ZenPayroll, Inc.

Access to sensitive information in a database can be restricted to improve security and enable efficient auditing. A security engine receives a request from a requesting entity to access data in the database and determines that the requested data includes sensitive information. In response to the requesting entity being authorized to access the data, the security engine retrieves the requested data from the database and modifies the retrieved data by modifying metadata of the retrieved data to include a tag indicating that the retrieved data includes sensitive information. The security engine provides the modified data to the requesting entity and modifies a data access log to identify each attempted access to the modified data. When sensitive data is requested, an interface can include an obscuring element, requiring a user to manually select the element to view the data, enabling the logging of the explicit access request by the user.

An image processing system accesses an image of a completed form document. The image of the form document includes one or more features, such as form text, at particular locations within the image. The image processing system accesses a template of the form document and computes a rotation and zoom of the image of the form document relative to the template of the form document based on the locations of the features within the image of the form document relative to the locations of the corresponding features within the template of the form document. The image processing system performs a rotation operation and a zoom operation on the image of the form document, and extracts data entered into fields of the modified image of the form document. The extracted data can be then accessed or stored for subsequent use.

A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Access to sensitive information in a database can be restricted to improve security and enable efficient auditing. A security engine receives a request from a requesting entity to access data in the database and determines that the requested data includes sensitive information. In response to the requesting entity being authorized to access the data, the security engine retrieves the requested data from the database and modifies the retrieved data by modifying metadata of the retrieved data to include a tag indicating that the retrieved data includes sensitive information. The security engine provides the modified data to the requesting entity and modifies a data access log to identify each attempted access to the modified data. When sensitive data is requested, an interface can include an obscuring element, requiring a user to manually select the element to view the data, enabling the logging of the explicit access request by the user.

A central database system allows entities to easily manage human resources functions. An entity requests that the central database system executes an employer function on its behalf, and the central database system determines a probability of the entity defaulting before the entity can finalize the employer function with the central database system. The central database system can train and apply a machine-learned model to dynamically determine the default probability for the entity. Based on the default probability, the central database system determines a risk tolerance associated with the employer function and determines whether to process or challenge the employer function based on the risk tolerance and the default probability.

A central database system receives information associated with an employee from an employer. Using this information, the central database system can provision one or more user accounts for the employee, for instance via an API of an account provider. The central database system can use a machine learned model to identify fields of the API and to translate the information associated with the employee based on information requirements associated with the API. When a characteristic of the employee, such as the employee's title, subsequently changes within the central database system, one or more features associated with the user account can be automatically updated in response to the change.

A central database system provides predictive web navigation using machine learning and clustering to guide a user to a web page. After tracking a number of web pages viewed by various users on one or more web domains and the orders in which these web pages are viewed, the central database system can train a model to predict which web page a user is likely to view next. If the user is lost while navigating, the central database system may guide the user to the predicted web page. In one example of guidance, the central database system presents a web element with a hyperlink to the predicted web page. For example, the central database system can modify a web page that the lost user is presently viewing to include an iframe with a recommendation to travel to a different web page.

A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.

A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.

A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.