Method and system for artificial intelligence infused call audit and compilance

A system for call audit and compliance, the system comprising:

• a memory storing instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• source a plurality of call transcripts of an organization, recorded for a plurality of calls handled by a plurality of agents with a plurality of customers for a plurality of products;
• sample the plurality of call transcripts to obtain a sample set by performing uniform distribution across the plurality of agents, across time windows recorded for the set of calls within a day, and across length of conversations of the set of calls, wherein each call transcript within the sample set is validated via a first pretrained ML model executed by the one or more hardware processors, to eliminate junk calls;
• split the sample set in accordance with a predefined batch size to obtain a plurality of batches;
• and
• perform a first level processing of on a subset of call transcripts in each batch from among the plurality of batches to generate a call audit and compliance report for each of the plurality of agents, the first level processing comprising:
• clustering each batch in accordance with a similarity of a conversation among the subset of transcripts to obtain a plurality of clusters per batch, wherein the similarity of the conversation is identified in context of a category or subcategory of a product discussed in the conversation from among the plurality of products, an issue associated with the product in the conversation and a customer among the plurality of customers having the conversation;
• validating via a second pretrained ML model executed by the one or more hardware processors, each of the plurality of agents within each cluster by scoring each call transcript among the subset of call transcripts against a plurality of audit and compliance parameters to classify each of the plurality of agent into a category among a plurality of categories comprising compliant, partially compliant, and non-compliant;
• and
• generating a call audit and compliance report for each of the plurality of agents in accordance with a classified category and specifying one or more of the plurality of audit and compliance parameters scoring below a predefined scoring threshold.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• sourcing a plurality of call transcripts of an organization, recorded for a plurality of calls handled by a plurality of agents with a plurality of customers for a plurality of products;
• sampling the plurality of call transcripts to obtain a sample set by performing uniform distribution across the plurality of agents, across time windows recorded for the set of calls within a day, and across length of conversations of the set of calls, wherein each call transcript within the sample set is validated via a first pretrained ML model executed by the one or more hardware processors, to eliminate junk calls;
• splitting the sample set in accordance with a predefined batch size to obtain a plurality of batches;
• and
• performing a first level processing of on a subset of call transcripts in each batch from among the plurality of batches to generate a call audit and compliance report for each of the plurality of agents, the first level processing comprising:
• clustering, each batch in accordance with a similarity of a conversation among the subset of transcripts to obtain a plurality of clusters per batch, wherein the similarity of conversation is identified in context of a category or subcategory of a product discussed in the conversation from among the plurality of products, an issue associated with the product in the conversation and a customer among the plurality of customers having the conversation;
• validating, via a second pretrained ML model executed by the one or more hardware processors, each of the plurality of agents within each cluster by scoring each call transcript among the subset of call transcripts against a plurality of audit and compliance parameters to classify each of the plurality of agent into a category among a plurality of categories comprising compliant, partially compliant, and non-compliant;
• and
• generating, a call audit and compliance report for each of the plurality of agents in accordance with a classified category and specifying one or more of the plurality of audit and compliance parameters scoring below a predefined scoring threshold.