Great American Insurance Group’s Post

One common issue encountered while working as a business analyst in the life insurance industry is the complexity of data analysis and interpretation. With vast amounts of data available, ranging from customer demographics to policy details and claims history, it can be overwhelming to sift through and extract meaningful insights. To reduce this complexity and enhance efficiency, one potential solution is to implement advanced data analytics tools and techniques. These tools can help streamline data processing, automate repetitive tasks, and uncover hidden patterns or trends that may not be immediately apparent. Additionally, investing in employee training programs to enhance data analysis skills can empower business analysts to leverage these tools effectively. Furthermore, developing clear and concise reporting frameworks can facilitate better communication of insights to key stakeholders, enabling informed decision-making. By embracing technology and fostering a culture of data-driven decision-making, businesses can overcome the challenges associated with data complexity in the life insurance industry. RGI Group #insurance #life_insurance #business_Analyst #Data_Analysis

🚀 Exciting News! Just wrapped up my first project as a Data Analyst Associate at RevoU X Zurich Insurance. The 'Ads Performance Dashboard' is now live! 📊 Zurich is a leading insurance provider serving its customers in local and global markets, actively investing in digital marketing to increase brand visibility and attract potential customers. The creation of this dashboard stems from Zurich's need for proper evaluation and guidance on how data can be utilized to analyze the decision-making process in managing advertising. Target Audience: ✅ Marketing team. ✅ Marketing Manager. Objective of the Dashboard: ✅ Understanding ads that cover basic marketing goals, including awareness, interest, and decision-making. ✅ Analyzing the effectiveness of ads in terms of conversion rates. ✅ Analyzing the efficiency of ads in terms of cost. ✅ Identifying the best-performing ads. A special thank you to my SDC, Muhammad Maynanda Alphatian, for his support and insights during the creation of this dashboard. Your contribution has been invaluable! Explore the dashboard here https://lnkd.in/gPPuvv8z and share your thoughts below! Let's continue the journey of learning and growth together. 🌱 Disclaimer: The data provided is a representation of the original data, the amount of which has been adjusted. #DataAnalytics #SalesDashboard #LearningJourney #DataEnthusiast #CareerDevelopment

Before hiring managers and talent teams start hiring cycles it is really important to understand the current market you are trying to attract from. We work with lots of insurance businesses who are currently trying to build their data teams with Data Scientists and Data Engineers specifically. For example, did you know that the average time a Data Engineer spends in a role is 1.5 years? To help you, we have pull together some key market insights into the demographics, educational backgrounds and career trends of Data Scientists and Data Engineers within insurance. Check out some of the top line facts and also read my article to give you further insights into this pool of talent. https://lnkd.in/gPhGMSqw #insurance #insuranceindustry #womenindata #womenininsurance #datascience #dataengineering

As a BI lead in the insurance sector, I understand the critical importance of data literacy in driving informed decision-making and fostering a data-driven culture within organizations. Today, let’s dive into the topic of data literacy! Data literacy is not just about understanding data, it’s about empowering everyone in the organization to confidently interpret, analyze, and apply data insights to their roles. From underwriters to claims processors, every team member plays a vital role in harnessing the power of data to enhance efficiency and drive business growth. Here’s a simple example to illustrate the significance of data literacy: Imagine you’re an underwriter at an insurance company. You receive a flood of applications for property insurance from a region hit by recent severe weather. Some applications provide inconsistent flood risk assessments. Without strong data literacy skills, it might be tempting to accept or reject applications based on surface-level information. However, a data-literate underwriter dives deeper, cross-referencing data with historical weather patterns and flood maps. By leveraging data literacy skills, the underwriter identifies inconsistencies and verifies information accuracy. This helps the company avoid financial losses by accurately assessing risk and providing fair coverage to policyholders. How do you promote data literacy within your organization? Share your insights and experiences in the comments below! #DataLiteracy #Insurance #BusinessIntelligence #Data

Well, this is it. Today is my last day working at Assurance IQ since the company announced on April 30th that they were going out of business.   I have been blessed to have worked with some amazing people who I will truly miss. It was a joy logging in to work every day, working with an awesome team and doing work that I thoroughly enjoyed. I honestly loved it!   And, I was favored enough to be asked to stay until July 31st to help with the business wind down process. They asked me to stay on to manage the high volume of Medicare marketing complaints and inquiries asset requests that would continue coming in from the carriers. I would also train and support the remaining core staff who would manage those requests after I left. I was more than happy to do that! But today is the end of that chapter, and now it's time to move on to my next opportunity.   Where do I go from here? What company needs this rockstar?!! ✨   Well, with over 12 years of industry education and experience, I definitely want to stay on the corporate administration side of insurance, in all things Medicare, U65 Health and Final Expense/Life. However, I will keep my AHIP certification current and my broker licenses in over 32 states. But I'm looking for those remote analyst roles that are business + technical because that's where I truly thrive. And as an analyst, those skills work excellently within most all departments including marketing, finance, compliance, operations, IT and others.   I enjoy communicating and problem-solving where you have to "figure things out" and use technical solutions to resolve and present findings. Whether that's writing SQL queries or presenting the data in a visualization tool or in a stakeholder's meeting, I tend to really grasp my work and own my projects. So once I get the basics covered, just let me drive!   Feel free to comment with an introduction or a lead, but also...click that "repost" button down below. ⬇ Please do share my post with your network so it can reach as many decision-makers and hiring managers as possible. They're looking for me! 😀 Email: bvwright (at) gmail (dot) (com) Cell: (205) 679-8158 LinkedIn: https://lnkd.in/gMnU88UJ Thank you everyone! Send those good vibes my way and wish me the best! #businessanalyst #compliancespecialist #medicareauditspecialist #medicarespecialist #insurancebusinessanalyst

As a retired IT Business Analyst, I often cringe when I observe situations where that role may have been minimized or in some cases, completely omitted. Here is an example of a pest control company I have been using since 1990 who last night sent me an email confirming that my account had been canceled at my request. Of course, I did not cancel my service. What happened was the company migrated to a new service/billing system over the weekend and as a result, the old system generatated emails like the one below as it processed each account record. A Business Analyst with deep system knowledge would have predicted this. Absent that person, a small scale dress rehearsal would have been warranted, so this could have been avoided. To this company’s credit, their customer service phones were well staffed as I did not have to wait very long to confirm my suspicions. I know some of my former colleagues are smirking, knowing that I am really dunking on a certain development methodolgy. #requirementsmatter #businessanalysisrules

The Role of a Business Operations Analyst A Business Operations Analyst serves as a linchpin between various departments within an organization, bridging the gap between strategy and execution. Visit for more: https://lnkd.in/d9BcBxM9 #BusinessAnalyst #OperationalEfficiency #DataAnalysis #StrategicPlanning #BusinessSuccess #EfficiencyInAction #ContinuousImprovement #BusinessSkills #Adaptability #OperationalExcellence

Dear Business Analyst, Next time a client asks about price, time, and quality, remind them that they can't have it all. As a business analyst, it's important to manage expectations and prioritize accordingly. #client #businessanalyst

Data are important, but which data are the most important in insurance? I've seen carriers become interested in using new analytical tools to price risk or "score" risks by comparing filed pricing to actuarially identified (or data model identified) pricing need. Sometimes, this results in smart decisions and other times it flies in the face of common sense (e.g., a surplus of filed over actuarial indication can lead to discounting rates too often). Modern data analytics is a fast developing field and one where machine learning proves highly useful and will continue to do so. Overall, this bodes well for humans and we can use these tools to run calculations and assess info in ways that take more time than our days (or lives) would allow. However, the very newness of some of these tools brings inherent risky (smaller sample size of time in use, greater likelihood of outliers). For example, if well-established actuarial methods are used to price a risk and newer, potentially unrelated methods are used to generate a rather different number, which do you trust when comparing the two results? Perhaps an experienced person blending the two can make the right determination. Unfortunately, though, the human element is becoming weaker in my estimation (anecdotally, of course, it's hard to calculate to what extent). Seasoned underwriters often know exactly what they're doing even when presented with more than two tools, but newer underwriters may have yet to develop "gut instinct" aka the hard to describe logic derived from experience. Too much use of tools that are theoretically "smarter" than their users can rob their users of the opportunity to develop confidence as underwriters. It can result in an attitude summed up in the question, "who am I to challenge a data model?" The answer is, of course, "you are someone with agency and greater vision than any data model," but convincing someone of this can be hard. So, to answer my initial question about which data are the most important, I'd say those that we understand best and that have the longest track records. This favors established means of analyzing numbers but does not preclude the use of new tools (many of which are innovative and beneficial). It simply means we should not let the shine of newness limit our ability to view things with the clarity of common sense. #insurance #actuaries #data #datamodels #insurancedata #datamodeling #actuarialdata #bigdata

Why data analysis skills are essential for the Insurance Industry In the dynamic world of insurance, leveraging data analysis skills is more crucial than ever. Here are some reasons why: 1. Risk Assessment: Analysing historical data helps insurers understand patterns of risk and predict future losses accurately, enabling better risk assessment and mitigation strategies. 2. Pricing Precision: By crunching numbers on demographics, claims history, and market trends, insurers can determine appropriate premiums, ensuring competitive pricing while maintaining profitability. 3. Fraud Detection: Data analysis techniques aid in identifying suspicious patterns or behaviours, allowing insurers to combat insurance fraud effectively and reduce financial losses. 4. Customer Segmentation: Analysing customer data enables insurers to tailor products and services to specific market segments, enhancing customer satisfaction and retention. 5. Claims Management Efficiency: Streamlining the claims process through data analysis improves accuracy, speeds up settlements, and helps identify fraudulent claims, ultimately enhancing customer experience. In a competitive and data-driven industry, honing data analysis skills isn’t just beneficial, it's imperative for insurance professionals to make informed decisions and better serve their clients. #DataAnalysis #InsuranceIndustry #RiskManagement