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Using Data To Illuminate The Intentionally Opaque Insurance Industry

Data Engineering Podcast

Summary The insurance industry is notoriously opaque and hard to navigate. In this episode he shares his journey of data collection and analysis and the challenges of automating an intentionally manual industry. What are the most challenging aspects of collecting that data?

Insurance 162
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Retain Customers with Faster, Friendlier Claims: 4 Strategies for Insurers

Precisely

Key Takeaways: In the insurance industry, customer satisfaction has a direct impact on your bottom line. For insurers, this decline is a serious concern, especially in an industry where customer loyalty and retention are vital to profitability. Most customers don’t think about insurance until they file a claim. Power report.

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Revolutionizing Insurance: Optimize Your Claims Process to Drive Customer Satisfaction and Retention

Precisely

Key Takeaways: Insurers provide better customer experiences with claims processes that are simple, fast, empathetic, and deliver proactive communication throughout. For most people, insurance is a safety net that remains out of mind until it becomes necessary – typically when an incident occurs and they’re filing a claim.

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Personalized Insurance: Auto and Telematics, Health, and Other Success Stories

AltexSoft

In today’s society, insurers can no longer ignore the mounting expectations of customers. Clients now expect insurers to provide different levels of personalization that are fast, adaptable, and up to date. Is personalized insurance really the future of insurance? What is personalized insurance, and why is it important?

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Machine Learning in Insurance: Applications, Use Cases, and Projects

ProjectPro

Ever wondered how insurance companies successfully implement machine learning to expand their businesses? Despite its long history of resistance to innovation, the insurance sector is currently experiencing a digital revolution. For both applicants and insurers, this quick move has significant implications.

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Data Collection for Machine Learning: Steps, Methods, and Best Practices

AltexSoft

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

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Biases in Data Collection: Types and How to Avoid the Same

U-Next

Our data wouldn’t be biased if we had complete knowledge of every entity in it (such as customers, insurance claims, and software sessions) and if we could save data on every imaginable entity. The source material is not the only way bias can enter data. Outliers can severely distort data.