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.

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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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This presentation provides a brief insight into the need to undertake an analytics project, particularly as it pertains to claims management and fraud. To this end the presentation will touch on the general challenges confronting the property and casualty insurance industry, as well as the challenges and lessons learnt from early adopters of business intelligence. In the face of these challenges analytics holds the potential to generate substantial value as evidenced by several short case study examples. The presentation concludes with a look at the issue of fraud as it pertains to the industry and some of the metrics that are influenced by it. The presentation draws extensively, and focuses on, the work and viewpoints from industry participants including; Accenture, IBM, Ernst & Young, Strategy Meets Action, Ordnance Survey, Gartner, Insurance Institute of America, American Institute for Chartered Property Casualty Underwriters, International Risk Management Institute and John Standish Consulting. References are included on each slide as well as on the “References” slides at the end of the presentation.

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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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Chapter 1 HW.docx Insurance Operations Chapter 1 Questions Name ______________________________ Please remember that you must do your own work. Any plagiarism will result in a grade of zero for all students involved. Please use your own words even if you are using the textbook for answers. Always provide a citation when a reference is used. 1. Explain how stock and mutual insurers differ. 2. Explain what it means when an insurer is licensed in a state. 3. Explain how compliance with legal requirements helps an insurer meet its goals. 4. Explain why the goal of meeting customers’ needs can often conflict with the profit goal. 5. Explain why diversifying risk has become an emerging goal for property-casualty insurers. 6. Describe the advantages (representing fewer constraints) that large insurers have over small insurers in meeting their goals. 7. Explain how the external constraint of required regulatory approvals prevents insurers from meeting their profit and customer need goals. 8. Identify the indicators that insurers can use to gauge their success in meeting customers’ needs. 9. Explain the difference between regulators and financial rating agencies. 10. Identify three core functions that exist within the structure of a typical insurer. Chapter 10 HW.docx Insurance Operations- Chapter 10 Name _____________________ Please remember that you must do your own work. Any plagiarism will result in a grade of zero for all students involved. Please use your own words even if you are using the textbook for answers. Always provide a citation when a reference is used. 1. What key elements do insurer mission statements often include? 2. Identify the three basic components of strategy formulation. 3. Describe the factors in the general environment that managers should consider as part of an environmental assessment. 4. List the five forces that drive competition within an industry. 5. Identify areas within the company that managers would examine to identify organizational strengths. 6. Describe the levels at which strategy is formulated within an organization. 7. Compare forward and backward integration in a vertical integration strategy. 8. Describe the three key areas for an insurer to evaluate in making a strategic decision about expansion into a global market. 9. And 10. XYZ Insurance Company is a personal lines insurer with headquarters in Chicago and regional offices throughout the Midwest and Northeast. XYZ’s reputation as a strong, reliable organization has engendered loyalty among its employees, many of whom have worked for the company for ten years or more. However, because of heavy competition in the types of insurance it writes, XYZ has lost considerable market share in most of its territories and has experienced steadily decreasing profits. XYZ believes that it is losing market share because it has not kept pace with the needs of its existing customers or the buying preferences of prospective customers. Ad ...

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Predictive Modeling for Life, Health and Property & Casualty Actuaries