@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://aclanthology.org/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{\%}.",
}
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<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%.</abstract>
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%0 Conference Proceedings
%T News Risk Alerting System (NRAS): A Data-Driven LLM Approach to Proactive Credit Risk Monitoring
%A Nygaard, Adil
%A Upadhyay, Ashish
%A Hinkle, Lauren
%A Skotti, Xenia
%A Halliwell, Joe
%A Brown, Ian C.
%A Noronha, Glen
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F nygaard-etal-2024-news
%X 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%.
%U https://aclanthology.org/2024.emnlp-industry.32
%P 429-439
Markdown (Informal)
[News Risk Alerting System (NRAS): A Data-Driven LLM Approach to Proactive Credit Risk Monitoring](https://aclanthology.org/2024.emnlp-industry.32) (Nygaard et al., EMNLP 2024)
ACL
- Adil Nygaard, Ashish Upadhyay, Lauren Hinkle, Xenia Skotti, Joe Halliwell, Ian C Brown, and Glen Noronha. 2024. News Risk Alerting System (NRAS): A Data-Driven LLM Approach to Proactive Credit Risk Monitoring. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 429–439, Miami, Florida, US. Association for Computational Linguistics.