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InsurTech_2022-04-01_04-55-46.xlsx

#artificialintelligence

The graph represents a network of 1,882 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 April 2022 at 12:09 UTC. The requested start date was Friday, 01 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 15-minute period from Tuesday, 29 March 2022 at 15:45 UTC to Friday, 01 April 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


InsurTech_2021-12-31_04-55-47.xlsx

#artificialintelligence

The graph represents a network of 2,048 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 31 December 2021 at 13:12 UTC. The requested start date was Friday, 31 December 2021 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 10-hour, 32-minute period from Monday, 27 December 2021 at 14:28 UTC to Friday, 31 December 2021 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


#insurTech Twitter NodeXL SNA Map and Report for Monday, 27 December 2021 at 14:43 UTC

#artificialintelligence

The graph represents a network of 2,365 Twitter users whose recent tweets contained "#insurTech", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Monday, 27 December 2021 at 17:08 UTC. The tweets in the network were tweeted over the 6-day, 12-hour, 57-minute period from Tuesday, 21 December 2021 at 01:40 UTC to Monday, 27 December 2021 at 14:37 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.


InsurTech_2021-12-24_04-55-46.xlsx

#artificialintelligence

The graph represents a network of 1,552 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 24 December 2021 at 13:11 UTC. The requested start date was Friday, 24 December 2021 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 6-day, 7-hour, 49-minute period from Friday, 17 December 2021 at 17:10 UTC to Friday, 24 December 2021 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


AI in Finance: Challenges, Techniques and Opportunities

arXiv.org Artificial Intelligence

AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.


Why artificial intelligence still needs a human touch - California News Times

#artificialintelligence

This article is an on-site version of the #fintechFT newsletter. Using artificial intelligence to improve fraud detection is becoming one of the hottest trends in the insurance industry, but it is also one of the most controversial trends. US insurer Lemonade has become a case study of potential technology rewards and reputational risks. Lemonade has become one of the most successful large-scale IPOs in 2020, fulfilling its promise to speed up and simplify lessor insurance and home insurance with AI-powered apps. But earlier this year, it sparked a social media backlash in concerns about the behavior of the algorithm.


InsurTech_2020-08-28_04-48-01.xlsx

#artificialintelligence

The graph represents a network of 3,616 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 28 August 2020 at 12:04 UTC. The requested start date was Friday, 28 August 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 7-day, 0-hour, 35-minute period from Saturday, 15 August 2020 at 11:58 UTC to Saturday, 22 August 2020 at 12:33 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


The Future of Disruptive and Enabling Financial Technology post CV-19

#artificialintelligence

In addition, Banks likely constrained given higher capital preservation requirements 2020 will be challenging for FinTechs to navigate, but prosperous times remain ahead post crisis where Disruptive winners take it all and demand for AI, Tech and IoT companies that help financials transform to a digital and Data driven interaction will surge.


Insurance Technology: 11 Disruptive Ideas to Transform Traditional Insurance Company with Machine Learning, APIs, Blockchain, and Telematics

#artificialintelligence

Have you ever tried to check your insurance claim status? It often requires several calls, some emails, or even visiting an agent to get claim status details. Lack of web presence equals lower customer satisfaction. Today, nearly 61 percent of customers prefer to monitor their application status with digital tools. While some insurance carriers have made significant modifications courtesy of disruptive digitalization (we've already discussed this topic in our whitepaper), most companies trail behind. And the chasm between modern insurtech agencies and traditional ones is deepening. The disruption caused by Haven Life is a prime example.


Technology is the Future of Insurance - FINTECH Circle

#artificialintelligence

The traditional insurance business model is going to fundamentally and permanently change from what was invented in the Lloyd's coffee shop in 1668 and which has been the basis of insurance ever since: Risk Mitigation via Indemnity and minimising interactions with customers. The next 10 years will see unprecedented change in the insurance industry. Traditional insurance companies selling and servicing the old style product model are being replaced by IT enabled, risk management companies selling profitable, long term contracts for valuable services delivered as RMAAS (risk management as a service). The experience of other industries offers a stark warning to the insurance industry: banks suffering death by a thousand cuts from tech companies in payments, cards, lending and now open banking, show the way it will go. Even software is sprinting to a cloud based, software as a service (SAAS) model.