Software - The Rise of the Machine & the impact for actuarial modelling


Life in the Insurance sector is going to change in the near future as a result of scientific advancements in the areas of machine learning and predictive analytics. It has been forecast that the bulk of manual actuarial modelling will be replaced by machines. Martina King, CEO of Adaptive Behavioural Analytics company Featurespace, takes a look at the impact these changes will have for actuaries, and the steps they can take to stay ahead of the curve.

 By Martina King CEO, Featurespace
 A shift is occurring in the volume of manual actuarial work which can be replaced by pioneering machine learning and predictive analytics methods, capable of cutting-edge pattern recognition and anomaly spotting. These changes are already having an impact on how insurance companies detect and block application fraud in real time, spot quote manipulation and reduce claims fraud. 
  
 Some actuaries reading this article will already be aware of this emerging trend and taking steps to retrain or gain additional skills in machine learning. Others will say that it will be impossible for machines to do their work. As always, the true answer is going to lie somewhere between the two extremes.
  
 An assumption-less approach to data
 Modern fraud attacks are evolving at a more rapid pace than humans can imagine or write the rules needed to block it. Fraudsters are constantly looking to expose the vulnerabilities in insurance organisations’ lines of defence, whether it is organised ‘ghost broking’ rings making fraudulent applications or individual customers attempting to manipulate quotes or commit claims fraud.
  
 Recent technological advancements in modelling single entity level data from complex, multiple data sources has enabled a new assumption-less, bottom-up approach to statistics. This is a revolutionary mathematical shift, which is enabling earlier, more accurate fraud detection – spotting and blocking fraud attacks as they occur by understanding each individual customer’s behaviour in real-time. Using anomaly-spotting algorithms, sophisticated systems can separate ‘good’ customer behaviour from ‘bad’ – meaning that insurance organisations can concentrate on retention strategies to keep existing customers happy and loyal, rather than spending all their money and time investigating the few fraud attacks.
  
 The development of self-learning algorithms, which automatically predict outcomes of customer interactions and produce accurate probability risk scores, mean that machine learning systems have become more reliable – and often more accurate – than rules-based risk management systems, and even than human observation and review.
  
 Reaping the rewards of new analytical methods
 Industries including insurance and wider financial services are seeing radical change from these new methodologies, which couple new statistical modelling with the latest developments in computer science.
  
 At Featurespace, we have been working alongside actuarial and analytics teams in the Insurance sector. Together, we’ve been discovering the benefits of adding a machine learning layer of Adaptive Behavioural Analytics to insurance organisations’ existing methods for understanding and predicting risk. A considerable amount of our work with the Insurance sector has involved creating systems that model the behaviour of customers at an individual and group level, creating accurate risk scores in real time. We have been able to identify and block more application fraud at early stages, detect quote manipulation and predict customer churn to increase retention. A knock-on benefit has also been to reduce the number of manual claims investigations by understanding the likelihood that an individual customer is making a genuine claim.
 
 What is the impact on Actuaries careers?
 The change in technology may be rapid, but what is certain is that in highly regulated, competitive markets such as insurance, there is a constant business driver to reduce the cost base. One of the business lines regularly reviewed is the number and cost of employees. In the Insurance sector, reducing the number of highly skilled, highly paid actuaries by replacing them with technology is attractive. It’s a potentially scary prospect for actuarial careers. However, it also presents an opportunity for those actuaries who notice the trend to gain additional skills and gain a hiring advantage over their peers.
  
 Looking at the jobs section of Actuarial Post, there are few open positions for individuals with predictive analytical skills. In other sectors too, organisations are slotting into job ads the request for experience in machine learning. It’s worth the investment in gaining these skills to get ahead.
  
 Life in the Insurance sector is changing, but the humans will still control the machines – for as long as we can predict!
  
 
  

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