Blog Industry: Insurance
Building Dynamic Data Pipelines with Snowpark: Our Framework to Drive Modern Data Transformation
Learn about the challenges of traditional data transformation methods and how a dynamic approach using metadata configuration can help address these issues. By defining transformation rules and specifications, enterprises can create flexible pipelines that adapt to their evolving data processing needs, ultimately accelerating the process of extracting insights from data.
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How to Simplify Data Profiling and Management with Snowpark and Streamlit
Learn why data quality is one of the most overlooked aspects of data management. While all models need good quality data to generate useful insights and patterns, data quality is especially important. In this blog, we explore how data profiling can help you understand your data quality. Discover how Tiger Analytics leverages Snowpark and Streamlit to simplify data profiling and management.
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Tiger’s Snowpark-Based Framework for Snowflake: Illuminating the Path to Efficient Data Ingestion
In the era of AI and machine learning, efficient data ingestion is crucial for organizations to harness the full potential of their data assets. Tiger’s Snowpark-based framework addresses the limitations of Snowflake’s native data ingestion methods, offering a highly customizable and metadata-driven approach that ensures data quality, observability, and seamless transformation.
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Migrating from Legacy Systems to Snowflake: Simplifying Excel Data Migration with Snowpark Python
Discover how Snowpark Python streamlines the process of migrating complex Excel data to Snowflake, eliminating the need for external ETL tools and ensuring data accuracy.
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AI-Powered Insurance Wins: Unlocking Process Efficiencies with NLP and Generative AI
Explore the synergy of Natural Language Processing (NLP) and Generative AI in the insurance sector. Discover how these technologies accelerate Pricing and Underwriting, simplify Claims Processing, improve Contact Center Operations, and strengthen Marketing and Distribution, initiating a digital transformation journey.
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How Insurance companies are using NLP to streamline application approval
Insurance companies are using Natural Language Processing (NLP) to speed up the approval of applications. NLP helps to pull out important details from text, making it easier to decide on approvals. By adding AI to their current systems, companies have seen faster renewals, showing that NLP can help make the approval process smoother and quicker.
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Rethinking Insurance Dynamics in a Changing World
The insurance industry grapples with disruptive forces – Insurtech, climate change, and the COVID pandemic necessitate digitalization and dynamic underwriting. Loss prevention now drives innovation, redefining insurers as proactive partners. The future hinges on a data-driven approach, driving industry evolution beyond financial protection.
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Revolutionizing SMB Insurance with AI-led Underwriting Data Prefill Solutions
US SMBs often struggle with complex and time-consuming insurance processes, leading to underinsurance. Tiger Analytics’ AWS-powered prefill solution offers a customizable, accurate, and cost-saving approach. With 95% data accuracy, a 90% fill rate, and potential $10M annual savings, insurers can streamline underwriting, boost risk assessment, and gain a competitive edge.
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Electronic Health Records: Improving the Disability Insurance Value Chain
Know how electronic health records (EHRs) are optimizing the disability insurance value chain by helping improve claims processing, risk assessment, and customer service. Discover the advantages of utilizing EHRs for accurate data analysis, quicker decision-making, and enhanced policyholder experiences.
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Building Data Engineering Solutions: A Step-by-Step Guide with AWS
In this article, delve into the intricacies of an AWS-based Analytics pipeline. Learn to apply this design thinking to tackle similar challenges you might encounter and in order to streamline data workflows.
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