Detecting Economic Vulnerability via Multi-Agent LLM Architecture and Context-Aware Cluster Analysis
Vitali Herrera-Semenets (Advanced Technologies Application Center)
Lázaro Bustio-Martínez (Universidad Iberoamericana)
Jan van den Berg (TU Delft - Cyber Security)
Miguel Ángel Álvarez-Carmona (Centro de Investigacion en Matematicas, CIMAT)
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Abstract
Social security programs aim to protect vulnerable populations; however, accurately identifying individuals with significantly lower incomes than their peers (accounting for age, occupation, and education level) remains an operational challenge. This article proposes an innovative method for detecting economic vulnerability by combining income data enrichment with large language models in a multi-agent architecture, unsupervised clustering techniques, and statistical heuristics. The developed algorithm analyzes demographic and labor-related variables to estimate expected annual income by profile, thereby identifying atypical discrepancies that suggest vulnerability. This approach not only optimizes the prioritization of beneficiaries for targeted assistance but also serves as a preventive mechanism against the inadvertent exclusion of eligible groups. Preliminary results demonstrate the method’s effectiveness in detecting hidden vulnerability particularly among young adults aged 17–23, whose high underemployment rates () in recent national statistics closely align with the concentration of vulnerability detected. These findings underscore its potential as a complementary tool to enhance equity and efficiency in social policy implementation.