Reimagining Life Insurance with Brain AI Advancements
Reimagining Life Insurance with Brain AI Advancements
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The convergence of rapid advancements in neuroscience and artificial intelligence (AI) is breaking new ground in the realm of life insurance.
These innovative technologies offer opportunities to revolutionize how insurance is developed and provided, paving the way for personalized and preventive care.
At the Online Summit in Sydney, speakers noted the exciting potential of AI and neuroscience to reshape healthcare outcomes and insurance services. Professor Matthew Kiernan, CEO of Neuroscience Research Australia, discussed how AI-supported neurological diagnostics are transforming the field, which could lead to improved treatment and management of neurological disorders.
One compelling example Kiernan gave was a UK study where blood samples from 55,000 participants were analyzed over two decades. Among them, 1,000 developed Alzheimer's disease. By running these samples through AI, researchers identified specific proteins that could serve as a diagnostic test for Alzheimer’s, a development that promises much more accessible and affordable screening.
Current Alzheimer’s diagnostic methods, like amyloid PET scans and lumbar punctures, are costly, ranging from $5,000 to $7,000. The new AI-driven blood test could reduce this to about $100, making early detection more feasible.
Kiernan emphasized the crucial role that superannuation and insurance sectors can play in supporting the initial high costs of such innovations until they become scalable and more affordable. "You’re the brilliant minds who need to devise solutions, as many groundbreaking therapies are initially exorbitant," he stated.
Advancements in AI allow for more meaningful data pattern recognition, improving predictive capabilities. Ajay Agrawal, Professor of Strategic Management at the University of Toronto’s Rotman School of Management, explained that AI, at its core, is about prediction. "It uses current information to generate data we don’t have, and it's getting exponentially better over time," he said.
Agrawal pointed out three essential predictions in insurance:
Marketing to the right customer with the appropriate products.
Predicting the risk and likelihood of claims.
Validating claims to determine legitimacy.
This evolving predictive technology holds the potential to overhaul how the insurance industry operates by reducing uncertainties and improving outcomes. For instance, personalized engagement with members can ensure communication is effective and relevant, enhancing satisfaction and proactive health management.
On the healthcare front, AI can predict patient risks, detect anomalies through imaging, and identify effective treatments. Additionally, it aids in fraud detection and claim validity assessments, acting as an enhanced statistical tool.
Agrawal encouraged insurers to "lean further into" these capabilities, advising to not only understand and underwrite risk but also manage it proactively. He suggested that with AI, insurers could begin knowing more about an individual’s risks and how to mitigate them than the individuals themselves.
He also emphasized that the insurance industry should focus on meaningful engagement rather than merely collecting data. The goal is to use AI’s learning capabilities to drive better engagement and outcomes. "Machine intelligence is unique as it learns from use, unlike any tool we’ve had before," Agrawal noted.
As cited by the original article from Investment Magazine, the potential for AI and neuroscience to drastically reshape life insurance signifies a new era where technology not only predicts and underwrites risk but actively reduces and manages it.
Published:Thursday, 8th Aug 2024 Source: Paige Estritori
Please Note: If this information affects you, seek advice from a licensed professional.
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Subrogation: An insurance carrier may reserve the "right of subrogation" in the event of a loss. This means that the company may choose to take action to recover the amount of a claim paid to a covered insured if the loss was caused by a third party.
