The ability to access huge amounts of data from a wide variety of sources, coupled with increased use of automation and artificial intelligence, are transforming the way industries and companies work today
Martin Vinkenfluegel, Risk Consulting Regional Manager for Europa, AXA XL
The fourth industrial revolution – Industry 4.0 – is being driven in part by the use of machine learning to help make faster, smarter and better decisions based on increasingly precise data.
For risk managers in companies across the world, this brings new opportunities. And for their insurers too, there are huge advantages to being able to harness vast data sets and to analyse that data more swiftly than a human ever could.
Machine reading – which parses and reasons information – is a hugely powerful tool for property risk consultants.
Risk consultants are now using algorithms that can determine the exact meaning of a word or phrase, in context, and that are able to recognise relationships between different characteristics or facts.
This enables us to process the mountains of data about our clients’ risks in a more efficient way. And that not only empowers our underwriters to write risks more efficiently, it also enables us to give our clients greater insights into ways to manage those risks and reduce their exposures.
The risk data about a multinational client with multiple production facilities across the world is vast. Machine reading can provide us – quickly – with a summary report of all of that information, thus enabling the risk consultants to direct their expertise to the most important parts of those reports flagged by the system and focus on those risk areas that really need their attention.
The contextual analysis of this data also helps risk consultants to prioritise site visits; and this, in turn, means that we can direct the support we give clients on risk and exposure management on-the-ground to the areas that need them most.
There are other benefits of using AI in risk engineering too. AI can help us to produce more precise loss estimates. Currently most risk management and underwriting decisions are taken based upon either estimated maximum losses or normal loss expectancies.
But the greater capability for data analysis that AI provides should enable us to give more precise estimates of average annual losses. As well as giving our underwriters better information on which to base their underwriting decisions, this has the potential to allow risk managers and their CFOs to align their budgets to expected losses and also tailor their risk management and mitigation strategies even more closely.
We can never completely accurately predict the future, but using artificial intelligence to complement human expertise can help us to manage expectations.
By Martin Vinkenfluegel, Risk Consulting Regional Manager for Europa, AXA XL, a Division of AXA
This article was sponsored by