System and method for context-based life planning interactions

A system comprising:

• a computing system comprising one or more processors and a data storage system in communication with the one or more processors, wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to:
• receive information related to events from external third-party data sources and internal user profile data sources, wherein the information from the external third-party data sources includes documents that may implicate estate transfer;
• automatically process the documents to identify critical terms contained within the documents and determining a proximity relationship between the critical terms within the documents;
• automatically identify one or more potentially life-threatening events using machine learning based on the information, wherein using the machine learning includes using a trained machine learning model with closed loop feedback training;
• generate a casualty score for the one or more potentially life-threatening events based on the critical terms and the proximity relationship between the critical terms information;
• automatically correlate the one or more potentially life-threatening events with user profile data from the internal user profile data sources;
• automatically identify one or more potentially impacted individuals of the one or more potentially life-threatening events using the trained machine learning model based on the automatic correlating;
• generate an impact likelihood score for the one or more potentially impacted individuals based on the automatic correlating information;
• categorize the one or more potentially impacted individuals into an impact category based on the impact likelihood score;
• automatically generate estate transfer related action items and a context-based, personalized script for a financial institution advisor of the one or more potentially impacted individuals based on one or more of the impact likelihood score, the casualty score or the impact category;
• automatically perform proactive measures related to estate planning to protect the one or more potentially impacted individuals, based on one or more of the impact likelihood score, the casualty score or the impact category;
• and
• automatically update the trained machine learning model based on verification feedback from the one or more potentially impacted individuals to improve future predictions.

A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that, when executed by computers, cause the computers to perform operations of:

• receiving information related to events from external third-party data sources and internal user profile data sources, wherein the information from the external third-party data sources includes documents that may implicate estate transfer;
• automatically processing the documents to identify critical terms contained within the documents and determining a proximity relationship between the critical terms within the documents;
• automatically identifying one or more potentially life-threatening events using machine learning based on the information, wherein using the machine learning includes using a trained machine learning model with closed loop feedback training;
• generating a casualty score for the one or more potentially life-threatening events based on the critical terms and the proximity relationship between the critical terms information;
• automatically correlating the one or more potentially life-threatening events with user profile data from the internal user profile data sources;
• automatically identifying one or more potentially impacted individuals of the one or more potentially life-threatening events using the trained machine learning model based on the automatic correlating;
• generating an impact likelihood score for the one or more potentially impacted individuals based on the automatic correlating information;
• categorizing the one or more potentially impacted individuals into an impact category based on the impact likelihood score;
• automatically generating estate transfer related action items and a context-based, personalized script for a financial institution advisor of the one or more potentially impacted individuals based on one or more of the impact likelihood score, the casualty score or the impact category;
• automatically performing proactive measures related to estate planning to protect the one or more potentially impacted individuals, based on one or more of the impact likelihood score, the casualty score or the impact category;
• and
• automatically updating the trained machine learning model based on verification feedback from the one or more potentially impacted individuals to improve future predictions.