Machine Learning and the opportunity for making better decisions, faster

17 October 2016

By Michel Abbink

This article originally appeared on the world in beta blog

Financial Services organisations make predictions all the time, in all parts of their business. Guessing what the future might hold and aligning decisions with these estimates, is implicitly what most organisations do.

Organisations often take ad-hoc approaches to such predictions, leaving experts and professionals free to apply their judgement. Research has shown that this can lead to a hidden cost of inconsistent and sometimes suboptimal decision making.

As a first step, organisations should become more aware of the important role predictions play and recognise that different categories that exist:

(1) Many important events are so unique that predicting the past provides little guidance on how to deal with them. ‘Superforecasting', an approach to breaking down problems into elements that can be forecasted with greater accuracy, can help insurers prepare for and deal with new and unique events.   

(2) Other prediction cases have past data but paradigms are shifting. The recent pension reform in the UK left many insurers grappling to predict client behaviour despite large amounts of pension data collected under the old regime. In these cases we have seen insurance companies apply simulation approaches.

(3) There are also situations where there is plenty of historical data and the prediction target paradigm is stable. In these situations there are a lot of helpful insights hidden in data that can be uncovered by machine learning.

Machine learning is applicable to a surprising number of day-to-day operational decisions in all business, ranging from logistical challenges in supply and demand, to decisions on next 'best actions' in customer interactions.

The benefits of machine learning for everyone

Digitally native companies have perfected the art of using data to improve customer interactions and experiences. So far, the type of powerful machine learning capability developed by these on-line giants has not been easily accessible to most companies. 

This is about to change. The recent emergence of a new generation of machine learning platforms is set to make machine learning far more accessible to businesses of all types and sizes. 

A platform that we work with, contains a library of the strongest machine learning algorithms and has automated a lot of the specialised data preparation and technology work needed to run and deploy the algorithms in business as usual. 

This has helped our clients to prove the business benefits of machine learning in a time frame and at a fraction of the cost.

Human involvement in the collection and engineering of relevant data remains crucial to machine learning success, but this platform allows data scientists to engage better with the business by freeing up their time.

Better decisions in insurance

Insurance is a good example of an industry that relies heavily on expert judgments to assess and manage the risks, employing insurers, underwriters, claim handlers and actuaries. This has worked well, but there is no doubt that there is significant scope to instil more data-driven decisions across the insurance value chain.

Recently, my team assisted the customer service department of an insurance company with increasing cancellations in a profitable product line.  The anti-churn campaigns had been based on dealing with cancelling customers in a reactive ad-hoc way. By collecting historic data and using machine learning to make predictions about the ‘likelihood of’ and ‘reason for’ cancellation for each customer, we were able to help intercept before a customer cancelled, and improve retention.

Even in a mature data-rich industry there is still significant potential to improve the quality and speed of decision making. Increased prediction awareness and the emergence of more accessible machine learning technology will make the difference.

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