Method and system for determining event occurrence based on data signals

A method of training an Artificial Intelligence (AI) model for determining occurrence of events, the method comprising:

• extracting a plurality of training data signals associated with at least one entity from a plurality of data sources, wherein the plurality of training data signals comprises structured training data signals and unstructured training data signals, and wherein the plurality of training data signals is extracted from the plurality of data sources for a pre-defined time period;
• deriving one or more training data signals from the plurality of training data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Ai based analysis technique, wherein each of the one or more training data signals comprises an associated time dimension;
• converting each of the one or more derived training data signals and the associated time dimension into a set of training vectors;
• and
• iteratively training the AI model based on the set of training vectors and the one or more derived training data signals for determining occurrence of one or more events for each of the plurality of training data signals.

A system for determining occurrence of events based on data signals, the system comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:
• receive a plurality of data signals associated with an entity from a plurality of data sources, wherein the plurality of data signals comprises structured data signals and unstructured data signals;
• derive one or more data signals from the plurality of data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Artificial Intelligence (AI) based analysis technique, wherein each of the one or more derived data signals comprises an associated time dimension;
• identify, by a trained AI model, a pattern associated with the entity within the one or more derived data signals and the plurality of data signals, wherein the pattern corresponds to occurrence of two or more data signals within an overlap in the time dimensions associated with the two or more data signals;
• and
• determine, by the trained AI model, occurrence of an event associated with the entity within a predefined time period, based on the identified pattern and the overlap in the time dimensions associated with the two or more data signals.

A system for training an Artificial Intelligence (AI) model for determining occurrence of events, the system comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:
• extract a plurality of training data signals associated with at least one entity from a plurality of data sources, wherein the plurality of training data signals comprises structured training data signals and unstructured training data signals, and wherein the plurality of training data signals is extracted from the plurality of data sources for a pre-defined time period;
• derive one or more training data signals from the plurality of training data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an AI based analysis technique, wherein each of the one or more training data signals comprises an associated time dimension;
• convert each of the one or more derived training data signals and the associated time dimension into a set of training vectors;
• and
• iteratively train the AI model based on the set of training vectors and the one or more derived training data signals for determining occurrence of one or more events for each of the plurality of training data signals.