Various systems and methods for a collaborative phone reputation system are described herein. A system for implementing a collaborative phone reputation system includes a compute device comprising: a call handling module to detect, at the compute device, an incoming call for a user of the compute device; a scoring module to determine a local probabilistic score that the incoming call is desirable for the user; and an execution module to perform an action at the compute device based on the local probabilistic score.

A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.

The present disclosure relates to systems and methods for filtering electronic activities. The method includes identifying an electronic activity. The method includes parsing the electronic activity to identify one or more electronic accounts in the electronic activity. The method includes determining, responsive to parsing the electronic activity, that the electronic activity is associated with an electronic account of the one or more electronic accounts. The method includes selecting, based on the electronic account, one or more filtering policies associated with the data source provider to apply to the electronic activity. The method includes determining, by applying the selected one or more filtering policies to the electronic activity, to restrict the electronic activity from further processing based on the electronic activity satisfying at least one of the selected one or more filtering policies. The method includes restricting, the electronic activity from further processing.

One or more embodiments related to a method that includes querying a data store for current interaction data between call center personnel and customers. The call center personnel are grouped into call center groups. The method further includes determining, for at least some call center groups, a current interaction metric specific to the call center group. The current interaction method is provided for each of the at least some call center groups.

Embodiments described herein provide for a fraud detection engine for detecting various types of fraud at a call center and a fraud importance engine for tailoring the fraud detection operations to relative importance of fraud events. Fraud importance engine determines which fraud events are comparative more important than others. The fraud detection engine comprises machine-learning models that consume contact data and fraud importance information for various anti-fraud processes. The fraud importance engine calculates importance scores for fraud events based on user-customized attributes, such as fraud-type or fraud activity. The fraud importance scores are used in various processes, such as model training, model selection, and selecting weights or hyper-parameters for the ML models, among others. The fraud detection engine uses the importance scores to prioritize fraud alerts for review. The fraud importance engine receives detection feedback, which contacts involved false negatives, where fraud events were undetected but should have been detected.

Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of contacts available for connection to an agent; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the agent and the plurality of contacts; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic network flow model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

Various embodiments concern obtaining communication data and generating activity logs. More specifically, communication data such as contact information and call time associated with communications are obtained. The obtained data is then used to generate a report including the time, duration, and project or client associated with communication. Thus, an activity log is automatically generated using the communication data.

Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

The location of voice prompts within a call waveform is usually conducted by match filtering a snippet of the prompt (approx. 800 ms) to the call waveform. In an enhanced process that can accommodate transmission errors when detecting voice prompts on lower quality transmission lines, a snippet of a voice prompt may be divided into sniplets, typically 100 ms long. The sniplets can be individually detected. If a sufficient number of sniplets are detected within allowed time tolerances, then this subset of detected sniplets can indicate the presence of the snippet, and thus the associated voice prompt.

Methods, systems, and storage media for generating new record objects based on electronic activities are disclosed. Example implementations may: access a plurality of electronic activities; access a plurality of record objects; parse an electronic activity of the plurality of electronic activities; determine, responsive to parsing the electronic activity, that the electronic activity is to be matched to one or more record objects; determine for each candidate record object that a match score indicating a likelihood of electronic activity being matched to the candidate record object is below a threshold; determine an object type of a new record object to generate based on one or more participants of the electronic activity; generate the new record object of the determined type; and store in a data structure an association between the new record object and the electronic activity.