Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts

Version 1 : Received: 19 September 2022 / Approved: 21 September 2022 / Online: 21 September 2022 (10:25:26 CEST)

How to cite: Stefan, C.; Stella, M. Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts. Preprints 2022, 2022090324 (doi: 10.20944/preprints202209.0324.v1). Stefan, C.; Stella, M. Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts. Preprints 2022, 2022090324 (doi: 10.20944/preprints202209.0324.v1).

Abstract

The ability to identify and rank risk is essential for efficient and effective supervision of financial service firms, such as banks and insurers. Risk ranking ensures limited resources are allocated where they are most needed. Today, automatic risk identification within insurance supervision primarily relies on quantitative metrics based on numerical data (e.g. returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can achieve similar automated risk ranking and identification by analysing textual data, i.e. NIDT=829 investor transcripts from Bloomberg. To this aim, this work explores and tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of "climate" grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson’s correlation of ρ=0.16 (p<0.001,N=829) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results’ stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in automated risk ranking.

Keywords

Insurance; natural language processing; topic modelling; text analysis; complex networks; risk ranking

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.

We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.