Systems and methods for objective validation of enterprise protocols involving variable data sources

A computer implemented method of analyzing a protocol of an enterprise, the method comprising:

• accessing, via a processor, client resolution data including information regarding multiple resolutions, wherein each resolution includes an identity of an agent and a relief amount, each resolution of the multiple resolutions being associated with a claim from a respective client and including an identity of an agent, and a resolution value;
• executing, by the processor, an expected value machine learning model trained using machine-learning techniques based on datasets generated using prior resolution data, the expected value machine learning model configured to receive the client resolution data as input and generate an expected resolution value for each claim associated with the multiple resolutions;
• executing, by the processor, an outlier detection model comprising one or more outlier analysis tests executed using the multiple resolutions and the expected resolution value for each claim of the multiple resolutions as input to identify abnormal resolutions, wherein executing the outlier detection model generates (i) an identifier of at least one variable of the resolutions that has a value that violates at least one rule of at least one resolution, and (ii) a categorization of the multiple resolutions based on the at least one variable;
• generating, via the processor, a stratified sample dataset from the client resolution data, the stratified sample dataset being stratified based on the categorization of the multiple resolutions and comprising expected resolution value for each claim associated with the multiple resolutions;
• generating, via the processor, a summary of the stratified sample data set, the summary comprising the categorization of the multiple resolutions and the identifier of the at least one variable that has the value that violates the at least one rule;
• providing, via the processor, the summary of the stratified sample data set in a graphical user interface (GUI) the GUI comprising a plurality of interactive elements that enable automatic reorganization of the indications of the abnormal resolutions in response to user input;
• storing, via the processor, the summary of the stratified sample dataset as additional prior resolution data;
• and
• training, via the processor, expected value machine learning model based on the additional prior resolution data.

A system, comprising:

• a display configured to present a graphical user interface (GUI);
• and
• a processor and program logic stored in memory and executed by the processor, the program logic including auditing logic configured to:
• maintain, in a plurality of electronic databases, client resolution data including information regarding multiple resolutions, the client resolution data captured via electronic communications from respective user devices associated with agents;
• access the client resolution data from the plurality of electronic databases, the client resolution data comprising information regarding multiple resolutions, each resolution being associated with a complaint or claim from a respective client and including multiple variables with corresponding values, each claim comprising respective text data;
• execute an expected value machine learning model trained using machine-learning techniques based on datasets generated using prior resolution data, the expected value machine learning model configured to receive the client resolution data as input and generate an expected resolution value for each claim associated with the multiple resolutions;
• execute a machine-learning natural language processing (NLP) model to generate a resolution dataset comprising an instance for each claim and variables for each instance, wherein the variables include the resolution value, an identity of the agent, the expected resolution value of the claim generated by the expected value machine learning model, and an identification of products provided by the enterprise and associated with the claim, the machine-learning NLP model executed using the respective text data of the claim as input;
• execute an outlier detection model trained using the resolution dataset as input to identify abnormal resolutions, wherein execution of the outlier detection model generates:
• (i) an identifier of at least one of the variables within each resolution that has a value that violates at least one rule of at least one resolution;
• and
• (ii) a summary comprising one or more categorizations of the resolutions based on at least one of the variables that violates the at least one rule of the at least one resolution;
• display, in the GUI, an outlier response comprising the summary, such that the summary comprises a plurality of interactive elements that enable automatic reorganization of the summary in response to user input;
• store the summary as additional prior resolution data;
• and
• train the expected value machine learning model based on the additional prior resolution data.