Large language model-assisted fraudulent call detection

A computer-implemented method of detecting fraudulent intent in a telephonic voice call on a user device, comprising:

• providing, to a large language model (LLM), a transcript of a portion of an ongoing call between a user and a second party;
• receiving, from the LLM, respective parameter scores for a plurality of indicia of fraud associated with the transcript of the call;
• computing a weighted fraud score for the ongoing call via a device-local detector of the user device;
• and
• if the weighted fraud score exceeds a threshold, warning the user.

One or more tangible, non-transitory computer-readable storage media having stored thereon executable instructions to detect fraudulent intent in a telephonic voice call on a user device, the instructions to instruct a processor to:

• provide, to a large language model (LLM), a transcript of a portion of an ongoing call between a user and a second party;
• receive, from the LLM, respective parameter scores for a plurality of indicia of fraud associated with the transcript of the call;
• compute a weighted fraud score for the ongoing call via a device-local detector of the user device;
• and
• if the weighted fraud score exceeds a threshold, warn the user.

A computing apparatus configured to detect fraudulent intent in a telephonic voice call, comprising:

• a hardware platform comprising a processor circuit and a memory;
• and
• instructions encoded within the memory to instruct the processor circuit to:
• provide, to a large language model (LLM), a transcript of a portion of an ongoing call between a user and a second party;
• receive, from the LLM, respective parameter scores for a plurality of indicia of fraud associated with the transcript of the call;
• compute a weighted fraud score for the ongoing call via a device-local detector;
• and
• if the weighted fraud score exceeds a threshold, warn the user.