[2602.23373] An Agentic LLM Framework for Adverse Media Screening in AML Compliance

Computer Science > Artificial Intelligence arXiv:2602.23373 (cs) [Submitted on 29 Dec 2025] Title:An Agentic LLM Framework for Adverse Media Screening in AML Compliance Authors:Pavel Chernakov, Sasan Jafarnejad, Raphaël Frank View a PDF of the paper titled An Agentic LLM Framework for Adverse Media Screening in AML Compliance, by Pavel Chernakov and 2 other authors View PDF Abstract:Adverse media screening is a critical component of anti-money laundering (AML) and know-your-customer (KYC) compliance processes in financial institutions. Traditional approaches rely on keyword-based searches that generate high false-positive rates or require extensive manual review. We present an agentic system that leverages Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to automate adverse media screening. Our system implements a multi-step approach where an LLM agent searches the web, retrieves and processes relevant documents, and computes an Adverse Media Index (AMI) score for each subject. We evaluate our approach using multiple LLM backends on a dataset comprising Politically Exposed Persons (PEPs), persons from regulatory watchlists, and sanctioned persons from OpenSanctions and clean names from academic sources, demonstrating the system's ability to distinguish between high-risk and low-risk individuals. Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR) Cite as: arXiv:2602.23373 [cs.AI]   (or arXiv:2602....