Abstract: A fraud prevention system that includes a client server and a fraud prevention server. The fraud prevention server includes an electronic processor and a memory. The memory includes an online application origination (OAO) service. When executing the OAO service, the electronic processor is configured to determine a fraud score of an online application based on an online application origination (OAO) model that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Abstract: This disclosure describes an intelligent fraud detections system that, as part of an inter-network facilitation system, can intelligently generate fraud predictions for digital claims to improve accuracy and efficiency of network-transaction security systems. For example, the disclosed systems can utilize a fraud detection machine-learning model to generate a fraud prediction for a digital claim disputing a digital transaction. Indeed, the disclosed systems can identify features of a digital claim and, based on those features, utilize a fraud detection machine-learning model to generate a fraud prediction for the digital claim. Additionally, based on the fraud prediction, the disclosed systems can perform authorizing, remedial, or other actions with regard to the digital claim.

Abstract: A fraud prevention system that includes a fraud prevention server including an electronic processor and a memory. The memory includes an online application origination (OAO) service. When executing the OAO service, the electronic processor is configured to determine whether the OAO service is enabled and whether a website configuration includes a list of multi-page placements for an online application, determine that input data needs to be stored in the memory and combined into multi-page input data, determine a fraud risk score of the online application based on the multi-page input data and an online application origination (OAO) model that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device, and control a client server to approve, hold, or deny the online application based on the fraud risk score that is determined.

Abstract: User identification with blended response from dual-layer identification service. In one embodiment, a server comprising an electronic processor configured to detect an access request by a user of a user interface device, retrieve a plurality of input profile records from an input profile record repository, perform an identification of the user with one or more passive biometrics models and the plurality of input profile records that are retrieved, generate an identification response and an additional identification request based on an outcome of the identification of the user, control the communication interface to transmit the additional identification request to the second server via the network, receive a second identification response from the second server, and generate a blended response by modifying one or more characteristics of the identification response with the second identification response, the blended response indicating the identification of the user.

Abstract: A system for generating a fraud risk score for a third party provider (TPP) transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a frequency-recency-monetary value feature, a reputation feature, and a rule feature for a TPP transaction, using the frequency-recency-monetary value feature, the reputation feature, and the rule feature as input for a machine learning model, execute the machine learning model to generate a blended score, and, when the blended score is above a second predetermined threshold, determine that the TPP transaction is fraudulent.

Abstract: This disclosure describes an intelligent fraud detections system that, as part of an inter-network facilitation system, can intelligently generate fraud predictions for digital claims to improve accuracy and efficiency of network-transaction security systems. For example, the disclosed systems can utilize a fraud detection machine-learning model to generate a fraud prediction for a digital claim disputing a digital transaction. Indeed, the disclosed systems can identify features of a digital claim and, based on those features, utilize a fraud detection machine-learning model to generate a fraud prediction for the digital claim. Additionally, based on the fraud prediction, the disclosed systems can perform authorizing, remedial, or other actions with regard to the digital claim.

Abstract: User identification with blended response from dual-layer identification service. In one embodiment, a server comprising an electronic processor configured to detect an access request by a user of a user interface device, retrieve a plurality of input profile records from an input profile record repository, perform an identification of the user with one or more passive biometrics models and the plurality of input profile records that are retrieved, generate an identification response and an additional identification request based on an outcome of the identification of the user, control the communication interface to transmit the additional identification request to the second server via the network, receive a second identification response from the second server, and generate a blended response by modifying one or more characteristics of the identification response with the second identification response, the blended response indicating the identification of the user.

Abstract: A system for generating a fraud risk score for a third party provider (TPP) transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a frequency-recency-monetary value feature, a reputation feature, and a rule feature for a TPP transaction, using the frequency-recency-monetary value feature, the reputation feature, and the rule feature as input for a machine learning model, execute the machine learning model to generate a blended score, and, when the blended score is above a second predetermined threshold, determine that the TPP transaction is fraudulent.

Abstract: A fraud prevention server and method. The fraud prevention server includes an electronic processor and a memory. The memory includes an online application origination (OAO) service. The electronic processor is configured to perform feature encoding on one or more categorical variables in a first matrix to generate one or more feature encoded categorical variables, generate a second matrix including the one or more feature encoded categorical variables, generate a feature encoded OAO model by training an OAO model with the second matrix, receive information regarding a submission of an online application on a device, determine a fraud score of the online application based on the information that is received and the feature encoded OAO model, the feature encoded OAO model being more precise than the OAO model, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Abstract: User identification with blended response from dual-layer identification service. In one embodiment, a server comprising an electronic processor configured to detect an access request by a user of a user interface device, retrieve a plurality of input profile records from an input profile record repository, perform an identification of the user with one or more passive biometrics models and the plurality of input profile records that are retrieved, generate an identification response and an additional identification request based on an outcome of the identification of the user, control the communication interface to transmit the additional identification request to the second server via the network, receive a second identification response from the second server, and generate a blended response by modifying one or more characteristics of the identification response with the second identification response, the blended response indicating the identification of the user.

Abstract: A system for utilizing behavioral features to authenticate a user entering login credentials. The system includes an electronic processor configured to receive a request to access a user account and compare behavioral features included in the request to behavioral features included in a user behavior profile associated with the user account. The electronic processor is also configured to, based on the comparison, generate one or more scores. The electronic processor is further configured to, for each of the one or more scores, compare the score to a predetermined threshold and, based on the comparison of the score to the predetermined threshold, adjust a match value. The electronic processor is also configured to compare the match value to one or more predetermined thresholds to determine whether the behavioral features included in the request to access the user account authenticates the user, does not authenticate the user, or is inconclusive.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating, utilizing a dispute-evaluator machine-learning model, a likelihood of approval score for a submitted dispute request and granting or denying provisional credit for the dispute request based on the likelihood of approval score. In particular, in one or more embodiments, the disclosed system receives a dispute request with information associated with disputed transactions within the dispute request. Based on the generated likelihood of approval score satisfying a predetermined threshold, the disclosed system can grant or deny to the user account a provisional credit.

Abstract: A system for determining a fraud risk score associated with a transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a plurality of rules based on a plurality of transactions over time and extract one or more features of the transaction. The electronic processor is also configured to select, based on the plurality of rules, a plurality of fraud risk features Each non-categorical fraud risk feature selected is associated with a fraud risk feature value and each categorical fraud risk feature selected is associated with a categorical variable value. The electronic processor is configured to determine, for each categorical fraud risk feature, a fraud risk feature value. The electronic processor is also configured to determine the fraud risk score based on the one or more of the transformed fraud risk feature values.

Abstract: This disclosure describes an intelligent fraud detections system that, as part of an inter-network facilitation system, can intelligently generate fraud predictions for digital claims to improve accuracy and efficiency of network-transaction security systems. For example, the disclosed systems can utilize a fraud detection machine-learning model to generate a fraud prediction for a digital claim disputing a digital transaction. Indeed, the disclosed systems can identify features of a digital claim and, based on those features, utilize a fraud detection machine-learning model to generate a fraud prediction for the digital claim. Additionally, based on the fraud prediction, the disclosed systems can perform authorizing, remedial, or other actions with regard to the digital claim.

Abstract: A universal device identifier (UDID) service with adjusted attribute distances. In one embodiment, a server includes an electronic processor and a memory storing the UDID service. The electronic processor is configured to receive an identification request regarding a currently-observed device having a first set of device attributes, retrieve any previously-observed devices, determine whether one or more devices are found from retrieving the any of the previously-observed devices, determine all changed device attributes for each device of the one or more devices that are found relative to the first set of device attributes, generate an adjusted attribute distance corresponding to the all changed device attributes for the each device, retrieve a universal device identifier (UDID) of a closest device having a lowest adjusted attribute distance among all adjusted attribute distances that are generated, and assign the UDID of the closest device to the currently-observed device.

Abstract: A fraud prevention system that includes a fraud prevention server including an electronic processor and a memory. The memory includes a feature drift hardened online application origination (OAO) service. When executing the feature drift hardened OAO service, the electronic processor is configured to detect, with respect to a first OAO model, feature drift in a dataset of an online application that exceeds a predefined threshold, generate one or more feature drift hardened OAO models that mitigate the feature drift, determine a fraud score of the online application based on the one or more feature drift hardened OAO models that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device and mitigates the feature drift, and control a client server to approve, hold, or deny the online application based on the fraud score.

Abstract: An account recommendation system that includes a server. The server is operable to identify a user account based on a server-side persistent device identification. The server includes a processor and a memory. The server is configured to receive a request including one or more attributes related to a client device, identify the server-side persistent device identification (“PDI”) record for the client device based on the one or more attributes of the client device, identify one or more candidate accounts based on the server-side persistent device identification, determine confidence scores for each of the one or more candidate accounts, generate a recommendation signal for the user account associated with the client device based on the confidence scores, and transmit the recommendation signal to a merchant server.

Abstract: A system for generating a fraud risk score for a third party provider (TPP) transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a frequency-recency-monetary value feature, a reputation feature, and a rule feature for a TPP transaction, using the frequency-recency-monetary value feature, the reputation feature, and the rule feature as input for a machine learning model, execute the machine learning model to generate a blended score, and, when the blended score is above a second predetermined threshold, determine that the TPP transaction is fraudulent.

Abstract: An account recommendation system that includes a server. The server is operable to identify a user account based on a server-side persistent device identification. The server includes a processor and a memory. The server is configured to receive a request including one or more attributes related to a client device, identify the server-side persistent device identification (“PDI”) record for the client device based on the one or more attributes of the client device, identify one or more candidate accounts based on the server-side persistent device identification, determine confidence scores for each of the one or more candidate accounts, generate a recommendation signal for the user account associated with the client device based on the confidence scores, and transmit the recommendation signal to a merchant server.

Abstract: A universal device identifier (UDID) service with adjusted attribute distances. In one embodiment, a server includes an electronic processor and a memory storing the UDID service. The electronic processor is configured to receive an identification request regarding a currently-observed device having a first set of device attributes, retrieve any previously-observed devices, determine whether one or more devices are found from retrieving the any of the previously-observed devices, determine all changed device attributes for each device of the one or more devices that are found relative to the first set of device attributes, generate an adjusted attribute distance corresponding to the all changed device attributes for the each device, retrieve a universal device identifier (UDID) of a closest device having a lowest adjusted attribute distance among all adjusted attribute distances that are generated, and assign the UDID of the closest device to the currently-observed device.