Predictive Modeling for Life, Health and Property & Casualty Actuaries

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This predictive analytics presentation will provide eight examples of strong industry presentations and articles where actuaries (actuary) can apply predictive modeling to life insurance, property and casualty insurance and health insurance / actuarial data.

Negócios

Predictive Modeling for Actuaries By Claude Penland, Associate of the Casualty Actuarial Society www.PredNews.com

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Keeping top-performing physicians happy is the key to a hospital's success. They enhance the reputation of your hospital and attract more patients. With physician engagement at an all-time low, hospitals need to find a solution—and fast. Discover how an insight community can help you build better relationships with your physicians and drive better business decisions: https://www.visioncritical.com/solutions/health-and-pharmaceuticals/

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It’s estimated that 80% of healthcare data is unstructured, which makes it challenging to do any sort of analytics to drive improvements in population health, patient care and operational efficiency. Machine learning techniques can be utilized to predict future events from similar past events, anticipate resource capacity issues and proactively identify bottlenecks and patient outcome risks. This session will provide an overview of how process data mining can be applied to healthcare and provide real-world examples of process data mining in action.

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With data analysis showing up in domains as varied as baseball, evidence-based medicine, predicting recidivism and child support lapses, judging wine quality, credit scoring, supermarket scanner data analysis, and “genius” recommendation engines, “business analytics” is part of the zeitgeist. This is a good moment for actuaries to remember that their discipline is arguably the first – and a quarter of a millennium old – example of business analytics at work. Today, the widespread availability of sophisticated open-source statistical computing and data visualization environments provides the actuarial profession with an unprecedented opportunity to deepen its expertise as well as broaden its horizons, living up to its potential as a profession of creative and flexible data scientists. This session will include an overview of the R statistical computing environment as well as a sequence of brief case studies of actuarial analyses in R. Case studies will include examples from loss distribution analysis, ratemaking, loss reserving, and predictive modeling.

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Artificial intelligence (AI) currently being used by insurance companies has failed to remove gender bias from the profession’s claims, underwriting and marketing processes. A Chartered Insurance Institute (CII) report tells insurers they must tackle these gender biases. The report found that the datasets used to train the algorithms which support AI systems are rooted in outdated gender concepts. Algorithms learn by being trained on historic data but the report notes more and more of that data is now unstructured, coming from text, audio, video and sensors. Yet the report warns embedded in that historic data are decisions based upon historic biases, particularly around gender. The report concluded insurance firms need to prepare a structured response to this issue, starting with visible leadership on tackling gender bias in AI.

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