5 minute read
Meteorological Intelligence
Jamie Banasik MSc, Founder of Metswift, delves into the details of weather analysis and the steps the insurance industry still need to take.
QPlease tell us a little about yourself and the creation of Metswift? A While studying at The University of Reading the two founders of Metswift, myself and Shaun Pammenter MSc, began evolving what would become a ground-breaking climatological and predictive understanding of weather. We found ourselves frustrated at how weather analysis was not serving industry effectively, we were expecting the industry to be able to decipher weather peril risk from reams of historical data. So we initially challenged what the Re/Insurance industry believed was possible for weather prediction, and how it could assist the sector to instantly access weather data that was accurate and interpreted in to information they could use immediately to allow them to make informed decisions. It was important to us that we gave them the tools to quantify climate change, reduce claims and determine accurate premiums. We began working with underwriters and the interest continued to grow. Here at MetSwift, we bring an acuity to weather and natcat analysis, seamlessly integrating into business processes. Our mission – utilizing our expertise – is to revolutionise weather insight accessed by industry; we have made it faster, smarter, global & hyperlocal, of higher quality and applied our advanced and award winning proprietary Artificial Intelligence.
Advertisement
QPlease can you explain the term ‘New weather’? A ‘New Weather’ is AI, advanced statistical analysis and the integration of long-term historic trends, weather and natcat insight. Global, hyperlocal, cleansed datasets
that draw on big data, followed by swift analysis powered by machine-learning that can instantly render risk. The application of artificial intelligence enables longer term and more accurate forecasting than ever before. Insurance must recognise the meteorological intelligence they need is now at their fingertips. The power is now in their hands.
QHow can the insurance industry increase resilience as extreme weather events increase? A Resilience is a difficult question as - other than short term, reactive weather information to mitigate their ongoing risk - ultimately the weather will do what it will do. However, for companies to better understand and manage their exposure, with better analysis and quantification of appropriate risk for a time and place based upon the likely or expected weather, is extremely beneficial. Specifically, breaking down the driving forces and impact of individual weather components, heavy rainfall patterns for example, can better inform insurers further in advance than ever before. Understanding that the changing climate is likely to drive weather patterns which have not been seen before, and therefore their risks have not been quantified, is a new important concept. Providing insurers with a range of outcomes or parametric options allows them to manage their risk appetite to whatever the weather throws their way in the future. Improving their portfolio and mitigating some risk.
QMetswift has recently been named a ‘gamechanger’ how are you changing the landscape in regards to weather analysis? A MetSwift is using machine/deep learning to build weather models whose predictions are based on artificial learning of temporal dependence (timeseries data), and handling of temporal structures like trends and seasonality. In doing so, these trends that may not seem obvious to domain experts and traditional algorithms due to the size, complexity, variety and uniformity, become very clear. We let the data tell the story alongside our team of meteorologists and data scientists, who understand, enrich, and pre-process the data; the results are complex, deep learning networks capable of
generating accurate relevant weather insights instantly and further in advance than ever before. We are currently predicting weather 5 years in advance and are working on 10 years.
QDo you think the insurance industry is utilising the meteorological intelligence available or do you feel traditional methods and skills are still relied upon? A The majority are not, yet! As climate-related changes, awareness and impact proliferate at speed, most are recognising the need to quantify climate risk and provide quote and premium consistency, but are unsure of how to access accurate resources. Risk directors, underwriters and brokers are asking how the existential, global and most perilous crisis of our age will impact business, and how they can navigate this – MetSwift.live can to show them. Importantly, MetSwift provide the weather and risk insight so users can apply their domain expertise and create solutions for themselves, other weather providers simply provide the data, often complex, rarely instant and not specific to the enquiry made by the user.
QWhat is Metlive and what impact will this have on underwriters? A MetSwift.live is our unique and proprietary platform that delivers instant weather insights anywhere in the world up to 5 years in advance. The results enhance the underwriter’s ability to utilise intelligent weather insights with statistical and predictive modelling, meaning that substantial losses can potentially be minimised, climate changes can be quantified and consistency of portfolio can be achieved. The simplicity and ease of use of the results on the platform allow users to take what they need with minimal digestion and can therefore make quicker, more informed decisions.
QAs unintelligible weather data becomes a thing of the past and the role of AI increases what do you predict for the future? A The world will move away from statistical information and analysis, and look towards predictive algorithms for answers. The key is to learn from the past to predict the future, not look at the past to infer it. Critically, as the change in climate accelerates in both its non-uniformity and non-linearity globally, historical data will become less and less relevant and therefore useful. Industries must move towards understanding the trends from historical data but let the algorithms turn these trends into actuals.
