Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts
dc.contributor.author | Claus, S | |
dc.contributor.author | Stella, M | |
dc.date.accessioned | 2022-11-04T10:29:21Z | |
dc.date.issued | 2022-10-12 | |
dc.date.updated | 2022-11-04T09:39:03Z | |
dc.description.abstract | The ability to spot key ideas, trends, and relationships between them in documents is key to financial services, such as banks and insurers. Identifying patterns across vast amounts of domain-specific reports is crucial for devising efficient and targeted supervisory plans, subsequently allocating limited resources where most needed. Today, insurance supervisory planning primarily relies on quantitative metrics based on numerical data (e.g., solvency financial returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can highlight events and relationships of relevance for regulators that supervise the insurance market, replacing human coding of information with automatic text analysis. To this aim, this work introduces a dataset of NIDT=829 investor transcripts from Bloomberg and explores/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 supporting effective oversight of the insurance market. | en_GB |
dc.format.extent | 291- | |
dc.identifier.citation | Vol. 14(10), article 291 | en_GB |
dc.identifier.doi | https://doi.org/10.3390/fi14100291 | |
dc.identifier.uri | http://hdl.handle.net/10871/131626 | |
dc.identifier | ORCID: 0000-0003-1810-9699 (Stella, Massimo) | |
dc.language.iso | en | en_GB |
dc.publisher | MDPI | en_GB |
dc.relation.url | https://osf.io/9xh82/, | en_GB |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_GB |
dc.subject | insurance | en_GB |
dc.subject | natural language processing | en_GB |
dc.subject | topic modelling | en_GB |
dc.subject | text analysis | en_GB |
dc.subject | complex networks | en_GB |
dc.subject | risk ranking | en_GB |
dc.title | Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-11-04T10:29:21Z | |
dc.identifier.issn | 1999-5903 | |
dc.description | This is the final version. Available on open access from MDPI via the DOI in this record | en_GB |
dc.description | Data Availability Statement: The reports analysed in this study are publicly available on the Bloomberg Terminal (cf. www.bloomberg.com, last accessed: 14 September 2022). A copy of these transcripts is available on this Open Science Foundation repository: https://osf.io/9xh82/, accessed on 10 October 2022. | en_GB |
dc.identifier.eissn | 1999-5903 | |
dc.identifier.journal | Future Internet | en_GB |
dc.relation.ispartof | Future Internet, 14(10) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-10-10 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-10-12 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-11-04T10:25:34Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2022-11-04T10:29:27Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2022-10-12 |
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Except where otherwise noted, this item's licence is described as © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).