@inproceedings{nygaard-etal-2024-news,
    title = "News Risk Alerting System ({NRAS}): A Data-Driven {LLM} Approach to Proactive Credit Risk Monitoring",
    author = "Nygaard, Adil  and
      Upadhyay, Ashish  and
      Hinkle, Lauren  and
      Skotti, Xenia  and
      Halliwell, Joe  and
      Brown, Ian C  and
      Noronha, Glen",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-industry.32/",
    doi = "10.18653/v1/2024.emnlp-industry.32",
    pages = "429--439",
    abstract = "Credit risk monitoring is an essential process for financial institutions to evaluate the creditworthiness of borrowing entities and minimize potential losses. Traditionally, this involves the periodic assessment of news regarding client companies to identify events which can impact their financial standing. This process can prove arduous and delay a timely response to credit impacting events. The News Risk Alerting System (NRAS) proactively identifies credit-relevant news related to clients and alerts the relevant Credit Officer (CO). This production system has been deployed for nearly three years and has alerted COs to over 2700 credit-relevant events with an estimated precision of 77{\%}."
}