Machine learning and artificial intelligence
Machine learning tools are being used to assess risk, detect fraud, and predict price. With the use of machine learning on the increase, what impact can machine learning have on the future of insurance?
It is commonly thought that machine learning is just another phrase for artificial intelligence (AI), though they are closely related, they are not the same. For many, machine learning is the watershed moment in computing and is already changing the face of many industries.
But what is it? In short, machine learning uses mathematical models of data to help a computer learn without direct instruction. To learn through training and experience, rather than the traditional approach of being programmed with set rules.
Applied AI has come a long way since IBM’s Big Blue played chess with Gary Kasparov in 1995, with the champion winning 4-2. In 2017 DeepMind’s AlphaGo, developed by Google, was beating world number one player Ke Jie, at Go, acknowledged as one of the most complex strategy games in the world.
Of course, machine learning can be used for so much more than playing games and beating champions! The same techniques that predict the behaviour of an opponent to improve the performance of a player can and are applied to improve just about anything.
Whether you are running drug trials, autonomous driving or even insurance, one of the fundamentals of machine learning is the ability to interpret and evaluate risk. And we all know how important risk is in insurance.
Brokers are operating in a highly competitive market, everyone is chasing the lower-risk consumers, the more profitable policyholder. To achieve this the broker needs to take a risk on the loss ratio or find a way to take on better risks. Leveraging the existing data is critical to assess risk and drive better decision making and this is where machine learning makes a unique contribution.
Data has always played an important role in the insurance industry, and today we have more data than ever. By sending large amounts of anonymised historical data to a machine learning algorithm, the machine can pick up subtle details and trends within the data batches and can ultimately identify one from another.
Powerful insights can be produced when combining machine learning with insurance, generated from the data produced from everyday insurance premiums. One aspect of machine learning is asking the data to predict the future from the past.
Predictive modelling, assessing risk and detecting fraud
Machine learning technology has been widely adopted across the industry on various applications from website chatbots to mobile applications that assess damage at road traffic accidents.
The industry is now applying machine learning where it is making a real impact, to the policy itself. Using available data, products using machine learning algorithms are available as enrichment calls at the point of quote to assess risk, detect fraud and predict price.
To help address the problem of managing the quality of policyholders, machine learning can be used to assess risk more accurately to predict a prospect’s propensity to claim. Valuable insights are provided, reviewing and refining over 200 risk factors, to give that extra degree of confidence. This automated process delivers a more accurate assessment of risk, saving time and increasing profitability.
Fraud continues to be a problem within the insurance industry, with over £1bn worth of detected fraudulent claims in 2020*. Machine learning algorithms can be used to analyse historical data and identify policies that may result in a fraudulent claim. This helps anti-fraud teams to be more productive, selecting only the key policies to investigate and reducing the cost of fraud.
For businesses to gain a competitive edge they need to ensure that they have the best applications at their disposal and the companies who have already embraced machine learning into their toolbox are experiencing real benefits.
The use of machine learning tools within insurance is still in its infancy and is already making an impact. Using a single tool can be used to enhance risk selection and help profitability. Combining multiple tools to assess risk, detect fraud, and predict price is a necessity for the brokers of tomorrow.
Accenture: Machine Learning in Insurance Report
Machine Learning Programs (MLP)