Companies are investing heavily in artificial intelligence and machine learning techniques. Harnessing the value from data available internally and externally has become a business-critical capability for insurers.
Using sophisticated methods and algorithms, machine learning uses automation to find patterns in data, not always obvious to the human eye. Data can be mined from a variety of sources to help insurers build a fuller picture of their customers and machine learning can be used in all areas of an insurer’s business from claims processing and underwriting to fraud detection.
An advantage of machine learning is that algorithms can potentially analyse huge amounts of information quickly. Solutions can be recalibrated and redeployed rapidly by automating a process without introducing human error or bias. The desire to uncover hidden patterns and discover something the rest of the market is missing is a key driver for many companies though it is easy to be seduced by the technology and the fear of not wanting to be left behind. There are pitfalls to avoid and sometimes it is all too easy to concentrate on the technology and lose sight of other perhaps more important pieces of the jigsaw.
Business Insight has been researching machine learning techniques and has developed its own AI platform that can take large volumes of records across many variables as data feeds before iteratively learning from the data, uncovering hidden patterns and forming an optimal solution. The software can take a vast number of input data points and hundreds of corresponding risk factors per case before constructing a more accurate estimate of risk. The main advantage of the neural network platform we have developed is that it can potentially offer significant improvements in predictive accuracy compared to statistical data models. There can also be significant savings in time to rebuild and redeploy by the reduction in human involvement.
Traditional statistical methods require intensive manual pre-processing of input data to identify perceived potential interactions between variables. Whereas a neural network needs minimal data preparation and interactions between variables drop out automatically which saves a considerable amount of time in model building. That said, you do need to ensure that you are not blindly seduced by the technology as there are other issues just as important when carrying out analysis of large databases.
Pearls of wisdom
Here are a few observations from what we have learned over the years that may seem blindingly obvious yet often get ignored, specifically:
1) Focus first on data quality
The validity, veracity and completeness of the underlying data you are feeding into the system is paramount. Whether internal data or external data feeds, data quality is essential. The saying ‘garbage in, garbage out’ is often true if the data you are using is of inferior quality. Hidden patterns are not ‘gems’ of knowledge but costly blind alleys if the data you are using is riddled with inaccuracies or is out of date. Quality external data is becoming more easily accessible to the insurance market and investing in the best quality data will pay dividends over the long term.
2) Ensure the relevance of your input data for what you are trying to achieve
If you are asking the system to predict a particular target outcome you should ask: Is the data you are utilising fit for purpose, is it relevant or sufficiently meaningful and is it representative relative to what you are trying to achieve?
3) Ensure you have the relevant knowledge and expertise to maximise the results
Though the technology is readily available, having people with a deep knowledge base, domain expertise and experience in this area is not something that is easily accessible in the insurance market. A deep understanding and knowledge of the market, the data and experience of why certain risk drivers happen is often under estimated.
The winners in the market will be those able to address these points focusing not on the technology in isolation but also the data, both internal and external, as well as attracting the best talent with the relevant domain knowledge and expertise to maximise value. Those that make sure they invest in the technology as well as the people and the appropriate data assets to drive their business forward, will be the winners in the years to come.