Method and system for identifying frauds in unemployment insurance claims using hybrid weighted decision model

A system, for identifying frauds in unemployment insurance claims further comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• extract a set of claim information from a request of a claimant to assess risk affecting eligibility of the claimant to receive benefits, wherein the set of claim information comprises one or more demographics data, skill and employment data, and an industry data, and one or more claims;
• determine a set of key fraud indicators by performing a correlation analysis on the set of claim information using a correlation score;
• predict a classification probability for the set of key fraud indicators for each claim using a set of top features associated with a prescient artificial intelligence (AI) model;
• identify using a LLM one or more outliers from each claim that are excluded from the set of claim information;
• generate a weighted probability for each claim using a hybrid weighted decision model;
• and
• identify one or more frauds associated with the unemployment insurance claim of the claimant based on the weighted probability of each claim, a network diagram generated using the weighted probability for each claim, a set of filtered claims, and a set of business rules.

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:

• extracting a set of claim information from a request of a claimant to assess risk affecting eligibility of the claimant to receive benefits, wherein the set of claim information comprises one or more demographics data, skill and employment data, and an industry data, and one or more claims;
• determining a set of key fraud indicators by performing a correlation analysis on the set of claim information using a correlation score;
• predicting a classification probability for the set of key fraud indicators for each claim using a set of top features associated with a prescient artificial intelligence (AI) model;
• identifying using a LLM one or more outliers from each claim that are excluded from the set of claim information;
• generating a weighted probability for each claim using a hybrid weighted decision model;
• and
• identifying one or more frauds associated with the unemployment insurance claim of the claimant based on the weighted probability of each claim, a network diagram generated using the weighted probability for each claim, a set of filtered claims, and a set of business rules.