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Risk detection through LLMs: An EU banking case study in monitoring media with AI

Vedad Sehanovic et al.

Journal of Risk Management in Financial Institutions2026https://doi.org/10.69554/xddx5506article
ABDC C
Weight
0.50

What the paper says

This paper presents a case study of an artificial intelligence (AI)-supported media monitoring system implemented in a European commercial banking group to enhance early detection of emerging risks. The pipeline processes millions of media articles daily using a scalable Databricks-based architecture and large language models (LLMs) for relevance scoring, novelty detection and summarisation. It demonstrates how AI-based text analysis supports credit, liquidity and geopolitical risk monitoring by transforming unstructured news into risk-relevant intelligence delivered through automated e-mail briefings, dashboards and early warning system (EWS) integration. The case study further illustrates how such AI-supported media monitoring can be governed and deployed within the regulatory and supervisory framework of the European banking sector while strengthening risk awareness and decision making and offering additional value for compliance, operational risk and communication functions. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

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https://doi.org/https://doi.org/10.69554/xddx5506

Or copy a formatted citation

@article{vedad2026,
  title        = {{Risk detection through LLMs: An EU banking case study in monitoring media with AI}},
  author       = {Vedad Sehanovic et al.},
  journal      = {Journal of Risk Management in Financial Institutions},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.69554/xddx5506},
}

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Risk detection through LLMs: An EU banking case study in monitoring media with AI

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.