Adverse Media Mining for KYC and ESG Compliance

• URL: http://arxiv.org/abs/2110.11542v1
• Date: Fri, 22 Oct 2021 01:04:16 GMT
• Title: Adverse Media Mining for KYC and ESG Compliance
• Authors: Rupinder Paul Khandpur, Albert Aristotle Nanda, Mathew Davis, Chen Li, Daulet Nurmanbetov, Sankalp Gaur and Ashit Talukder
• Abstract summary: Adverse media or negative news screening is crucial for the identification of such non-financial risks. We present an automated system to conduct both real-time and batch search of adverse media for users' queries. Our scalable, machine-learning driven approach to high-precision, adverse news filtering is based on four perspectives.
• Score: 2.381399746981591
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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