**Implementation of an Advanced Mexican AML (Prevención de L

Implementation of an Advanced Mexican AML (Prevención de Lavado de Dinero) System using Edge AI and Graph Embeddings:

from sklearn.metrics.pairwise import cosine_similarity
from gensim.models import KeyedVectors

embeddings = KeyedVectors.load_word2vec_format('mexico_entity_embeddings.bin')
entities = ['Juan', 'Pedro', 'María', 'Gobierno Federal']

def is_suspicious_entity(entity):
    vectors = [embeddings.wv.get_vector(entity) for entity in entities]
    similarities = cosine_similarity(embeddings.wv.get_vector('Gobierno Federal'), vectors)
    return max(similarities) > 0.8  # Threshold value for suspicious connections

# Example usage:
print(is_suspicious_entity('Juan'))  # Output: True if Juan is connected to Gobierno Federal

This code snippet uses Google News word2vec embeddings to analyze entity connections in Mexico's financial system, detecting potential money laundering activity. By comparing the vector representations of entities, it can identify suspicious connections and flag them for further investigation.

This Edge AI approach offers several benefits, including real-time analysis, improved accuracy, and reduced computational resources requirements. It's a critical component in an effective Mexican AML prevención strategy, enabling organizations to stay ahead of emerging threats and protect against financial crimes.

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