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Express Cloudera POV on 2021 data trends in insurance

Cloudera

We’ve written about the changes forced on the traditionally risk-averse insurance industry by COVID-19. In 2021, with the crisis hopefully fading, insurance will have time to evaluate the changes made in 2020, assessing what worked and what didn’t, and planning a new way forward rather than reacting in real time. .

Insurance 105
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The business value of operating core insurance solutions on the cloud

IBM Big Data Hub

Although interest rates have increased at an unprecedented rate over the past year due to efforts by central banks to curb inflation, insurers are locked into low-yielding investments, and it will take several years for their investment yields to improve. Core modernization (processes and technology) is a top priority for every insurer.

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Business Management Systems for Data-Driven Businesses

Smart Data Collective

Big data has become an invaluable aspect to most modern businesses. Nevertheless, many companies have been reluctant to Harvard Business Review reports that only 30% of businesses have a data strategy. However, companies with data strategies are far more successful than those without.

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How digital turned Nationale-Nederlanden into an omnichannel company

CIO Business Intelligence

Dutch insurance and asset management company Nationale-Nederlanden, part of the NN Group, has a presence in 19 countries and serves several million retail and corporate customers. Digitization vs tradition Although the insurance sector has a traditional image, that stopped being the case years ago, says Vaquero.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

I am the Chief Practice Officer for Insurance, Healthcare, and Hi-Tech verticals at Fractal. The Insurance practice is currently engaged with several top 10 P&C insurers in the US, across the Insurance value chain through AI, Engineering, Design & Behavioural Sciences programs.

Insurance 250
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AI-First Benefits: 5 Real-World Outcomes

Cloudera

The availability and maturity of automated data collection and analysis systems is making it possible for businesses to implement AI across their entire operations to boost efficiency and agility. Such human frailties are not an issue for AI-driven systems. The more efficient you can be, the less time and money you spend on a task.

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Conversational AI use cases for enterprises

IBM Big Data Hub

The emergence of NLG has dramatically improved the quality of automated customer service tools, making interactions more pleasant for users, and reducing reliance on human agents for routine inquiries. DL models can improve over time through further training and exposure to more data.