the actual behaviors and actions of the customer are also monitored while at the SST  120  and while within the store associated with the SST  120 . 
     Item return manager  114  independently verifies the authenticity of the item being returned in addition to the identification of the item based on scanning and original transaction receipt information. The item is measured (height, length, width, size (dimensions) from the images, a weight is record as provided by weigh scale  127 , recent customer transaction data is obtained from the customer&#39;s transaction history to identify recent behaviors of the customer, a current item-return grade for the customer is obtained, and fraud associated with the store (local store) and/or the item as a whole is obtained from transaction histories of the store and the retailer associated with the store. Fraud manager  115  calculates a current fraud score and the current fraud score along with the customer item-return grade is processed to determine a fraud risk level. 
     Item return manager  114  uses the fraud risk level to determine if a risk threshold for the customer (specific to the customer and the local store) is exceed or not. If the risk threshold is exceeded, item return manager  114  sends the appropriate information to alert/notification manager  117 . Alert/Notification manager  117  then processes rules to alert the appropriate resources (human and automated). For example, on-site security may be initiated through an API call to the security system; a designated resource at the store may be sent a notification on his/her mobile device; item return agent  124  is instructed to render a message on the display of the SST  120  for the customer to please wait while assistance is obtained for the customer. In some cases, a remote video conference with a remote agent is established between the customer at the SST  120  and a remote staff member for assistance and auditing of the item and the item-return transaction. 
     The entire item-return transaction and all the collected data, decisions, scores, grades, video, audio, events are packaged in an audit log and updated to the retailer&#39;s data warehouse for subsequent review, mining, and reporting. 
     After a decision is made by item return manager  114  (either to permit the item-return transaction to continue and to complete with the customer operating the SST  120  or to suspend and require an in-person or remote video conference audit), the item-return transaction data, calculated fraud score, and decision made (to permit or not permit) is provided to the customer grade manager  117  where the current customer item-return grade is updated based on that information. 
     When the self-item return transaction is permitted to proceed or when it is permitted through manual override by a staff member (remote or in person at the SST  120 ), item return manager  114  process a refund for the item in a manner determined by store rules, customer profile, and/or customer selection. The refund can be cash dispensed at the SST  120 , voucher printed or provided electronically, customer account credit, prepaid gift card (loaded at the SST  120  with the refund amount), credit to the original payment card account used to originally purchase the item based on the original transaction receipt, or other manners or methods (such as digital wallet transfers of credits, cash, and/or cryptocurrency). 
     System  100  and methods  100 A and  100 B permit self-item return transactions that are secure and efficient. Fraud is dynamically calculated based on factors specific to the customer, the local store, and the item being returned. Customers have item-return grades that are continuously updated based on customer behavior and transaction history. The item-return grades can be used in combination with calculated fraud scores to provide a customer and transaction specific determination as to whether an self-item return transaction may proceed without staff assistance or as to whether staff assistance is needed (assistance may be remote (through video conference) or may be on-site at the SST  120 ). The determinations made are in real time and are data driven. In addition, a variety of image-based (e.g., dimensions, size, color signatures, etc.) and sensor-based (e.g., weight, etc.) security checks are made to independently authenticate the item being returned; such that even when a given fraud score may not exceed a given risk threshold, the self-item return transaction may require staff intervention when the item being returned is unable to be independently authenticated. These techniques substantially streamline the return transactions for retailers and provide superior security, which retailers lack today with existing manual returns. 
     In an embodiment, system  100  may be deployed with a POS terminal  120  for purposes of authenticating and ensuring staff performing a return are not committing fraud or unwittingly participating in a fraud being perpetrated by a customer. Here, the system  100  is processed for assisted item-return transactions in addition to the self-item return transactions described above. In the assisted item-return transactions, alert/notification manager  117  may dispatch an onsite manager to audit the transaction or may establish a remote video session with a manager over a network connection to audit the transaction. 
     These and other embodiments are now discussed with reference to  FIGS. 2-3 . 
       FIG. 2  is a diagram of a method  200  for SST item return anti-fraud processing, according to an example embodiment. The software module(s) that implements the method  200  is referred to as an “return transaction fraud manager.” The return transaction fraud manager is implemented as executable instructions programmed and residing within memory and/or a non-transitory computer-readable (processor-readable) storage medium and executed by one or more processors of one or more devices. The processor(s) of the device that executes the return transaction fraud manager are specifically configured and programmed to process the return transaction fraud manager. The return transaction fraud manager may have access to one or more network connections during its processing. The network connections can be wired, wireless, or a combination of wired and wireless. 
     In an embodiment, the device that executes the return transaction fraud manager is server  110 . In an embodiment, server  110  is a cloud-based server comprised of a plurality of servers logically cooperating and accessible as a single server  110 , a local-area network (LAN)-based server, or a wide-area network (WAN) server. 
     In an embodiment, multiple devices execute different portions of return transaction fraud manager. For example, some portions of the return transaction fraud manager execute on a cloud  110  while other portions of the return transaction fraud manager execute on one or more WAN or LAN servers  110 . As another example, some portions of return transaction fraud manager execute on transaction terminals  120 . In an embodiment, transaction terminals  120  may comprise, SSTs, POS terminals, or mobile devices (phones, tablets, laptops, wearable processing devices—as discussed above with the  FIGS. 1A-1C ). 
     In an embodiment, the return transaction fraud manager is all or some combination of  113 - 117 , and/or  123 - 124 . 
     At  210 , the return transaction fraud manager identify an item associated with an item return transaction (a transaction for which at least one item is being returned by a customer). 
     In an embodiment, at  211 , the return transaction fraud manager obtains the item code from a scanner or a camera associated with a transaction terminal. 
     At  220 , the return transaction fraud manager obtains an item image provided by the transaction terminal (a camera interfaced to the transaction terminal). 
     In an embodiment of  211  and  220 , at  221 , the return transaction fraud manager determines dimensions for the item from the item image (e.g., height, length, width). 
     In an embodiment of  221  and at  222 , the return transaction fraud manager calculates a color signature from colors of the item image. The color signature can include a predominate color or a score based on the colors and patterns of colors for the item. 
     At  230 , the return transaction fraud manager authenticates the item based on an item code for the item and the item image captured at the transaction terminal. 
     In an embodiment of  221  and  230 , at  231 , the return transaction fraud manager compares the item dimensions against known dimensions for a model item associated with the item code and compares the color signature against a known color signature for the model item. 
     In an embodiment of  231  and at  232 , the return transaction fraud manager determines based on the comparison whether the item is damaged or counterfeit (not the item it is purported to be by the item code). It is noted that if the item is determined to be counterfeit, then an alert is immediately sent to the transaction terminal and a staff member is dispatched to the transaction terminal. This may also be the case if the item is determined to be damaged, but the return may be permitted to the proceed if the damage is not extensive. In an embodiment, a damaged value may be used at  250  to calculate the fraud score in addition to the factors recited below. 
     At  240 , the return transaction fraud manager obtains transaction history data associated with the item, a customer associated with the item return transaction, and a store associated with the item and the transaction terminal. 
     In an embodiment, at  241 , the return transaction fraud manager identifies factors that are to be used to process the fraud score at  250  based on an item type for the item, an item category for the item within the item type, and an item brand for the item within the item category. That is, high-end or expensive items have different factors used from the transaction history data than do low-end and inexpensive items. 
     In an embodiment of  241  and at  242 , the return transaction fraud manager identifies known fraudulent transaction for the item, the store, and the customer from the transaction history data, if any. 
     At  250 , the return transaction fraud manager calculates a fraud score based on the transaction history data. The fraud score may also use an item damaged value as was discussed above at  232 . 
     In an embodiment of  242  and  250 , at  251 , the return transaction fraud manager calculates a fraud score based on the factors using the transaction history data and the known fraudulent transactions (if any were present in the transaction history data). 
     At  260 , the return transaction fraud manager obtains an item return rating for the customer. This is a scalar value calculated based on the customer&#39;s transaction history data associated with item returns. For instance, a count maintained for all item returns, which is weighted based on a time interval; store policy may indicate no more than 3 returns within a month, such that a most-recent three-month total may be 2 but the total returns for the customer within the past year were 10. In this case, the item return rating for the customer may permit a return but a weighted rating may be lowered because of the 10 total item returns. 
     At  270 , the return transaction fraud manager determines based on the fraud score and the item return rating of the customer whether the item return transaction can complete at the transaction terminal without any staff assistance or whether the item return transaction is to be suspended for assistance or an audit (assistance or the audit may be at the transaction terminal and in person or remote via video conference between a remote agent and the customer at the transaction terminal). 
     In an embodiment of  251  and  270 , at  271 , the return transaction fraud manager calculates an item return rating for the item return transaction from the fraud score and the item return rating of the customer. 
     In an embodiment of  271  and at  272 , the return transaction fraud manager compares the item return transaction rating against a threshold value or a range of values to determine whether the item return transaction can complete at the transaction terminal or whether the item return transaction is to be held in abeyance and suspended for assistance or an audit (in person or remote via a remote video session with the customer at the transaction terminal). 
     In an embodiment, at  280 , the return transaction fraud manager updates the item return rating for the customer based on the decision made at  270 . 
       FIG. 3  is a diagram of another method  300  for SST item return anti-fraud processing, according to an example embodiment. The software module(s) that implements the method  300  is referred to as an “item return security agent.” The item return security agent is implemented as executable instructions programmed and residing within memory and/or a non-transitory computer-readable (processor-readable) storage medium and executed by one or more processors of a device. The processors that execute the item return security agent are specifically configured and programmed for processing the item return security agent. The item return security agent may have access to one or more network connections during its processing. The network connections can be wired, wireless, or a combination of wired and wireless. 
     In an embodiment, the device that executes the item return security agent is transaction terminal  120 . In an embodiment, terminal  120  is an SST, a POS terminal, or a mobile device (as described above with the discussion of the  FIGS. 1A-1C . 
     In an embodiment, the item return security agent is all of or some combination of  123 - 124 . 
     The item return security agent presents another and, in some ways, enhanced processing perspective of the what was discussed above for transaction terminal  120 . 
     At  310 , item return security agent identifies an item code for an item associated with an item return transaction of a customer at at SST of a store. 
     In an embodiment, at  311 , the item return security agent automatically identifies a customer identifier associated with the customer from card data read from a card provided by the customer at the SST, a transaction receipt code (barcode or QR code) scanned at the SST and associated with an original purchase transaction of the item by the customer, or biometric attributes captured from the customer at the SST (as was discussed above with the  FIGS. 1A-1C ). 
     At  320 , the item return security agent obtains an item image of the item from a camera. The camera may be associated with (integrated into or wirelessly interfaced to) the SST or the camera may be any networked-based camera within the store associated with the SST that has a field of view of the item at the SST to capture the item image. It is noted that there may be other cameras throughout the store from which the images are processed to maintain a video record or log of the customer&#39;s visit within the store. Image analysis may track both the customer and the item as well from the images. 
     At  330 , the item return security agent calculates dimensions (height, length, width) and an item color signature from the item image. 
     At  340 , the item return security agent obtains an item weight for the item from a weight sensor (weigh scale) associated with (integrated into or wirelessly interfaced to) the SST. 
     At  350 , the item return security agent obtains model dimensions, a model color signature, and a model weight (or model weight range) for a model item that is linked or associated to the item code. 
     At  360 , the item return security agent authenticates the item based on comparisons between the item&#39;s calculated dimensions and the item&#39;s model dimensions, and the item&#39;s obtained or read weight and the item&#39;s model weight or model weight range. 
     If the item is not successfully authenticated, then an alert may be sent to the SST that forces the item return transaction to be suspended and a staff member may be dispatched to the SST for an audit and customer assistance. 
     Assuming the item is authenticated, at  370 , the item return security agent calculates a fraud score for the item return transaction based on transaction histories specific to the item, the customer, and a store associated with the SST. 
     In an embodiment, at  371 , the item return security agent determines weights and factors processed when calculating the fraud score based on an item type, an item category, and item brand, a store identifier for the store, and the customer identifier for the customer. 
     At  380 , the item return security agent calculates an item return transaction rating based on the fraud score and an item return rating associated with the customer. 
     At  390 , the item return security agent suspends the item return transaction on the SST for assistance or an audit when the item return transaction rating exceeds a threshold value or falls outside a range of values. 
     In an embodiment, at  391 , the item return security agent initiates a remote assistance video session between the customer and a staff member (remote agent) at the SST for assistance or the audit when the item return transaction rating falls outside the range by less than a predetermined amount. That is depending on the degree of deviation from what is considered to not require an audit and the item return transaction rating, a remote audit or remote assistance may be permitted. 
     In an embodiment, at  392 , the item return security agent raises an alert to a security system and/or causes a staff member to be dispatched to the SST for assistance or the audit when the item return transaction rating exceeds the threshold or falls outside the range by more than a predetermined amount. 
     It should be appreciated that where software is described in a particular form (such as a component or module) this is merely to aid understanding and is not intended to limit how software that implements those functions may be architected or structured. For example, modules are illustrated as separate modules, but may be implemented as homogenous code, as individual components, some, but not all of these modules may be combined, or the functions may be implemented in software structured in any other convenient manner. 
     Furthermore, although the software modules are illustrated as executing on one piece of hardware, the software may be distributed over multiple processors or in any other convenient manner. 
     The above description is illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of embodiments should therefore be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. 
     In the foregoing description of the embodiments, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting that the claimed embodiments have more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Description of the Embodiments, with each claim standing on its own as a separate exemplary embodiment.