Machine Learning boosts weather predictability for insurers
Machine Learning (ML) technology is making predictions in crisis weather events more accurate, helping insurers create increasingly effective and financially sound reinsurance design, say experts.
Extreme weather and insurance risk assessment
Statistical reports show that 2020 suffered a dramatic year in terms of climate and weather induced crisis compared to previous years. Although natural disasters in Europe remained low, the US, China and the North Atlantic saw extreme weather and damage.
- A record hurricane season: More storms in the North Atlantic than ever before
- Historic wildfires in the western US
- Worldwide, natural disasters produced losses of US$ 210bn, with insured losses of $82bn
- Floods in China were responsible for the highest individual loss of $17bn, only around 2% of which was insured
- Global losses from natural disasters in 2020 amounted to $210bn
Currently, the ability to predict extreme weather events and cycles such as the El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM), are limited from six to 12 months.
The new modelling techniques, say experts, will be critical in lengthening the forecasting range as insurers rely on technology and predictive analytics to assess the crisis risk.
The current limitation has resulted in disclosure requirements “leap-frogging” the capabilities of climate science by at least a decade, say experts, who noted that this natural variability is a key element of uncertainty in climate risk modelling.
Extending the forecasting ability by an additional six months will be vital in helping insurers manage pricing, reinsurance design and budgeting and experts are hopeful technology may hold the solution.
Machine Learning in risk management insurance
Suncorp Natural Perils Senior Pricing Advisor Tatiana Potemina, explained, “Improvements so far have been incremental rather than a step change but machine learning and neural networks are increasingly being used and the results are really promising.
Potemina said the new techniques can predict climate fluctuations up to 17 months in advance, and called the new development a significant step in crisis and risk modelling.
“There are some promising results there. For insurers, it’s important to understand natural climate variability because without this understanding some incorrect assumptions can be made about the trend.”
A new scientific paper entitled Deep learning for multi-year ENSO forecasts, shows that although the current system of modelling produces strong predictions, it’s only offered lead times of up to 12 months. Long-lead forecasts would be valuable for managing policy responses.
According to reports, the new study shows that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one-and-a-half years.
Potemina said the study utilised ‘transfer learning’ to train a CNN on historical simulations and reanalysis. The research revealed the correlation skill of the CNN model was “much higher than those of current state-of-the-art dynamical forecast systems”.
She continued, “The CNN model is also better at predicting the detailed zonal distribution of sea surface temperatures, overcoming a weakness of dynamical forecast models,” the paper says.
“The CNN model is a powerful tool for both the prediction of ENSO events and for the analysis of their associated complex mechanisms,” Potemina added.
SLK Software: Optimising performance in the digital economy
Established in 2000 in Bengaluru, India, SLK Software recognises that fast-paced digital transformation is creating an unprecedentedly fertile period of opportunity for global businesses.
As such, with a firm belief in the power of simplification and automation to yield new and exciting experiences, the company has been challenging the status quo for over 20 years through an approach that is:
- Relationship oriented
- Strategically focused on a desired outcome
- Reliant on automation tech
Believing in purposeful automation
SLK’s specialisation in automation tech is full spectrum: artificial intelligence (AI) and machine learning (ML), Computer Vision, Natural Language Processing (NLP), Robotic Process Automation (RPA), and more, are all part of its core competencies.
Citing 90% productivity improvements, 30% business growth through better customer experiences, and up to 20x faster go-to-market capabilities, the reasons for its focus are clear.
The company currently serves the banking, financial services, insurance, retirement services, M&A, manufacturing, and supply chain sectors. Solutions offered include:
- Intelligent Business Transformation
- Agile IT Automation
Accelerating workflow processes
The latter is a tool specifically calibrated to enable business users an easy method for capturing document processes. This can occur across any application, with these individual tasks then seamlessly combined for both improved compliance and governance.
Carol Castelloni, VP of Transformation at CNA Insurance, highlighted this as providing critical support in helping the company meet its business objectives:
“SLK’s Avo Discover tool accelerates how we can document workflow processes, measure impacts on enhancements, and identifies future automation opportunities.” Liberated from having to focus on these process-driven aspects of business, CNA Insurance has been able to refocus its attention on creative problem-solving instead.
Ultimately, this is the most important benefit that SLK brings: it optimises the back end so that clients can channel their energy towards what matters the most, customers.
Read more about SLK Software and CNA Insurance in the June 2021 edition of FinTech Magazine.
Pictured: SLK Software team (source)