thewave’s Post

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.

Balancing Test Data Management: Finding the Perfect Middle Ground Imagine an old-fashioned balance scale. To achieve equilibrium, you must weigh both sides carefully until the scale rests perfectly in the middle. This analogy beautifully captures the essence of balancing the interests of software quality and information security in test data management. In the initial stages of managing test data, we often face a crucial balancing act. On one side, there's the necessity of using data that is as representative as possible to ensure software quality. On the other, there's the imperative to secure data to meet stringent information security standards. The challenge lies in striking the right balance. For instance, in the insurance industry, balancing these interests differs vastly between car insurance and health insurance. The needs and priorities for test data in car insurance, which might focus on claims data, differ from those in health insurance, which deals with highly sensitive patient data. There's no one-size-fits-all solution. Different industries, and even different segments within the same industry, weigh these interests differently. However, with years of experience in test data management, we at DATPROF have consistently found a middle ground that satisfies both information security professionals and software quality teams. Often, the solution is simpler than initially anticipated. If you're looking to navigate this balance and take some practical steps in the right direction, let's connect! I'm eager to share insights and help you achieve optimal test data management for your organization. #TestDataManagement #SoftwareQuality #InformationSecurity

Excited to share my latest project where I delved into the world of insurance claims data analysis! 📊 📌 Project Overview: I conducted exploratory analysis and hypothesis testing on insurance claims data to uncover insights and drive informed decision-making. 🔍 Key Tasks: Data Audit: Ensured data integrity by auditing data types and aligning them with their business significance. Missing Value Imputation: Imputed missing values with appropriate measures (mean for continuous, mode for categorical). Customer Segmentation: Categorized customers into age groups (Children, Youth, Adult, Senior) based on their age. Analysis & Visualization: Conducted various analyses and visualized trends using charts and graphs. 💡 Key Insights: >Average claim amounts across segments were calculated to understand customer behavior. >Total claim amounts based on incident causes were analyzed, focusing on claims made before October 1, 2018. >Driver-related insurance claims by adults from specific states were identified. >Gender and age-based analysis of claim amounts was visualized using pie and facetted bar charts. >Hypothesis testing was conducted to explore relationships and similarities within the data. 🚀 Business Implications: Insights from the analysis can inform strategic decisions related to customer segmentation, claims processing, and fraud detection. Understanding trends in claim amounts and customer behavior can help optimize insurance products and services. Github: https://lnkd.in/g6eKBzrD

Sharing a recent experience where my personal and professional experiences intersect! ⚠ We recently received a notice that our homeowner's insurance would not be renewed because the provider was leaving the area. While unfortunate, I wasn't surprised because the location of the property is susceptible to hurricanes. 💲 I have been going through the somewhat painful exercise of gathering insurance quotes and determining coverages, all the fun things that few like to do! Unfortunately, all the quotes are substantially higher than before, which makes this situation even more stressful. 📨 While I was analyzing the various insurance broker quotes, I received a new email alert: the insurance company that would not renew my policy sent me an upsell offer for umbrella coverage!!! As I laughed about the lunacy of this situation I immediately started thinking about the data gaps this insurer must have: ✅ Lack of solid Data Governance to understand key customer attributes (such as policy status!) ✅ Likely no client Master Data Management to ensure they have a unified customer record. ✅ Probably in dire need of a Customer Data Platform to have a 360 view of the customer, which would include integration with policy issuance systems. ✅ In need of clear Customer Journey maps to better orchestrate the very experience they mishandled completely! The moral of the story: the work we do in the data space may feel abstract but it impacts customers in a myriad of ways, both good and bad! Every data project has the opportunity to improve someone's experience if done correctly 😉 #data #customer #ai #innovation #strategy #insurance

In the fast-paced world of insurance technology, data quality is paramount. As a Business Analyst with experience in DuckCreek, I've seen firsthand how high-quality data and robust data governance can make a significant difference. Let's dive into why this is so important and how it impacts business outcomes. What is Data Quality? Data quality refers to the condition of data based on factors such as accuracy, completeness, reliability, and relevance. In the context of insurance systems, high-quality data is essential for making informed decisions, processing claims efficiently, and providing excellent customer service. The Role of Data Governance Data governance is the framework that ensures data is managed consistently and used properly. It involves policies, procedures, and standards to maintain data quality across the organization. In DuckCreek, effective data governance helps ensure that the data flowing through the system is reliable and secure. Impact on Business Outcomes 1. Improved Decision Making   - With accurate and complete data, insurance companies can make better decisions. This includes everything from underwriting to risk assessment, ensuring policies are priced correctly and customers are adequately covered. 2. Enhanced Customer Experience   - High-quality data allows for smoother and faster claims processing. Customers experience fewer delays and issues, leading to higher satisfaction and loyalty. 3. Regulatory Compliance   - The insurance industry is heavily regulated. Robust data governance helps ensure that all data handling complies with legal standards, reducing the risk of fines and legal issues. 4. Operational Efficiency   - Reliable data reduces the need for manual corrections and rework. This streamlines operations and frees up resources to focus on more strategic tasks. 5. Risk Management   - Accurate data helps in identifying and managing risks effectively. It allows insurers to predict potential issues and mitigate them before they become major problems. DuckCreek and Data Quality DuckCreek provides comprehensive tools to manage data quality and governance. Its platform supports data validation, cleansing, and integration, ensuring that data remains accurate and up-to-date throughout its lifecycle. Conclusion In conclusion, data quality and governance are critical components of successful insurance operations. By leveraging DuckCreek's robust capabilities, insurance companies can enhance their decision-making, improve customer satisfaction, ensure compliance, and achieve greater operational efficiency. Investing in data quality is not just about technology—it's about driving better business outcomes. #InsuranceTech #DataGovernance #DuckCreek #InsuranceIndustry #BusinessAnalysis #CustomerExperience #RegulatoryCompliance #OperationalEfficiency #RiskManagement #DataManagement #InsuranceSolutions #BusinessAnalyst #Agile #JIRA #API #JobOpportunity #Career #opentowork #hiring #Analystjob

Last week, we asked you, "Which aspect of data governance is most critical to your organization?" and we truly appreciate everyone who took the time to cast their votes. Today, let's unveil the poll results: - Data security and observability: 44% - Data quality management: 29% - Enterprise data cataloging: 15% - Master data management: 12% But guess what? You're not alone in facing these challenges. Many of our clients are grappling with similar Data Governance and Quality issues. For instance, consider the transformative journey of a leading property and casualty (P&C) insurance provider. Challenged by data silos and redundancy, they encountered the daunting task of lacking a single source of truth for their vital enterprise data. Learn how Eviden provided a comprehensive data governance solution, ushering in unmatched visibility and security. Explore the full client story to witness: 🔍 The intricate data-centric conundrum they faced 🛡️ Solutions implemented to fortify data trust and compliance 📊 Tangible outcomes, including accelerated time to market and a remarkable 30% reduction in data duplication. Discover the power of unified data governance and elevate your organization's data strategy. Read the full story here https://lnkd.in/d5mHw5Dx #DataGovernance #InsuranceIndustry #DigitalTransformation

Last week, we asked you, "Which aspect of data governance is most critical to your organization?" and we truly appreciate everyone who took the time to cast their votes. Today, let's unveil the poll results: - Data security and observability: 44% - Data quality management: 29% - Enterprise data cataloging: 15% - Master data management: 12% But guess what? You're not alone in facing these challenges. Many of our clients are grappling with similar Data Governance and Quality issues. For instance, consider the transformative journey of a leading property and casualty (P&C) insurance provider. Challenged by data silos and redundancy, they encountered the daunting task of lacking a single source of truth for their vital enterprise data. Learn how Eviden provided a comprehensive data governance solution, ushering in unmatched visibility and security. Explore the full client story to witness: 🔍 The intricate data-centric conundrum they faced 🛡️ Solutions implemented to fortify data trust and compliance 📊 Tangible outcomes, including accelerated time to market and a remarkable 30% reduction in data duplication. Discover the power of unified data governance and elevate your organization's data strategy. Read the full story here https://lnkd.in/ei9TF4N5 #DataGovernance #InsuranceIndustry #DigitalTransformation

Last week, we asked you, "Which aspect of data governance is most critical to your organization?" and we truly appreciate everyone who took the time to cast their votes. Today, let's unveil the poll results: - Data security and observability: 44% - Data quality management: 29% - Enterprise data cataloging: 15% - Master data management: 12% But guess what? You're not alone in facing these challenges. Many of our clients are grappling with similar Data Governance and Quality issues. For instance, consider the transformative journey of a leading property and casualty (P&C) insurance provider. Challenged by data silos and redundancy, they encountered the daunting task of lacking a single source of truth for their vital enterprise data. Learn how Eviden provided a comprehensive data governance solution, ushering in unmatched visibility and security. Explore the full client story to witness: 🔍 The intricate data-centric conundrum they faced 🛡️ Solutions implemented to fortify data trust and compliance 📊 Tangible outcomes, including accelerated time to market and a remarkable 30% reduction in data duplication. Discover the power of unified data governance and elevate your organization's data strategy. Read the full story here https://lnkd.in/gGvYzjdy #DataGovernance #InsuranceIndustry #DigitalTransformation

Last week, we asked you, "Which aspect of data governance is most critical to your organization?" and we truly appreciate everyone who took the time to cast their votes. Today, let's unveil the poll results: - Data security and observability: 44% - Data quality management: 29% - Enterprise data cataloging: 15% - Master data management: 12% But guess what? You're not alone in facing these challenges. Many of our clients are grappling with similar Data Governance and Quality issues. For instance, consider the transformative journey of a leading property and casualty (P&C) insurance provider. Challenged by data silos and redundancy, they encountered the daunting task of lacking a single source of truth for their vital enterprise data. Learn how Eviden provided a comprehensive data governance solution, ushering in unmatched visibility and security. Explore the full client story to witness: 🔍 The intricate data-centric conundrum they faced 🛡️ Solutions implemented to fortify data trust and compliance 📊 Tangible outcomes, including accelerated time to market and a remarkable 30% reduction in data duplication. Discover the power of unified data governance and elevate your organization's data strategy. Read the full story here https://lnkd.in/dVVA9MCG #DataGovernance #InsuranceIndustry #DigitalTransformation

🔥 News alert! Symfa presents its Insurance Data Platform at the Global Innovation Awards! Presented by the International Insurance Society and Insurance Thought Leadership, these prestigious awards honor insurers and #insurtechs that leverage the tech to drive positive change. We are thrilled to showcases the result of our two years’ work to the global insurance community – our ETL Data Mapping and Reconciliation Platform that: 💪 processes up to 10TB on a daily basis, 📝 handles data from 350+ agents and 120 more are coming, ❗ simplifies the reconciling of data collected over a 28-year span, 📈 has 220 mln records in the database and that’s not the limit. The heroes behind the success story – Andrey Zhilitskiy, Alesia Sumkina, Ivan Sokolov, Maria Likulina, Eduard Shayakberov, Margarita Astreika and 18 more ETL and BI developers, QA engineers and BAs at Symfa. Your dedication and contribution have been truly outstanding! https://lnkd.in/ecDWuDrX #GlobalInnovationAwards #Insurance