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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. Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality.

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New regulation intensifies focus on IT risk management and operational resilience

CIO Business Intelligence

As IT landscapes and software delivery processes evolve, the risk of inadvertently creating new vulnerabilities increases. These risks are particularly critical for financial services institutions, which are now under greater scrutiny with the Digital Operational Resilience Act ( DORA ).

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Insurance IT leaders herald new era for digital customer experience

CIO Business Intelligence

The insurance industry is undergoing a sea change, with IT playing a crucial role in rolling out digital customer experiences for policyholders and agents, as in-person meetings all but vanish in the post-pandemic era. IT as industry differentiator Insurers and financial services firms like MassMutual, of Springfield, Mass.,

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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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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. Now, risk management has become exponentially complicated in multiple dimensions. .

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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

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Join us on April 7th for Our Free Webinar on Decision Modeling

Decision Management Solutions

How Decision Modeling Reduces Complexity in Regulated Industries. Organizations in highly regulated industries like healthcare, finance, insurance, and energy are constantly wrestling with how to balance risk and profitability. More about the host: Charlotte DeKeyrel. Decision Modeler, Decision Management Solutions.