###### 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 \(N_{IDT}=829\) 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 \(\rho=0.16\) (\(p<0.001,N=829\)) 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. insurance; natural language processing; topic modelling; text analysis; complex networks; risk ranking +
Footnote †: journal: _Future Internet_  
establish whether Natural Language Processing (NLP) techniques [4] can be adopted, enabling a systematic approach to analysing unstructured reports--e.g., extracting key topics and sentiment--thereby helping to monitor and identify firms with higher prudential risk [1]. Automatic NLP approaches could complement existing quantitative and qualitative assessments, based on numerical data [2], and help resource allocation, focusing more resources on those insurers described by more alarming text or associated more strongly with negative events. For regulators, the potential insights and hence benefits of NLP can be considered in mainly two areas as described below. Firstly, NLP approaches might help identify emerging market issues: Some risks such as the increase in cyber-crime can hit the headlines and are relatively easy to be identified by humans engaging in knowledge search through information platforms [5]. For this reason, many studies about market fluctuations have focused on social/news media analysis to predict financial patterns, like shocks in stock prices [6; 7; 8]. Other topics might give rise to niche or underground debates, e.g., health practice [9; 10]: These topics might be more uncommonly discussed across media channels and thus become more difficult to be identified by monitoring social/news systems [10]. In this regard, NLP approaches enhanced by cognitive science [11] might help identify market perceptions towards the insurance sector. Understanding \"the market's\" view of an insurer or the sector is important in judging to what extent investors are willing to provide additional funding following an adverse event [2; 12].