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Quanthacks
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Quanthacks

23 - 24 жов 202609:00 - 18:00Offline

Опис

QuantHacks — Code. Quantify. Innovate. QuantHacks Code. Quantify. Innovate. QuantHacks is a hackathon organized by the London Institute of Actuarial Studies (LIAS), bringing together technology, data, and actuarial science to tackle real-world problems involving risk, uncertainty, and decision-making. From cybersecurity and financial technology to education and career development, participants are challenged to build practical solutions using artificial intelligence, machine learning, statistics, probability, and predictive analytics. The goal isn't simply to build another application. It's to use quantitative thinking and technology to understand uncertainty, predict outcomes, and make better decisions. 🚀 What Can You Build? Participants can explore three core tracks: 🛡️ CybersecurityQuantify cyber risk, predict potential incidents, estimate financial losses, or build smarter risk assessment systems. 💰 FinTechBuild solutions for credit risk, financial planning, insurance pricing, loan defaults, investment decisions, or financial forecasting. 🎓 EdTechApply predictive analytics to student performance, exam preparation, dropout risk, personalized learning, and career planning. 📊 The Quantitative Edge Every project should meaningfully incorporate actuarial or quantitative principles, such as: Probability and statistical modeling Risk assessment Predictive analytics Expected value and expected loss Frequency and severity modeling Machine learning Scenario and stress testing Monte Carlo simulation Data-driven decision making You don't need to build a complicated mathematical model just for the sake of it. The objective is to show how quantitative reasoning actually improves your solution. 🏆 The Challenge Can you turn uncertainty into an advantage? Bring your ideas, your code, and your curiosity. Build something useful, back your decisions with data, and show us what happens when technology meets the mathematics of risk. QuantHacks — Code. Quantify. Innovate.

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