Assessing personalized risk for a user on a journey

A system for learning and predicting personalized risk associated with a journey for a user, comprising:

• one or more computers with executable instructions that when executed cause the system to:
• analyze contextual data gathered from a plurality of data sources relating to a journey of a user;
• learn one or more risks associated with the journey according to the contextual data;
• generate one or more risk models associated with the one or more risks;
• wherein generating the one or more risk models includes detecting an anomaly including a deviation between an expected route and an actual route taken by the user, and collecting feedback information from the user regarding the anomaly for determining the one or more risks and a cause of the one or more risks, the feedback information collected by cognitively interacting with the user via a cognitive component such that the user explains the feedback information, including a reasoning for the anomaly, to the cognitive component using natural language dialog;
• and
• predict one or more potential risks associated with a subsequent journey using the one or more risk models;
• wherein the one or more potential risks are automatically presented to the user via one of the one or more computers associated with the executable instructions.

A computer program product for, by a processor, learning and predicting personalized risk associated with a journey for a user, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

• an executable portion that analyzes contextual data gathered from a plurality of data sources relating to a journey of a user;
• an executable portion that learns one or more risks associated with the journey according to the contextual data;
• an executable portion that generates one or more risk models associated with the one or more risks;
• wherein generating the one or more risk models includes detecting an anomaly including a deviation between an expected route and an actual route taken by the user, and collecting feedback information from the user regarding the anomaly for determining the one or more risks and a cause of the one or more risks, the feedback information collected by cognitively interacting with the user via a cognitive component such that the user explains the feedback information, including a reasoning for the anomaly, to the cognitive component using natural language dialog;
• and
• an executable portion that predicts one or more potential risks associated with a subsequent journey using the one or more risk models;
• wherein the one or more potential risks are automatically presented to the user via a computing device associated with the processor.