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.

本文介绍了一种基于机器学习的自动化系统，实现了对实时和批量的负面媒体进行搜索，旨在进行高精度的非金融风险筛查。该系统采用四个角度 - 风险领域相关性，搜索查询相关性，负面情感分析和风险编码，为用户提供实时的信息监控和更新。

KYC和ESG合规的不良媒体挖掘