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The risks and limitations of AI in insurance

IBM Big Data Hub

In my previous post , I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. You should host the model on internal servers. Efficient and accurate AI requires fastidious data science.

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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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New regulation intensifies focus on IT risk management and operational resilience

CIO Business Intelligence

A comprehensive regulatory reach DORA addresses a broad range of ICT risks, including incident response, resilience testing, third-party risk management, and information sharing. The regulation impacts a broad spectrum of financial institutions, including banks, brokers, credit institutions, insurance companies, and payments processors.

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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. The context tests us and it’s necessary to reinvent ourselves every day.”

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What to Do When AI Fails

O'Reilly on Data

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. All predictive models are wrong at times?—just

Risk 361
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Optimizing Risk and Exposure Management – Roundtable Highlights

Cloudera

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. Pandemic “Pressure” Testing. Observe what the model has to offer even if not the intended output.

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3 areas where gen AI improves productivity — until its limits are exceeded

CIO Business Intelligence

We did side-by-side testing,” he says. AI models are able to present best practice code systems to help junior developers learn and hone their skills.” In testing, gen AI was also particularly good at generating test cases and creating dummy data for testing. It’s not about reducing headcount, he adds.

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