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: A fraud prevention system that includes a server. The server is operable to receive a first attribute of a client device from the client device and associated with a first transaction, receive a second attribute of the client device from the client device and associated with the first transaction, receive a third attribute related to the client device and associated with the first transaction, and generate a persistent device identification (“PDI”) record including the first attribute, the second attribute, and the third attribute, store the PDI record in a memory, receive the third attribute related to the client device and associated with a second transaction, and identify the client device using the PDI record based on the third attribute without receiving, in association with the second transaction, the first attribute of the client device and the second attribute of the client device.

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 method and apparatus for processing service requests. A computer system creates a preliminary compendium comprising building blocks. A building block is a word entity extracted from a text source in a plurality of text sources that are related to a domain of interest. At least a portion of the text sources comprises past service requests. The computer system filters the building blocks in the preliminary compendium based on relevance with respect to the domain of interest to create similarity indicators that form a refined compendium. The computer system analyzes text within a new service request using the past service requests and the refined compendium to generate a ranking of past service requests that ranks the past service requests based on similarity to the new service request. The ranking of past service requests enables the computer system to identify a set of possible resolutions for the new service request.

Abstract: A method and apparatus for processing service requests. A computer system creates a preliminary compendium comprising building blocks. A building block is a word entity extracted from a text source in a plurality of text sources that are related to a domain of interest. At least a portion of the text sources comprises past service requests. The computer system filters the building blocks in the preliminary compendium based on relevance with respect to the domain of interest to create similarity indicators that form a refined compendium. The computer system analyzes text within a new service request using the past service requests and the refined compendium to generate a ranking of past service requests that ranks the past service requests based on similarity to the new service request. The ranking of past service requests enables the computer system to identify a set of possible resolutions for the new service request.