Join the Medical Mutual team! See our latest job opening here: https://bit.ly/3RvkDow #AnalystJobs #Analyst
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What do legal professionals, data analysts, medical professionals, engineers and marketing professionals have in common? A place within the #InsuranceIndustry! Working in Insurance goes well beyond sales, underwriting and claims. We need experts in #LossControl, #ForensicAccounting, #Educators, #EnvironmentalProtection, #UXDesign and so much more! If you work within Insurance, and aren’t in claims, underwriting or sales, drop a comment and share what you do! #insurancediversity #insuranceopportunities
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Data Analyst | SQL, PowerBI, Advance Excel | Empowering Businesses with Insightful Analytics | Turned more than 1M+ Records into Actionable Sales Insights
📈🏥 Unlocking Insights: The Data Analyst's Vital Role in Health and Term Insurance 📈💼 In the dynamic world of insurance, data is the compass guiding decisions. 🧭 How Data Analysis transforms this industry from what I think it to be: 🔍 Risk Assessment: Analyzing vast datasets, one identifies trends and assesses risk factors. This precision ensures fair premiums and helps insurers thrive. 💊 Health Outcomes: By examining health data, one enables insurers to tailor policies that promote wellness and support policyholders through their health journeys. 📊 Pricing Strategies: We work behind the scenes to develop pricing strategies that balance affordability and profitability, benefiting both customers and insurers. 🚀 Fraud Detection: The vigilant data analysis tools spot anomalies and patterns that signal potential fraud, protecting the industry's integrity. Data analysts in health and term insurance bridge the gap between raw data and informed decisions. Are you a data enthusiast in the insurance field? Share your insights or experiences in the comments! Let's continue to shape the future of insurance together! 🌐💬 Follow me Parth Agheda for more such bite-sized informative content on Data Science. #DataAnalysis #Insurance #HealthInsurance #TermInsurance #DataAnalytics
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At Assurance IQ, we try to meet people where they are to help them access quality insurance coverage. Aly Hassan, a data scientist based in Toronto, shares more about how we do this: “We use data science to make each person’s experience with Assurance as seamless as possible. For example, if someone is ready to buy a plan right away, our algorithm might route them to a more express experience to connect to an agent faster. If someone is new to Assurance, we might point them toward a more in-depth conversation with one of our Guides first, to make sure we can help them and are sending them to the right place. We know some people would prefer to receive a phone call, while others might prefer a text or an email, so we can also adapt our outreach to their preferences. We want to meet people where they are, and ultimately make it easier to find quality insurance coverage that meets their needs.” Want to join us? We’re #hiring. Learn more and apply: https://lnkd.in/gynm3Psy
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Retired after 20 years as a Research Consultant at Northwestern Mutual. I am open to consulting on challenging projects.
We at Smart Data Analyst (smartdatanalyst.com) have created the best and simplest Medical Rating Manual in life insurance industry. It is based on our proprietary predictive model and is so robust that the mortality of the best price class is around 10 times lower than that of the worst one. It's an excellent way to establish the primacy and holistic nature of CV profile. It improves the entire UW paradigm making it CV-oriented. Have you seen a rating table that is capable of identifying the best 75% of cases that have mortality around or under 60% of mortality for the total business? And that obtained using only sex, age, BMI and systolic/diastolic blood pressure, without even lipids data. Individual impairments, if severe enough, can simply be taken care of by bumping up the "CV class". Of course for very severe impairments the applicant should be declined. Our rating manual can be used in conjunction with any other ones. Imagine first identifying best 75% of the business and then applying to it any additional criteria you want. And for clean cases the result of CV UW will not to be altered in any way. Our model has been tested using simulated insurance data, and we'd like very much to determine whether it works with real ones. If it does, that could be a major breakthrough in life UW. Currently we are looking for suitable customers/partners to take this project into practical realm.
Smart Data Analyst - NY/NJ/IL Data Consulting Services
smartdatanalyst.com
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Check out how Prophet is reshaping insurance forecasting, enhancing data-driven decisions! Written by Arusha Kelkar, a Senior Data Scientist at Constellation4, this blog dives into the significance of Claims Loss Ratio Forecasting in gauging the profitability of healthcare companies in comparison to their competitors. Arusha effectively illustrates the application of advanced data science techniques, highlighting how these models can empower healthcare organizations to thrive. Read the full blog below👇 #Constellatin4 #Healthcare #ClaimsLossRatio #Prophet #Forecasting #HealthcareAnalytics #Data #MedTech
Unveiling the Power of Claims Loss Ratio Forecasting - Healthcare - Constellation4
https://constellation4.com
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Data Engineer at Thoughtworks, Ex - CTS || Bigdata || Azure || PySpark || SQL writes to 11k+ @ Linkedin. Azure certified: AZ-900,DP-900,DP-203. Databricks certified associate data engineer.
Link to previous post: https://lnkd.in/daSPdv7G Q 47: As a data engineer in an insurance company, you are tasked with analyzing the claims data to derive insights into the cumulative claim amounts for policyholders. The claims data is stored in a table named claims, with columns such as claim_id, policyholder_id, claim_date, claim_amount, and coverage_type. Using SQL, write a query that calculates the cumulative claim amount for each policyholder over time, partitioned by policyholder_id. Solution: CREATE TABLE claims ( claim_id INT, policyholder_id INT, claim_date DATE, claim_amount DECIMAL(10, 2), coverage_type VARCHAR(50) ); INSERT INTO claims VALUES (1, 101, '2023-01-01', 500.00, 'Auto'), (2, 102, '2023-01-02', 750.00, 'Health'), (3, 101, '2023-01-03', 300.00, 'Home'), (4, 103, '2023-01-04', 1000.00, 'Auto'), (5, 102, '2023-01-05', 600.00, 'Health'), (6, 101, '2023-01-06', 400.00, 'Auto'), (7, 103, '2023-01-07', 800.00, 'Home'); WITH CumulativeClaimsCTE AS ( SELECT claim_id, policyholder_id, claim_date, claim_amount, coverage_type, SUM(claim_amount) OVER (PARTITION BY policyholder_id ORDER BY claim_date) AS cumulative_claim_amount FROM claims ) SELECT claim_id, policyholder_id, claim_date, claim_amount, coverage_type, cumulative_claim_amount FROM CumulativeClaimsCTE; 1) We create a Common Table Expression (CTE) named CumulativeClaimsCTE to calculate the cumulative claim amount for each policyholder. The SUM(claim_amount) OVER (PARTITION BY policyholder_id ORDER BY claim_date) uses the PARTITION BY clause to calculate the sum of claim amounts for each policyholder separately. 2) In the final SELECT statement, we retrieve the columns from the CTE, including the cumulative_claim_amount. This provides the cumulative claim amount for each claim, partitioned by policyholder_id. #sql #dataengineer #dataengineering #cte #insurance #partitioning #sum
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The continuous evolution of technology within the #Insurance industry offers an exciting opportunity to broaden our search for top-notch talent and upskill teams to meet future #underwriting requirements. Assessing the impact of digitisation on talent attraction and retention, co-founder and CEO Andy Moss, told Mia Wallace from Insurance Business UK: 'If you look at the type of roles being advertised by insurers these days, you’ll see a lot more breadth in terms of the skills and roles companies are looking for.' As #Data becomes increasingly important at the underwriting stage, there are many more data science and integration roles required to connect the various parts of the journey. Read Andy's views of talent and technology in full here: https://lnkd.in/eiA_fWWt #Innovation #Technology #CommercialUnderwriting #InsuranceJobs
CEO on how the role of technology in underwriting has changed
insurancebusinessmag.com
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My 16th Project, Health Insurance Coverage (A.C.A) Analysis. This dataset was provided by Quantum Analytics NG. Tool used: Power BI. Insights: 1. Average month Tax Credit, Medicaid 2013 & 2016, Employer Health Insurance. 2. Top 10 states by Employer Health Insurance Coverage 2015. 3. Top 10 states by Medicaid Enrollment 2013 & 2016. 4. Average monthly Tax Credit by state Top 10. 5. Uninsured rate 2010 & 2015 by state Top 10. 6. % States Medicaid Expansion. 7. MarketPlace Health Insurance by State. 8. Medicaid Enrollment Change by State. My appreciation goes to Quantum Analytics NG, Jonathan Osagie, Prince Chukwuemeka (MCDAA, MCT) for their commitment to my growth as a data analyst.
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If you are considering your first career or a change in career, consider these benefits: Renewable revenue. Sales professionals aren’t starting from zero each year. Resiliency in an economic downturn. Our agency has NEVER laid off employees. Ownership. At Knight Insurance Group, when you become an employee, you also become an owner with no investment on your part. Steady sector. Insurance is a critical component of our economy. You can feel secure that it’s not going away. Endless job variety. Whether you’re interested in technology, marketing, HR, customer service, management, there is a job in insurance!
Whether you're drawn to analytics, underwriting, claims management, or technology, there's a place for your talents within the #insurance industry. Ready to explore the possibilities? Visit insurancecareers.org to view a variety of career pathways in the industry. #CareerOpportunities #InsuranceCareers #NationalCareerDevelopmentMonth
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