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Examples of IBM assisting insurance companies in implementing generative AI-based solutions  

IBM Big Data Hub

IBM can help insurance companies insert generative AI into their business processes IBM is one of a few companies globally that can bring together the range of capabilities needed to completely transform the way insurance is marketed, sold, underwritten, serviced and paid for.

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Document Processing Vs. Robotic Process Automation

AIIM

For many businesses, content and data capture tools are highly sought out, particularly in the banking and insurance sectors. With so many different types of documents required to operate and adhere to compliances, the need for capturing data accurately and quickly, especially unstructured data, is ever growing.

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Document Processing Vs. Robotic Process Automation

AIIM

For many businesses, content and data capture tools are highly sought out, particularly in the banking and insurance sectors. With so many different types of documents required to operate and adhere to compliances, the need for capturing data accurately and quickly, especially unstructured data, is ever growing.

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The most valuable AI use cases for business

IBM Big Data Hub

For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions. For example, a supply-chain function can use algorithms to predict future needs and the time products need to be shipped for timely arrival. Routine questions from staff can be quickly answered using AI.

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How generative AI delivers value to insurance companies and their customers

IBM Big Data Hub

Insurers struggle to manage profitability while trying to grow their businesses and retain clients. Large, well-established insurance companies have a reputation of being very conservative in their decision making, and they have been slow to adopt new technologies.

Insurance 113
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5 misconceptions about cloud data warehouses

IBM Big Data Hub

For example, Marriott International built a decentralized hybrid-cloud data architecture while migrating from their legacy analytics appliances, and saw a nearly 90% increase in performance.

Cloud 58
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The 7 most common data quality issues

Collibra

Incomplete or inaccurate data, security problems, hidden data – the list is endless. Several surveys reveal the extent of cost damages across many verticals due to the problems associated with data quality. Some examples of poor data quality in business include: . Hidden data. Description.