Adverse Media Mining for KYC and ESG Compliance

TL;DR

This paper introduces an automated, machine-learning-based system for real-time and batch adverse media screening to help institutions manage non-financial risks like reputational damage and cyber threats more efficiently.

Contribution

It presents a scalable, high-precision adverse news filtering approach that integrates relevance, sentiment, and risk encoding for improved risk management in financial institutions.

Findings

Effective real-time adverse media detection demonstrated

High-precision filtering achieved through multi-perspective analysis

System performance validated with case studies

Abstract

In recent years, institutions operating in the global market economy face growing risks stemming from non-financial risk factors such as cyber, third-party, and reputational outweighing traditional risks of credit and liquidity. Adverse media or negative news screening is crucial for the identification of such non-financial risks. Typical tools for screening are not real-time, involve manual searches, require labor-intensive monitoring of information sources. Moreover, they are costly processes to maintain up-to-date with complex regulatory requirements and the institution's evolving risk appetite. In this extended abstract, we present an automated system to conduct both real-time and batch search of adverse media for users' queries (person or organization entities) using news and other open-source, unstructured sources of information. Our scalable, machine-learning driven approach to high-precision, adverse news filtering is based on four perspectives - relevance to risk domains, search query (entity) relevance, adverse sentiment analysis, and risk encoding. With the help of model evaluations and case studies, we summarize the performance of our deployed application.