In an illustrative embodiment, systems and methods for cyber vulnerability assessment include obtaining assessment data including information pertaining to domains of cyber security vulnerability of an enterprise and, for each security domain, a respective domain-level vulnerability score, identifying risk(s) relevant to the enterprise based on domain-level vulnerability score(s), identifying recommended products or services for mitigating each of the risks, and preparing a graphical user interface for selecting a portion of the recommended products or services. A user may select one or more products or services through the user interface for purchase and/or deployment planning. The domain-level vulnerability scores may be compared to peer vulnerabilities scores, target vulnerability scores, or prospective vulnerability scores based upon application of certain recommended products or services.

Some embodiments of the present disclosure disclose methods and systems for assessing the data risk management capabilities of data processors that receive second-party data as part of an engagement to provide support services. In some embodiments, the transfer of the second-party data to the data processors can be monitored to identify file transfers including unauthorized personally identifiable information (PII) attributes. In some embodiments, the database of the data processor may be scanned to locate any residual second-party data that should be removed after the data processor's engagement to provide the support services have expired.

Some embodiments of the present disclosure disclose methods and systems for assessing the data risk management capabilities of data processors that receive second-party data as part of an engagement to provide support services. In some embodiments, the transfer of the second-party data to the data processors can be monitored to identify file transfers including unauthorized personally identifiable information (PII) attributes. In some embodiments, the database of the data processor may be scanned to locate any residual second-party data that should be removed after the data processor's engagement to provide the support services have expired.

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of keystone activity based suggestions. The innovation detects a keystone activity of a user. The keystone activity is a planned event for the user such as recently purchased tickets to a specific show. Customer data of a financial institution is accessed where the customer data includes data of customers of the financial institution. A set of similar customers to the user is determined. Transaction data of the set of similar customers is determined and analyzed for likelihood of the user wanting to attend a secondary activity that is similar the set of similar customers. The secondary activity can be automatically scheduled for the user based on the keystone activity and the transaction data.

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of keystone activity based suggestions. The innovation detects a keystone activity of a user. The keystone activity is a planned event for the user such as recently purchased tickets to a specific show. Customer data of a financial institution is accessed where the customer data includes data of customers of the financial institution. A set of similar customers to the user is determined. Transaction data of the set of similar customers is determined and analyzed for likelihood of the user wanting to attend a secondary activity that is similar the set of similar customers. The secondary activity can be automatically scheduled for the user based on the keystone activity and the transaction data.

A method and system for managing financial wellbeing of customers is disclosed. In some embodiments, the method includes receiving a set of data instances associated with a plurality of customers from a plurality of data sources. The method further includes segmenting the plurality of customers based on an analysis of the set of data instances associated with each of the plurality of customers. The method further includes identifying at least one customer from the plurality of customers. The method further includes analyzing the at least one data instance associated with the at least one customer to identify one or more anomalies in financial pattern of the at least one customer; determining a root cause for the one or more anomalies identified in the financial pattern of the at least one customer; and providing at least one recommendation to the at least one customer based on the determined root cause.

A method and system for managing financial wellbeing of customers is disclosed. In some embodiments, the method includes receiving a set of data instances associated with a plurality of customers from a plurality of data sources. The method further includes segmenting the plurality of customers based on an analysis of the set of data instances associated with each of the plurality of customers. The method further includes identifying at least one customer from the plurality of customers. The method further includes analyzing the at least one data instance associated with the at least one customer to identify one or more anomalies in financial pattern of the at least one customer; determining a root cause for the one or more anomalies identified in the financial pattern of the at least one customer; and providing at least one recommendation to the at least one customer based on the determined root cause.

A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.

A method for playing videos can include: playing a first video; displaying a video viewing portal on a playback interface of the first video, wherein the video viewing portal is configured for viewing of videos related to the first video; switching, in response to no trigger operation on the video viewing portal being detected within a first reference duration, the video viewing portal to a float window corresponding to a second video related to the first video; and playing the second video in response to a trigger operation on the float window.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize collaborative filtering and a reinforcement learning model having an actor-critic framework to provide digital content items across client devices. In particular, in one or more embodiments, the disclosed systems monitor interactions of a client device with one or more digital content items to generate item embeddings (e.g., utilizing a collaborative filtering model). The disclosed systems further utilize a reinforcement learning model to generate a recommendation (e.g., determine one or more additional digital content items to provide to the client device) based on the user interactions. In some implementations, the disclosed systems utilize the reinforcement learning model to analyze every negative and positive interaction observed when generating the recommendation. Further, the disclosed systems utilize the reinforcement learning model to analyze item embeddings, which encode the relationships among the digital content items, when generating the recommendation.