A fully independent analysis conducted by Risk Strategies Consulting - a leader in actuarial and risk management - confirmed that Sword’s AI Care model: ✅ Saves clients $3 in medical spend for every $1 spent on Sword ✅ Reduces unnecessary procedures, surgeries, ER visits, and medical imaging ✅ Delivers savings in the first 12 months Read more here https://lnkd.in/gfuGS6w4
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Real-World Applications of Case-Based Reasoning: Success Stories and Lessons Learned 🎉 Exciting news! 🎉 We are thrilled to announce our latest blog post on the real-world applications of Case-Based Reasoning: Success Stories and Lessons Learned. This problem-solving approach leverages past experiences to solve new problems in various domains such as healthcare, finance, engineering, and law. 🌟 🔍 In this article, we explore some remarkable success stories and important lessons learned from the practical implementation of Case-Based Reasoning. From assisting in healthcare diagnosis and treatment planning to supporting investment decision-making in finance, CBR has proven its effectiveness across diverse fields. 💼 📚 Discover how Case-Based Reasoning has helped physicians make informed patient care decisions, identify fraudulent transactions in the finance industry, optimize mechanical designs, retrieve relevant legal cases, and much more. 🌐 👉 Read the full blog post here: [Real-World Applications of Case-Based Reasoning: Success Stories and Lessons Learned](https://ift.tt/Lr1Ksyq) 📈 As technology continues to advance, Case-Based Reasoning has the potential to revolutionize problem-solving in many domains. By integrating artificial intelligence, machine learning, and big data analytics, CBR can become even more powerful for solving complex real-world problems. 🌐 💡 Gain valuable insights from the success stories and lessons learned in this blog post. Researchers, practitioners, and developers can harness the full potential of Case-Based Reasoning. Let's shape the future of innovative problem-solving together! 💪 🔗 Don't miss out! Read the blog post now: [Real-World Applications of Case-Based Reasoning: Success Stories and Lessons Learned](https://ift.tt/Lr1Ksyq) #DataScience #CBR #InnovativeProblemSolving #SuccessStories #LessonsLearned #RealWorldApplications https://ift.tt/Lr1Ksyq
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"Orthogonalization" The problem statement is: You have a risk model that divides both forward risk and historical performance into systematic and idiosyncratic components. And you have decided to manage those core model factors in whatever way you want, anywhere along a spectrum from simple awareness to intraday neutralization. But you would now like to drill deeper into the idiosyncratic component of risk & performance: 1) Are there any systematic exposures buried inside idio not already captured by the factors you are using in your core risk model? 2) Can you explain some of the realized historical idiosyncratic performance as a function of aspects that are not purely company-specific? The first question runs into an ongoing debate about 'parsimony' in risk models: what set of risk factors explains systematic risk without overloading a factor model with so many (and so rapidly changing a set of) factors that it is impossible to actually manage a portfolio against? The second runs up against the deeper question of what it means for a return to be factor-driven vs. idiosyncratic. Ultimately, how to truly divide alpha and beta. The details of those debates can be settled another time. But in the context of those questions, “orthogonalization" (or "residualization") exists to allow yet another layer of decomposition more granular than factor vs idio, this time to decompose the idiosyncratic component itself. Why would you want to do this? A few reasons: For discretionary investors, the reality is you are intimately aware of many more systematic drivers of your stocks than can be captured in core style & industry factors. Orthogonalization captures exposures & performance associated with industry-specific datasets or macros you care about, nuances around consensus estimate revisions, varying momentum windows, options signals, and of course, crowding - either globally or sector-relative. In principle, each of those items could of course be integrated into a core risk factor set, explicitly limiting exposures & pro forma vol against each of those more nuanced, custom factors. But in practice, most firms that care intensely about these topics perform this separation of factor-types using orthogonalization: A relatively parsimonious set of core style & industry risk factors against which books must be managed. And a more extensive set of custom orthogonal factors that are used as awareness and insight tools, but do not limit risk taking. All of which produces a proactive as opposed to reactive approach to factor risk & performance, and to understanding the drivers before they become the only thing you can focus on. The sheet walks through orthogonalization math, building from the earlier factor model to layer in custom loadings, returns, and attribution. With strong engineering & data infrastructure, the math is doable. What you do with it is a different question. Let know if you'd like the excel.
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AI is being tested for risk assessment of platelet inventory.
Deep Learning A.I. System for Platelet Management - Transfusion News
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💭 Reimagine #HNI Engagement in Wealth Management: Balancing Experience with Compliance Join our upcoming webinar to discover how you can: 🧡 Deliver exceptional HNI experiences that build trust and loyalty. 🧭 Navigate regulatory requirements with confidence. 🤖 Leverage the latest trends in technology like AI and Low-Code to streamline onboarding, lending, contracts, and reporting. Gain insights into current trends and challenges shaping the wealth management industry from industry experts from Synpulse and Newgen Software! 👉 Register here: https://lnkd.in/gTEq2J_j #HNI #WealthManagement #Webinar #FinancialServices #Technology #AI #Compliance #newgen #synpulse XIAO WU Swaminathan Ganesan David Wager
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VP, Business Development at HSB | People Leader | Distribution Expert | Insurance Enthusiast | Girl Dad
💻In the first of a series of four articles, Patrick Greene, VP of Data at Munich Re Automation Solutions, discusses the common challenges that face insurance underwriters in the AI space. https://ow.ly/fCUy30sEP3S This section, on challenges with data resonated with what I’ve seen the past several years. “The main challenge in relation to data is silos. For the underwriter at application phase, we have point-of-sale systems, auto-underwriting systems, policy management systems and claims systems. We do not have systems that manage the whole lifecycle of the policy so typically all these systems have siloed data. They are using different IDs, different tokens and different data formats and it’s often not easy to access the data. This means underwriters are not able to view the entire lifecycle data. This is a huge challenge for organizations as a business process, but it also provides challenges in being able to train those data models for predictive purposes.” #Underwriting #ArtificalIntelligence #Insurtech #LifeInsurance
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#YDC #DataEconomics Center ingests hundreds of thousands of rows of data from external data sources to answer “market-oriented data valuation” questions such as: -What is the value of MRI and ECG records? -What is the value of an oncology Electronic Medical Record (EMR)? -What is the min, max, median fine for Anti-Money Laundering (AML)? Learn more at https://lnkd.in/gHPPtaj #DataManagement #DataValue #DataValuation #DataEconomics #Data #Analytics #CDO
YDC Data Economics Center
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This year was t+1’s first at the #LifeConference - and we’ve never been more excited about the state of #actuarial modelling. The way we see it, we’re standing on the cusp of unprecedented change. And we’re not the only ones who can feel the transformation that’s coming. Most of the conversations we had could all be traced back to one, unifying theme - how can we best shape the industry we love? For many, it’s a frightening time - in a business where your KPI is often how well you can manage risk, there are a lot of novel variables to wrestle with. So the conference was a week of challenging each other, learning from each other, and helping each other create the future we want to see in #insurance We’re looking forward to continuing the exciting conversations we had with so many decision-makers at the conference this year, and we hope everyone we spoke with is just as excited for the future as we are. Just as excited to see exactly how far we can push this space forward. Because businesses are like models - they can always be better, more efficient, more accurate. And that’s what we all need to aim for.
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AI-powered processing reduces the cost of #workerscompensation claims, improving profitability. Claims administrators resolve more claims, more efficiently. Improved accuracy eliminates human error, costly audits, and fewer cases being diverted into unnecessary utilization and peer reviews. Learn how Claim Clarity can reduce your costs.
Lower Workers' Comp Claim Costs with Claim Clarity
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NOW today announced the strategic disposition of Allegient Defense for a gross consideration up to $12.5 million. This sale aligns with NowVertical’s commitment to optimizing our core business strategy and integration. Strategic Benefits for NowVertical: Focusing on Core Assets: Allegient's specialized SETA work accounted for less than 5% of its revenue from AI/ML and data solutions. This sale allows Allegient to thrive in its niche while we focus on our "One Business, One Brand" strategy. Enhanced EBITDA and Margins: Allegient's cost-plus-fixed-fee structure provided consistent but limited margins. Now, we can concentrate on scalable and optimized operations, achieving higher margins across our core business. Financial Flexibility: The $7.5 million in cash received at closing, along with additional payments, provides significant financial flexibility. The proceeds will help reduce overall debt significantly clear acquisition liabilities, and invest in business growth. Debt Reduction: This transaction clears $3.8 million of debt from our balance sheet, significantly reducing our debt liabilities. Supporting Growth: The sale enables strategic investments in our integrated business, supporting our growth plans and enhancing financial health. Following this disposition, we will continue serving government clients in North America, UK, EMEA, and LATAM markets, ensuring consistent delivery of our data and AI solutions. Read the full release at: https://lnkd.in/gK72eVPj For more detailed insights about the disposition, watch a video prepared by Sandeep Mendiratta, available at: bit.ly/NOWDealInsights For further details about NowVertical Group Inc., #NowVertical #BusinessGrowth #StrategicSale #DebtReduction #AI #DataAnalytics #PublicSector #Innovation #SustainableGrowth
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Most businesses are integrating #GenAI into their audit, compliance and #risk #framework for faster and deeper #insights. Learn how Aurex.ai, the premier partner of Beinex, leverages the advanced capabilities of GenAI in #dataanalysis, predictive modeling and pattern recognition to enhance the audit, #compliance and risk landscapes. Read more: https://bit.ly/3QUaLUN #beinex #aurex #analytics #audit
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Powerful numbers 👏🏻👊🏻