Actioning classification of a telecommunications network call

A computer-implemented method for classifying a fraud level of a voice call that is in progress in a telecommunications network, comprising:

• accessing the voice call;
• for a specified segment of the voice call, converting a sample of the voice call to text, the text comprising a transcript of at least a portion of the voice call;
• computing an embedding vector of the transcript;
• searching a database of embedding vectors for a first embedding vector with a defined degree of similarity to the embedding vector of the transcript, each embedding vector representing an example text associated with a known classification of a fraud level of the example text;
• constructing a first prompt, the first prompt comprising:
• an instruction directed to a conversational large-language model, the instruction instructing the conversational large-language model to classify a fraud level of the call represented by the transcript using a first example;
• the transcript;
• the first example, the first example comprising:
• an example text represented by the first embedding vector;
• and
• the known classification associated with the example text represented by the first embedding vector;
• prompting the conversational large-language model with the first prompt;
• receiving a response to the first prompt from the conversational large-language model, the response comprising a classification of a fraud level of the call;
• and
• in response to the classification of the fraud level of the call indicating that the call is fraudulent above a defined level, initiating an action at the telecommunications network, the action comprising at least one of:
• sending a message to a user associated with the call, terminating the call, and interjecting an audio message into the call.

A computer-implemented method for classifying a call in a telecommunications network, comprising:

• accessing a transcript of the call;
• computing an embedding vector of the transcript;
• searching a database of embedding vectors for a first embedding vector with a defined degree of similarity to the embedding vector of the transcript, each embedding vector representing an example text associated with a known classification;
• constructing a first prompt, the first prompt comprising:
• an instruction directed to a large-language model, the instruction instructing the large-language model to classify the call represented by the transcript using a first example;
• the transcript;
• the first example, the first example being:
• an example text represented by the first embedding vector;
• and
• the known classification associated with the example text represented by the first embedding vector;
• prompting the large-language model with the first prompt;
• receiving a response to the first prompt from the large-language model, the response comprising a classification of the call;
• and
• in response to the classification meeting a criterion, initiating an action at the telecommunications network.