In Australia's rural insurance industry, artificial intelligence (AI) and machine learning is starting to change the way insurers manage the entire customer journey – from quote to claims and everything in between. It's fair to say that this new technology may not be well-embedded in what is traditionally seen as risk-averse industry as yet. However, as it becomes more accessible to businesses of all shapes and sizes, AI has significant potential in terms of shaping the types of policies insurance companies offer, thanks to more accurate yield predictions and risk management strategies.
The role of AI and machine learning in the rural insurance industry so far
Already bots and AI are supporting many of the administrative functions of insurance companies. Australia's insurers are utilising the power of AI in offering a 24-hour service to customers, providing product advice, making appointments and even analysing claims using deep-learning platforms such as IBM's 'Watson'. AI is so capable of managing these types of tasks that recent New York insurance start-up, Lemonade, began trading with bots embedded and aims to be a completely paperless environment. They still have humans in the office, but they deal with the claims and questions that are beyond the scope of the bots.
What is the potential for AI and machine learning in agricultural insurance looking ahead?
Beyond administrative functions, where AI will really revolutionise agricultural insurance is data science and machine learning. Developments in AI mean that farmers and insurance companies stand to be able to predict weather patterns and crop yields much more quickly and accurately than ever before. IBM's technology is already using satellite and big data to assist farmer decision-making.
This could revolutionise the way insurance companies assess risk around certain crops, geographical locations or weather patterns. For farmers, it could change how they manage risk to protect and improve their yield and income.
With AI at the helm, insurers and farmers alike stand to benefit from its ability to:
- Analyse and predict patterns in output to provide farmers with key data around the best time to plant or harvest crops.
- Determine risk factors, such as pest and disease likelihood, ahead of time and as they change to enable farmers to manage the risk.
- Analyse current conditions, such as soil quality, humidity and precipitation, to inform practices such as irrigation and maximise yield.
- Use robotic lens technology to ascertain crop development, predict yield dates and control light or temperature exposure to encourage or slow ripening according to the needs of the farmer.
If insurers use this data to create accurate forecasting models, they could significantly reduce the application process.
When it comes to insurers, access to this information could not only reduce claims through comprehensive risk management at farm level, but also play a part in creating more accurate yield forecasts. This type of data would allow them to ensure their policy cover for each farm is as accurate as possible, despite inevitable seasonal differences year-on-year, and inform the types of agricultural insurance products they offer.
From the customer perspective, if insurers use this data to create accurate forecasting models, they could significantly reduce the application process and provide products that better suit demand. While examples from within the industry are limited, a local example is from ANZ and UTS, who collaborated to analyse 10 years of insurance data. They were able to reduce the questions on their application form from 32 to seven by using AI to create links between answers, customer segments and resulting claims, according to Investment Magazine.
To learn more about how we innovate to improve our policies and help our farmers speak to our team today.