Gavin M.

Confused about the difference between AI and Generative AI (Gen AI)? My colleague, Donald Light, Principal Analyst on Celent's Insurance team, breaks it down in his latest blog: AI (Artificial Intelligence): "Making a Decision AI" uses data science to guide decisions, like processing insurance submissions. Gen AI (Generative AI): "Summarizing and Organizing Gen AI" generates new content from existing data, aiding underwriters by summarizing and organizing information. Final decisions are still made by humans. Both types of AI can add significant value to insurance processes, from analyzing aerial imagery for hazardous conditions to summarizing submission packages. Check out his full blog to learn how both AI and Gen AI can transform your underwriting process > https://lnkd.in/eXP3sE9q #AI #Underwriting #GenAI

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Data is the critical foundation for AI, right? My typical data-to-AI hierarchy of needs: Collect > Move / store > Explore / transform (cleaning, anomaly detection) / Aggregate & Label (training data) / Learn / Optimize (ML) / Apply GenAI & Deep Learning Or, you are built on already highly robust customer centred data-fluid foundations. EIS Ltd And you're clear on the risks and costs of proprietary vs. open source, and how we continually keep a human in the loop and a human in control at all times. Either way, insurers must ensure they have enough of the right data about the process, experience or task they’re trying to improve or the problem they’re trying to solve. Making sure they have enough "usable data" (quality + permissions) vital to the particular use case, in order to realize the value. Here’s the trap - pseudo productivity or in the case of insurance often policy based data attachment. We have a problem here because most of the really valuable stuff is derived from the customer. And an increased intelligent transformed outcome could be described as more risk mitigating, usage based and adaptive insurance. Not merely machine driven decisions of communications. In recent years, we’ve managed to build AI systems that can learn from thousands, or millions, of data points to help us better understand our world, or find new solutions to difficult problems. These large-scale models have now led to systems that can understand when we talk or write, such as the natural-language processing. From digital assistants to speech-to-text programs. And so on. Other systems, trained on things like the entire works of famous artists, or every chemistry textbook in existence, have allowed us to build generative models that can create new works of art based on those styles, or new compound ideas based on the history of chemical research. As a strategist I have a fundamental problem with this though. These “foundational" models are based on our historical or current understanding of the world and they are and will compound or “average” our understanding going forward. And what I often challenge - is this idea that the best we can do with this amazing tech is produce images or at the moment really bad moving picture content. Why not tackle the really important valuable stuff? For me AI is still mostly being used as an excuse to look “innovative”. AI isn’t the problem here - often we are. It offers so much potential. Lets makes sure we treat AI as the new Quarterback (hey Lisa Wardlaw) and keep moving forward to real value creation. AI is *not* another sticking plaster to the real transformation we need. We need to tackle the enterprise design of insurers today, build them around the customer and go from there. Hear some of this and more on this podcast: https://lnkd.in/eNJwB3Fy

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Price transparency MRF callout to be aware of. In the files, you could see for inpatient services MS-DRG's and/or APR-DRG's. These are different billing methodologies to be aware of when comparing network rates by NPI. See breakdown below. Diagnosis Related Groups (DRGs) are a patient classification system that relates the type of patients a hospital treats with the costs incurred by the care. Medicare Severity-Diagnosis Related Groups (MS-DRGs) classify a Medicare patient’s hospital stay into various groups to facilitate payment. These are typically used on the commercial side as well for inpatient services.   APR-DRGs are more comprehensive and include four levels of severity of illness or risk of mortality for each case. These are used on the commercial insurance side if MS-DRG are not chosen. Similar to MS-DRGs, an APR-DRG payment is calculated using an assigned numerical weight that is multiplied by an individual fixed dollar amount. APR-DRGs, however, consider severity of illness and risk of mortality instead of only a complication or comorbidity. More than one significant diagnosis, procedures, age, discharge disposition, and sex can add to the APR’s clinical severity. There are four levels of severity of illness or risk of mortality are: minor, moderate, major or severe.   APR-DRGs and MS-DRGs are both systems of classifying hospital cases based on diagnosis and procedure, and expand upon the basic DRG list by Medicare. #pricetransparency #employeebenefits #costcontainment

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Value-add risk insights and decision support at your fingertips. Data straight to analytics. The future is here. If you want to know more, I will be at RIMS next week. #WTW #Riskanalytics

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This was a great application of analytics, as Bob quickly linked the findings from the claims analysis and the risk transfer piece on another coverage and optimized resources accordingly.

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Thrilled to be part of Intel’s mlcon3! 🚀 Honored to give a presentation at #mlcon3 on the latest advancements in #ArtificialIntelligence and #MachineLearning. It is an honor to be among the thought leaders and innovators at this hybrid event. 🤖 Diving into the transformative power of AI & ML, I explored how these technologies are reshaping our industry and enhancing human capabilities. 🌐 A big shout-out to the organizers for creating a platform where knowledge meets innovation! 🔗 Stay tuned for more updates and feel free to connect if you’re interested in collaborating or learning more about AI and ML. #AI #ML #Innovation #Technology #mlcon3 Allstate

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It was great to have Avi Gesser and Ina Ebert as guests in our Munich Re podcast “AI - Safe & sound”. Together with our moderator Alexandra Matthews, Avi and Ina were discussing the impact of AI on questions of liability and litigation. They shared very interesting insights on AI liabilities and potential impact on insurance. If you are interested in AI Governance and AI risks, listen to our new podcast episode with Avi and Ina linked below. #artificialintelligence #aigovernance #insurance

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This podcast is well worth a listen. A great discussion about gen AI in the context of the actuarial profession. Some great insights!

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Payers are seeking new risk assessment tools to improve commercial underwriting accuracy and gain a competitive edge. Find out how these tools are evolving to evaluate risk better and set competitive premium rates. Read the article here: https://optumin.co/0iqu74   #Optum #healthplans #Payers #Underwriting #Riskassessment #healthcareanalytics #actuarial #predictivepower #riskevaluation

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Do you think about the impact of emerging technology on the actuarial function at your organization? Are you interested in thoughts on how your organization might take agency? Well...check out these thoughts. Actuaries are not helpless. We have an opportunity NOW to choose how big data, AI, and process modernization impacts our profession. Matt Heaphy Matthew Wininger Romina Egan, FSA, MAAA #transformation

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Despite the challenges of cost pressures and catastrophic events, insurers are leveraging advancements in data and analytics, as well as innovative solutions, to effectively identify low-risk properties within the same ZIP code. My colleague, Nathan Golia, Senior Analyst on Celent's P&C Insurance team, dives into how these cutting-edge technologies are empowering insurers to make informed decisions and maintain their position in risk management. Learn more > https://lnkd.in/eVfMxMcr

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Join us for an enlightening session where we delve into the evolving role of pharmacy actuaries. Discover how past and present concerns are shaping the future of pharmacy forecasting and what this means for upcoming pipeline events. View the 2024 SOA Health Meeting agenda here: https://optumin.co/trs63n #SOA #Optum #healthcare #payer #pharmacyactuaries 

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This is a gamechanger for underwriters. A solution that is both user friendly and highly efficient for leveraging the invaluable information that Clareto EHRs hold. #underwriting #gamechanger #EHR #munichRe #Clareto

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Built to support various underwriting use-cases, the Automated E.H.R. Summarizer allows you to incorporate E.H.R. data directly into AUW programs.

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WTW's Advanced Analytics Survey 2024 was released today! In it, insurers told us that they want to use analytics across their business - but in reality, progress is slow. When we ask why, we consistently hear themes around technology roadblocks, as well as challenges around translating models into business value. Read more here: https://ow.ly/hapU50S0ay6 Or reach out if you'd like to explore the results of the survey in more detail!

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Verisk CEO Lee Shavel shares two big risks that insurers should be looking closely at due to the potential impact in the insurance industry. Read the full article to learn more: vrsk.co/3yp50Ih #GenAI #AI #HumanTrafficking #DataPrivacy

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