Asset and Liability Management for Banks and Insurance Companies

Corlosquet‐Habart, Marine; Gehin, William; Janssen, Jacques; Manca, Raimondo

doi:10.1002/9781119184607

Cited by 6 publications

(4 citation statements)

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“…Technological innovations such as artificial intelligence (McKinsey, 2018a ), blockchain (BCG, 2018 ) and big data (Corlosquet-Habart & Janssen, 2018 ) are creating new opportunities in the insurance sector, with the promise of increasing efficiency (Lin & Chen, 2020 ). Despite these suggestions, literature empirically assessing whether insurance companies over the past few years were able to leverage new technologies to improve efficiency is scarce.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, we are observing the increasing relevance and disruptive nature of digitalization (Fitzgerald et al, 2014 ) and the emergence of Insurtech, “a phenomenon comprising innovations of one or more traditional or non-traditional market players exploiting information technology to deliver solutions specific to the insurance industry” (Stoeckli et al, 2018 , p. 289). Interest in the possibilities arising from digital innovation (Deloitte, 2018 ), focusing in particular on the impact that new technologies such as artificial intelligence (McKinsey, 2018a ), blockchain (BCG, 2018 ) and big data (Corlosquet-Habart & Janssen, 2018 ) may have on the increase of efficiency (McKinsey, 2018b ) is noticeable for traditional insurance companies, practitioners and academicians.…”

Section: Introductionmentioning

confidence: 99%

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Translating technological innovation into efficiency: the case of US public P&C insurance companies

Lanfranchi

Grassi

2021

Eurasian Bus Rev

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In recent years, Insurtech innovations, driven by technologies such as artificial intelligence and blockchain, emerged in the insurance industry, with the promise of improving efficiency. However, while the positive impact of technology on insurance companies’ efficiency is expected, literature assessing it empirically is scarce, when it comes to recent technological change. Focusing on the US public P&C insurance sector in the period 2012–2018 and relying on both nonparametric (two stage DEA) and parametric (SFA) approaches, it emerges that on average insurance companies were not able to leverage on technological innovations to improve their efficiency. On average a relative level of efficiency among companies, according to a two stage DEA model, was quite stable in time, while the SFA approach shows that the distance between efficient and less efficient firms slightly increased. Moreover, we found one very efficient firm, almost a leader of the market in terms of efficiency, and a homogeneous group of followers, indicating that there is vast scope for improvement for less efficient companies. Nevertheless, even the most efficient company impaired its efficiency over time, suggesting that neither the leader nor on average the followers properly leveraged technology to improve their efficiency. In a competitive scenario, with new players’ entrance and fierce competition, inertia may seriously affect their positioning. Academicians, managers and policymakers should carefully consider the effects that a non-improvement of efficiency following technological change may have on market structure, competition and regulations, potentially opening to further discussion on how technological innovations adoption should be facilitated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Translating technological innovation into efficiency: the case of US public P&C insurance companies

Lanfranchi

Grassi

2021

Eurasian Bus Rev

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“…There is thus a risk of revealing confidential information through cross-checking of data from various computer files and a risk of infringement of individual freedoms to the benefit of certain organizations, in particular administrative, financial, or insurance companies [29-31]. On the other hand, information technology tools are not always reliable in their handling (complexity of procedures, equipment breakdown or theft, loss or alteration of files, viruses, etc).…”

Section: Discussionmentioning

confidence: 99%

Clinicians’ Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App

Lemey¹,

Larsen²,

DeVylder³

et al. 2019

J Med Internet Res

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Background Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients’ environments using ecological momentary assessment techniques to support preventive therapeutic decision making. Objective The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features. Methods We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient’s emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies. Results Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this. Conclusions We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups.

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“…Policymakers can easily forecast future insurance policies using predictive models and machine learning techniques [101]. Since the major goal of insurance firms is to offer dependable services and achieve the utmost customer satisfaction, text and web mining techniques may help obtain the web-based data gathered from a wide range of sources, including policy agents, websites, social media, phone records, and user-interactive systems and facilitate the more accurate prediction of customers' needs [102]. Real-time prediction of consumers' behaviour through BDA helps insurance companies launch make customercentric insurance products and make more penetrative marketing strategies.…”

Section: Bda In Insurancementioning

confidence: 99%

State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary

et al. 2023

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Academicians and practitioners have recently begun to accord Artificial Intelligence (AI) and Big Data Analytics (BDA) significant consideration when exploring emerging research trends in different fields. The technique of bibliometric review has been extensively applied to the AI and BDA literature to map out existing scholarships. We summarise 711 bibliometric articles on AI & its sub-sets and BDA published in multiple fields to identify academic disciplines with significant research contributions. We pulled bibliometric review papers from the Scopus Q1 and Q2 journal database published between 2012 and 2022. The Scopus database returned 711 documents published in journals of different disciplines from 59 countries, averaging 17.9 citations per year. Multiple software and Database Analysers were used to investigate the data and illustrate the most active scientific bibliometric indicators such as authors and co-authors, citations, co-citations, countries, institutions, journal sources, and subject areas. The USA was the most influential nation (101 documents; 5405 citations), while China was the most productive nation (204 documents; 2371 citations). The most productive institution was Symbiosis International University, India (32 documents; 4.5%). The results reveal a substantial increase in bibliometric reviews in five clusters of disciplines: (a) Business & Management, (b) Engineering and Construction, (c) Healthcare, (d) Sustainable Operations & I4.0, and (e) Tourism and Hospitality Studies, the majority of which investigate the applications and use cases of AI and BDA to address real-world problems in the field. The keyword co-occurrence in the past bibliometric analyses indicates that BDA, AI, Machine Learning, Deep Learning, NLP, Fuzzy Logic, and Expert Systems will remain conspicuous research areas in these five diverse clusters of domain areas. Therefore, this paper summarises the bibliometric reviews on AI and BDA in the fields of Business, Engineering, Healthcare, Sustainable Operations, and Hospitality Tourism and serves as a starting point for novice and experienced researchers interested in these topics.

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Asset and Liability Management for Banks and Insurance Companies

Cited by 6 publications

References 0 publications

Translating technological innovation into efficiency: the case of US public P&C insurance companies

Translating technological innovation into efficiency: the case of US public P&C insurance companies

Clinicians’ Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App

State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary

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