Dynamic time-dependent asynchronous analysis

A system for dynamic time-dependent asynchronous analysis, the system comprising:

• a memory;
• and
• a processor coupled to the memory, wherein the processor is configured to:
• transmit a first query and a second query for information about a client associated with a client account;
• generate, using at least one trained machine learning model and based on prior data store interactions, respective estimated times to receive a first dataset responsive to a first query and a second dataset responsive to a second query;
• determine, using the at least one trained machine learning model and based on prior confidence score determinations, respective estimated importance levels of the first dataset and the second dataset to determining a confidence score for the client;
• generate a preliminary confidence score for the client based on the first dataset while waiting for the second dataset to be received;
• delay temporarily until the second dataset is received to pause generation of the confidence score for the client based on the estimated importance level of the second dataset reaching at least an importance threshold and based on the estimated time to receive the second dataset being within an amount of time for responding to a request;
• update the preliminary confidence score by an update amount using the second dataset to generate the confidence score for the client;
• determine an eligibility of the client account for a modification based on a comparison between the confidence score and a confidence threshold;
• and
• train the at least one trained machine learning model further using training data to update the at least one trained machine learning model for at least one further client analysis, wherein the training data includes the update amount and respective times to receive the first dataset and the second dataset.

A non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method of dynamic time-dependent asynchronous analysis, the method comprising:

• transmitting a first query and a second query for information about a client associated with a client account;
• generating, using at least one trained machine learning model and based on prior data store interactions, respective estimated times to receive a first dataset responsive to a first query and a second dataset responsive to a second query;
• determining, using the at least one trained machine learning model and based on prior confidence score determinations, respective estimated importance levels of the first dataset and the second dataset to determining a confidence score for the client;
• generating a preliminary confidence score for the client based on the first dataset while waiting for the second dataset to be received;
• delaying temporarily until the second dataset is received to pause generation of the confidence score for the client based on the estimated importance level of the second dataset reaching at least an importance threshold and based on the estimated time to receive the second dataset being within an amount of time for responding to a request;
• updating the preliminary confidence score by an update amount using the second dataset to generate the confidence score for the client;
• determining an eligibility of the client account for a modification based on a comparison between the confidence score and a confidence threshold;
• and
• training the at least one trained machine learning model further using training data to update the at least one trained machine learning model for at least one further client analysis, wherein the training data includes the update amount and respective times to receive the first dataset and the second dataset.