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How I got my first job in Data Analytics with no prior experience

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Hi, I am Rashi, a Data Analyst at Blue Cross Blue Shield based out of Chicago, and here's a story of securing my first job and other offers with no prior full-time work experience. In the ever-expanding technological world of today, there are new job roles posted each day on company portals, and in the race to the finish line, candidates are forced to apply for any and every job role in the hope to secure one. This becomes especially difficult for new grads or people switching careers. The book of business expects new hires to start adding value to the organization from day 1 while nobody gives you a job without experience, and you can't gain experience without a job. Now, if you are at a point considering a big career change or searching for a job post-graduation, and if you're wondering: do I have a chance of getting hired?


Insurance 2030--The impact of AI on the future of insurance

#artificialintelligence

Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into "active" mode. Scott's personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott's assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a "pay-as-you-live" basis, will increase by 2 percent for this quarter. The additional amounts are automatically debited from his bank account. When Scott pulls into his destination's parking lot, his car bumps into one of several parking signs.


Using AI And Machine Learning To Improve The Health Insurance Process

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Health insurance companies have been looking to artificial intelligence (AI) and machine learning to identify at-risk individuals and reduce rising costs in the healthcare sphere.


InsurTech_2022-01-21_04-55-47.xlsx

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The graph represents a network of 2,082 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, 21 January 2022 at 13:09 UTC. The requested start date was Friday, 21 January 2022 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 2-day, 16-hour, 52-minute period from Tuesday, 18 January 2022 at 08:08 UTC to Friday, 21 January 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Global Big Data Conference

#artificialintelligence

A common concern surrounding automation in recent years is that it will result in widescale job losses as the work previously done by people is taken over by technology. Of course, the reality doesn't really support this narrative, and indeed, companies that invest in technology often end up employing more people as a result of the improvement in their fortunes heralded by the investment. The leadership team of the fintech company Kashat highlight the reality of investing in technology. They reveal that microfinance has traditionally been highly labor intensive, with many of the skills the same as those used in the sector for years. With the introduction of AI, new skills have been introduced into the underwriting process in order to serve at scale, while enabling employees to further expand their skillset and become even more valuable in the future.


What AI cannot do

#artificialintelligence

The following is an excerpt adapted from AI 2041 by Kai-Fu Lee and Chen Qiufan. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher. Artificial intelligence can perform many tasks better than people can, at essentially zero cost. This simple fact is poised to generate tremendous economic value but also to cause unprecedented job displacement -- a wave of disruption that will hit blue- and white-collar workers alike. In the future, AI will be doing everything from underwriting our loans to building our homes, and even hiring and firing us.


22 of the most important insurance technology trends in 2022.

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Then came 2021, when insurers focused on pandemic recovery and meeting customer expectations for digitization and personalization. While adapting to the latest insurance technologies was a challenging experience for many carriers, those who did are selling more benefits faster and smarter than ever before. From underwriting and claims to the customer journey and distribution methods, here are the top insurance technology trends our team believes will be beneficial to carriers in 2022. It was once common for insureds to undergo in-person evaluations in traditional underwriting. However, this proved a challenge during the pandemic, and many insurers had to embrace new underwriting methods. The goal of automated underwriting is to streamline information-gathering and reduce as many human touchpoints as possible. Automated underwriting uses tools and techniques like robotic process automation and artificial intelligence to import and correct data, assess risk and determine how much coverage a client should get and how much they should pay in premiums. It is important that automated underwriting programs incorporate an insurer's business rules, halting the process when human intervention is required. To this end, automated underwriting technology should enable granular configuration of roles and permissions. The benefits of saving time and money have led to many insurers implementing automated underwriting into their value chain.


How is Artificial Intelligence in Insurance addressing key challenges?

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The insurance industry, after the trade market, is another sector where it is hard to predict the next big paradigm shift. Given the tentative stability and natural catastrophes, insurance companies often stand on a trembling ground and confront massive challenges, even when it comes to adopting seamless and intuitive digital solutions such as Artificial Intelligence in Insurance. According to PwC's 20th CEO survey conducted in 39 countries, the greatest concerns that loom over the 95 CEOs of the insurance sector today are the subdued premium rate, mild interest rates, shifting consumer behavior, slow economic growth, need for regulations and technological innovations and blazing market competition. Let's delve into the idea of introducing artificial intelligence in insurance, and how it impacts the current legacy processes. This shows how the insurance industry is struggling to comprehend and leverage digital advancements.


Insurtech 2022: Hype vs. Impact

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In 2017 we set out to sort the substance from the sensation in our first hype vs. impact article, as shown in the infographic below. In 2022 we revisit our predictions and set out our stall for the future. Thankfully the majority of our 2017 predictions have come to fruition with AI, big data, IoT, usage-based and telematics insurance all continuing to have an impact. As predicted, other areas have not been so lucky and have, therefore been dropped from our 2022 assessment. Self-driving vehicles: Autonomous vehicles saw huge hype in 2017 but with predictably little impact since, given the complexity of getting to mass adoption.


InsurTech_2022-01-14_04-55-46.xlsx

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The graph represents a network of 1,546 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, 14 January 2022 at 13:08 UTC. The requested start date was Friday, 14 January 2022 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 2-day, 4-hour, 44-minute period from Tuesday, 11 January 2022 at 20:16 UTC to Friday, 14 January 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.