Product Focus – DNA Dimensions – Uncovering the DNA of every street

DNA Dimensions is the latest in our suite of Risk Insight© products.  It has been designed to provide insurers with a Detailed Neighbourhood Analysis (DNA) across a range of demographic themes. This delivers a deep insight at a level of granularity to improve pricing models and risk selection capability.

DNA Dimensions is a set of orthogonal or uncorrelated risk scores explaining the variation across the vast range of demographic data sources held by Business Insight, including the latest Census information, geodemographic, environmental data and spatial data. DNA Dimensions provides a unique set of scores across a range of themes for every postcode in the UK. Candidly, this can be fed directly into insurer pricing models to explain more variation in the pattern of risk and improve the accuracy of risk pricing.

DNA Dimensions utilises a statistical analysis technique called ‘principal component analysis’ and has been applied to the full range of demographic data assets within Business Insight to uncover the underlying dimensions present down every street.  The range of themes output in the solution are essential to understanding risk such as wealth, affluence, family composition, rurality and industry. These explanatory risk themes also give a detailed insight as well as increasing the understanding of each geographic location.  Every neighbourhood of the UK has been analysed and has been given a different set of scores that uniquely describes each location across the range of factors in the DNA Dimensions product, this helps to understand:

  • The make-up of the local area
  • Affluence
  • Property turnover
  • Levels of urbanisation/ rurality
  • Housing type
  • Life stage
  • Occupation
  • Employment

The scores can be easily included in risk pricing and rating models to increase accuracy and to fill gaps where insurers have little or no experience data.  Our initial tests against experience data have shown DNA Dimensions to add considerable value to risk pricing models, indicate potential to help drive better risk selection and enhance underwriting performance.

Business Insight is focused on providing the insurance industry with innovative products that add value and drive business growth. Business Insight invests a significant amount in Research and Development every year and our expertise in statistics, big data processing as well as knowledge of insurance has ensured DNA Dimensions is relevant, precise and effective as an external data feed.

If you would like to find out more please get in touch via your Account Manager or contact our support team on 01926 421408.

 

AI and machine learning: things to consider

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.

Neural Networks
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.

 

Product Update – Data Dimensions

Data Dimensions’ is the latest data product offering recently launched by Business Insight.

Using principal component analysis across a vast database of demographic variables, ‘Data Dimensions’ is a suite of orthogonal or uncorrelated scores by postcode describing different demographic features such as wealth, affluence, family composition, rurality and industry.

Every neighbourhood in the UK has a different set of scores that uniquely describes each location across the range of factors in the ‘Data Dimensions’ product. The scores can be easily included in risk pricing and rating models to increase accuracy and to fill gaps where insurers have little or no experience data. Our initial tests against experience data for both motor and household have shown ‘Data Dimensions’ to add considerable value for risk selection, underwriting and pricing.

For more details or to see a demonstration, contact the sales team on 01926 421408

Using big data in the UK to classify residential neighbourhoods

resonateBig Data analytics is having an impact in many areas of industry. In the recent race for the US Presidency, it played a key part in Trump’s success. While the media in the US was consistently predicting a Clinton victory, behind the scenes it is reported the Trump campaign were employing an army of Data Scientists to crunch huge amounts of social media data using Artificial Intelligence (AI) (machine learning techniques) to work out who the marginal voters were. Looking at what people were doing, saying and communicating, they homed in on what key issues were most important for particular individuals, classifying them and then working out what messages they needed to target them with. Whether or not this was decisive is unclear, though it certainly will have had some influence on the numbers and the Trump victory.

Modern analytics allows us to combine large amounts of data from lots of different sources and use machine learning and AI techniques to convert this vast amount of data into meaningful insights to base informed decisions upon. Business Insight has built its own AI machine learning platform called ‘PERSPECTIVE’ to crunch huge amounts of data to produce estimates of risk or likely outcomes. Big data analytics in the insurance industry has a number of benefits for insurers; one of which is helping to understand customers better, others include helping to improve pricing, rating and underwriting through a greater understanding of risk.  It can also tease out hidden patterns and provide insights that may otherwise stay hidden.

The amount of data may have increased enormously in recent times though making sense of large volumes of demographic data and pigeonholing people is not something new.  A geodemographic classification assigns geographic areas to categories based on the similarities across a vast range of different variables. It is a structured way of making sense of complex and very large socio-economic datasets.

Streets and neighbourhoods can be classified into types such as ‘Affluent Achievers’ or ‘Comfortable Greys’ in wealthy areas through to types such as ‘Breadline & Benefits’ in more deprived areas. The products are based on the assumption that people living in similar housing and sharing similar characteristics across a range of factors relating to age, affluence, family composition and life stage are likely to have similar wants, needs and exhibit similar behaviour.

Geodemographic classifications have been around and used in industry for a very long time. The origins in the UK can be traced back to Charles Booth who analysed the 1891 UK Census and produced a classification of streets throughout London with neighbourhood types such as ‘Lowest Class. Vicious, Semi Criminal’ – not labels that would be palatable nowadays, even if accurate. With the arrival of the computer and as access to large amounts of data increased, we saw the emergence of commercial classifications in the 1970s with PRISM developed by Claritas in the USA and ACORN (A Classification of Residential Neighbourhoods) developed by CACI in the UK. The first versions developed in the 1970s were built from Census data and used by marketing departments to target product offerings more accurately. Since then the complexity, amount of different data sources used and the level of granularity has increased dramatically, as has the number of different commercial uses that the products are used for from advertising and target marketing through to analysing crime patterns and health resource requirements.

There are many different ‘lifestyle’ or ‘neighbourhood’ classifications though most are general purpose, i.e. they have been built with no specific industry or use in mind so can be useful across a range of industry sectors and for a range of purposes. In contrast, the ‘Resonate’ classification developed by Business Insight has been built with the insurance industry in mind. So for underwriting and pricing insurance, Resonate will offer more discrimination than the general purpose systems available in the market. With the ever increasing volumes of data and available computing power, risk models and lifestyle classifications are becoming more focused and more accurate.

With the ever increasing volumes of data and available computing power, risk models and lifestyle classifications are becoming more focused and more accurate.  Clever use of data and analytics can give companies a competitive edge, help to ensure you are not selected against and as we have seen, sometimes, lead to surprising results!

Click here for more details on Resonate or contact us at 01926 421408.

Leveraging the value of Big Data

big data square.jpgIt is claimed that 90% of all data in the world has been created in the last two years. Companies are switching on to the strategic and commercial value of harnessing this data.  A recent IBM survey (October 2015) found that 74% of insurance companies report that using big data and analytics is creating a competitive advantage for their organisations.

One way to maintain that competitive edge is by leveraging and maximising the use of big data. With much better access to data from a wide variety of sources, Insurers are able to gain new insights now into risk at a highly granular level.  Data can be used to spot and analyse trends, uncover new patterns and anomalies and identify, measure and manage risk exposure. This information can then be used by Insurers to gain a comprehensive understanding of markets, customers, products, distribution channels and competitors.

The Business Insight team has extensive big data experience and provides insurers with tools to address key business challenges such as business growth and benchmarking products and position in the market.  Our market leading products support quote enrichment, risk selection and claims validation such as ‘Location Matters©‘, one of our most recent solutions that combines geographic risk mapping and analytics with highly granular lifestyle, demographic and perils data to provide powerful new insights. The software combines state-of-the-art risk mapping technology with a complete set of highly granular perils models including best of breed data from Business Insight and partner suppliers from across the industry.

Please contact the team at Business Insight for a demonstration on 01926 421408.