Using classified text, deep learning algorithms and blockchain to identify risk in low-frequency, high value situations, and provide early warning

A method of using classified text augmented with a blockchain and deep learning to identify risk comprising:

• obtaining one or more training datasets comprising of textual data corresponding to one or more adverse risk situations;
• aggregating a plurality training data into a blockchain for one of said one or more training datasets, wherein each one of said plurality of training data consists of a specific adverse risk situation of said one or more adverse risk situations provided by one of a plurality of enterprises, wherein said training data comprises a number string;
• training one or more deep learning algorithms using said one or more training datasets;
• extracting one or more internal electronic communications of an enterprise;
• and
• applying said one or more deep learning algorithms to said one or more internal electronic communications to identify any one of said one or more adverse risk situations.

A computer program product for using classified text augmented with a blockchain and deep learning algorithms to identify risk, the computer program product comprising non-transitory computer-readable media encoded with instructions for execution by a processor to:

• obtain one or more training datasets comprising of textual data corresponding to one or more adverse risk situations;
• aggregate a plurality training data into a blockchain for one of said one or more training datasets, wherein each one of said plurality of training data consists of a specific adverse risk situation of said one or more adverse risk situations provided by one of a plurality of enterprises, wherein said training data comprises a number string;
• train one or more deep learning algorithms using said one or more training datasets;
• extract one or more internal electronic communications of an enterprise;
• and
• apply said one or more deep learning algorithms to said one or more internal electronic communications to identify any one of said one or more adverse risk situations.