Climate change and windstorms

The world’s climate is changing and the frequency of storms is impacted by variability in the climate. In 2017, the ABI together with catastrophe modeller AIR Worldwide and the Met Office collaborated on research into UK windstorms.  The research considered what effect global temperature increases of 1.5, 3 and 4.5°C would have on the frequency and intensity of UK windstorms.

The research highlighted that temperature increases of just a small number of degrees could lead to a large increase in insurance losses.   These increased losses would not be spread evenly across the country but would more likely to be concentrated in Northern Ireland, northern England and the Midlands, with southern England potentially seeing decreasing losses from storms.

This is based on Met Office analysis which shows that even small increases in temperature are likely to shift stronger winds further north. The full report can be found here.

Matt Cullen, Head of Strategy at the ABI, said: “Concerns about global warming often focus on rising water levels and the threat of flooding but this new research makes it clear the impact of other meteorological events such as high winds must not be overlooked.”

Extreme weather events are difficult to predict in advance. However, it is possible through analysis of vast volumes of historical data to understand and highlight the areas that are more at risk. Investing in technology and data models that are based on accurate, up-to-date information and that take account of changing risk patterns to gain a deeper insight into risk is crucial for insurers to ensure they are not selected against or over exposed in high risk areas.

Business Insight has Storm models for both residential and commercial properties.  Based on extensive research and the largest source of storm claims information available in the UK, the Storm Insight© models consider the variation in peak wind gusting across the UK together with factors such as topography, urban density, the local built environment and the likely state of repair of buildings to predict annualised loss estimates right down to individual property level. The models have been calibrated using over 72 million windspeed recordings focussed on areas where the UK’s insured population lives and use information supplied by a market leading supplier of weather information to the UK insurance industry.

To find out more, please contact your Account Manager or contact us on 01926 421408.

Data-Driven Underwriting

The explosion of new data sources is revolutionising the insurance industry. Artificial Intelligence (AI) is enabling insurers to capture, unlock and analyse data in ways that people alone simply cannot.

However not all data is created equal – ensuring you make the best underwriting decisions means relying on robust, accurate data and using powerful tools to help you with those decisions.

The wide availability of data and sophisticated analytical tools opens up opportunities for insurers to visualise risk in new ways and create a deeper, more holistic view of risk across their entire book of business.

Designed to support underwriting decisions at the point of sale as well as help with accumulation and exposure management, Business Insight’s Location Matters© provides interactive maps displaying property location, risk, perils, policies and claims. Users can customise their view in and around a location to consider all possible risk influences, for a 360-degree view of risk.

Enabling insurers to get a granular view of a location’s particular risk, as well as the properties, hazards and geographic make-up of a specific area, allows underwriters to be more strategic when it comes to risk selection and pricing risk.  Underwriters can map their book of business at an individual address level and assess accumulations of risk by locality.  It can also be used within claims departments to assess the validity of claims.

Users are able to upload their own data and combine it with third-party information for a deeper understanding of the significance of factors that might impact their business going forward and to select more profitable risks more effectively.

Contact the sales team for more information on 01926 421408.

Drones and their use in property insurance

Unmanned Aerial Vehicles (UAV’s) or drones as they are more commonly referred to, continue to make headlines in the UK for the wrong reasons such as being used to fly contraband into prisons or narrowly missing commercial aircraft!

The Government has recently published its consultation on the safe usage of drones.  The proposed legislation, which is due to be finalised later in the year, is set to place new responsibilities on owners of drones weighing 250g or more.  The new rules will attempt to tighten up safety around airports and will include mandatory registration by the user and the requirement to sit safety awareness tests to ensure they understand UK safety, security and privacy regulations.   A proper regulatory framework for the safe use of drones brings with it opportunities for the insurance industry.  In the US, insurance is already the fourth largest market for drones and drones are set to play an increasing role in the insurance cycle in the UK.

Drones can offer a number of advantages and opportunities for property insurers including surveying risky, hard to reach areas and their ability to produce high quality imagery quickly and economically.

Areas where property insurers can exploit the use of drones include:

  1. Claims inspections

One of the most common uses for drones by insurers in the US is conducting roof inspections. Roofs are notoriously difficult and hazardous to inspect and can be dangerous, particularly if a roof has suffered damage following a fire or a storm.  Drones remove the need for a loss adjuster to go out on site and they are able to capture high-resolution images of the entire area, including parts of the structure that wouldn’t necessarily be accessible to a human.

Drones can also be used to speed up the claims process.  A drone can survey a property quickly and accurately. The footage can then be reviewed, the damage assessed, and the claim processed, thereby reducing claims settlement time and improving customer experience.

  1. Post-Event surveying

Drones could be used to inspect areas following a major event like a flood or storm.   They are already being used in America in relation to wildfires where the photos of the damage are taken and then cross-referenced with risk modelling and underwriting information.

Drones can access areas that might be dangerous for loss adjusters to enter and could be employed quicker than mobilising a team of loss adjusters.

  1. Fraud Monitoring

Drones could also be used to deter insurance fraud.  For instance, an insurer could send a drone to take photos of an accident scene. It could then use the data collected to verify details submitted by the insured in a claim.

There is no doubt there is plenty of scope for the use of drones within property insurance.   However, a regulatory framework needs to be in place first so that concerns in relation to safety and privacy can be addressed.

Verisk Analytics Acquires Business Insight

Verisk Analytics, a provider of property/casualty (P&C) insurance risk information, has acquired UK-based insurtech Business Insight.

Warwick-based Business Insight is a technology company focused on developing predictive analytical models for insurers in the UK and Ireland. The company provides risk models to support underwriting, risk selection, and rating for the commercial property, homeowners, and private and commercial motor insurance markets. Its analytic solutions help insurers benchmark market pricing, gain a deeper insight into risk, and identify geographies to support profitable growth.

Anil Vasagiri, Senior Vice President and General Manager for global property at Verisk, said: “For more than 40 years, Verisk has provided property/casualty insurers in the United States with robust data and analytics to manage risk. Business Insight’s focus on technology-based solutions for insurers, coupled with its highly sophisticated analytics, will complement Verisk’s industry-leading solutions; and its talented group of data and insurance professionals will strengthen our deep and expanding international team.”

Mark Harrison, Managing Director of Business Insight, commented: “Verisk’s vast data resources and analytical solutions for insurers present a tremendous opportunity for us. We look forward to working with our new colleagues to create more value for our existing clients as well as developing new analytics solutions for insurers around the world.”

Product Focus – Theft model rebuild

resonateABI figures show that theft from households accounts for 13% of all claims received. Although the volume of theft claims has been falling in the last decade, it is still significant and amounted to over £440 million in the UK over the last year on property related claims. Having an accurate perils rating model that can differentiate risk at a highly granular level can make a considerable difference to improving loss ratios and boosting profitability.

The Business Insight residential theft model ‘Theft Insight’ predicts the relative risk and variation of domestic burglary across the UK and is currently used across the industry by sixteen major property insurers.

Business Insight also has a commercial property theft risk model specifically for commercial property insurers.  Both models are based on extensive research into crime patterns using the latest available data and take account of the changing economic landscape of the UK. This covers a cross-section of inner cities, large towns and suburban neighbourhoods through to small towns and more rural areas.  Built from high resolution spatial and demographic data and calibrated using sophisticated mathematical techniques, the models produce estimates of risk on a street by street level across the UK.

At Business Insight, we know our products need to add value to insurance company pricing and they also need to beat insurers own in-house actuarial models for an insurance company to licence our products as external data feeds.  Consequently, we invest significantly in R&D to ensure that our products help insurers maintain a competitive advantage.

Some vendors build a peril risk model which is a static product with little or no further refinement. Once built, the predictive accuracy of a perils risk model degrades over time so the continuity of development and focus on improvement and refinement is very important.

We are currently working on rebuilding our theft models using AI techniques, refreshed data and experimenting with a new level of geography that ensures the anonymity of people residing in those locations but that is also more powerful than current postcode versions. This will provide a deeper insight into crime and theft patterns across the UK and a higher level of predictive capability.

Contact the sales team for more information on 01926 421408.

GDPR – are you ready?

Previously, we looked at the impact of the GDPR on the insurance industry in terms of consent, automatic profiling and exemptions.  In this article, we look at whether postcodes constitute ‘personal data’ and sharing data with third parties.

The GDPR defines personal data as ‘any information relating to an identifiable person’ and that includes names and location data.

The Ordnance Survey definition of a postcode unit is “an area covered by a particular postcode”.  Postcode units are unique references and identify an average of 18 addresses.  Currently, the maximum number of addresses in a postcode is 100. There are over 77,000 postcodes with only one residential address and around 336,000 postcodes with less than five residential addresses. This might be perceived to be a problem if the data attached to that postcode can be deemed to be ‘personal’ and could be used to identify a particular individual.

There has so far been no guidance issued relating to the number of properties within a postcode deemed to be the level sufficient to safeguard the anonymity of individuals residing there when using any statistics or data relating to that postcode.  Some statisticians often refer to a number as high as 30, though this number relates to something called ‘the Central Limit Theorem’ and is more to do with producing robust, reliable statistics and estimates of the mean rather than relating to privacy.

Time limits and erasure
The use of personal data should be limited to the “specific purpose” for which the processing is intended. This change is likely to impact the insurance industry which up to now has sought to hold on to personal data for as long as possible to maximise its potential use.  Clearly, there are business reasons for keeping hold of customer data but Article 17 states that data subjects are entitled to have their personal data erased or forgotten if there is no longer a legal requirement to retain the data.  It also states that the data subject has the right to request that personal data is erased without “undue delay” when the personal data is no longer necessary in relation to the purposes for which they were collected.

Sharing personal data with third parties
Insurers share data with industry bodies and platforms such as the Claims and Underwriting Exchange [CUE], the Insurance Fraud Bureau [IFB] and the Insurance Fraud Register [IFR] for the purposes of preventing fraud. The Regulation states that insurers will have to rigorously record and evidence how and why they are using and sharing data.

The ABI has been lobbying the government to pass legislation so that insurers can continue to use fraud indicator data and criminal conviction data.

With GDPR taking effect in less than 6 months, you will need to start thinking about the implications sooner rather than later to ensure you have everything in place to meet the May 2018 deadline.

The future of insurance – a brave new world

resonateTechnology is already shaping the future of insurance from autonomous vehicles and advanced driver assistance systems (ADAS), to inter-connected homes, artificial intelligence (AI) and machine learning.

One of the biggest challenges the insurance industry faces is adapting to this brave new world and maximising the opportunities that the new technology creates. Established insurers face a huge threat from agile start-ups able to better harness the new technologies. Some of the new ‘tech’ may or may not live up to the billing and some will be certain to drive rapid change. Data and analytics, what we collect and how we extract value from the data, is one area already in motion.

The big data challenge
Big data technologies and analytics are making it easier for organisations to capture large datasets but many still struggle to generate meaningful insights from the vast amount of data.  The challenge is to convert the data into meaningful information and then connect it with and across datasets in a way that enables enrichment and deep insight.

Deep risk insights
In terms of risk management, where insurers are seeing the real value is using data and analytics to gain a deeper insight which allows for better, more profitable decision making. Using artificial intelligence and machine learning, patterns and trends can be identified that would otherwise have stayed hidden.  Technologies around data have emerged to handle the exponentially growing volumes, improve velocity to support real-time analytics, and integrate a greater variety of internal and external data. Twenty years ago, many insurers couldn’t even rate a risk by postcode, particularly those distributing through brokers, due to legacy systems and IT restrictions. Now, pricing can be based on data relating to the specific individual in real time. Personalised pricing has allowed insurers to be more targeted in the selection of customers, to proactively cross and up-sell and to target opportunities in new segments or markets with confidence.

Why the hype surrounding AI?
Machine learning techniques such as neural networks have been around for a long time and were in use in the industry during the 1990’s for predictive analytics, so what’s changed?  There are three main factors; firstly, computing power has significantly improved.  According to Moore’s law, computing power effectively doubles every couple of years.  This means that algorithms are now able to crunch much more data within timescales that were previously out of reach.  Secondly, the volume, availability, and granularity of data has also radically improved.   Thirdly, the efficiency and capability of the algorithms embedded deep within neural networks have also markedly improved. These three factors combined have resulted in these types of techniques coming more into focus recently for applications within insurance.

Insurers have been using AI technologies to improve their efficiency and speed up internal operations in terms of automating processes for claims and underwriting.  AI and neural networks can also be employed to help gain a deeper insight and to differentiate risk in a much more granular way.

Business Insight has developed its own AI platform called ‘Perspective’. It is a neural network 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 and offering significant improvements in predictive accuracy compared to statistical data models.

Changing customer needs
Behavioural analytics and advanced data analysis can also help insurance companies gain a deeper understanding of their customer base for the development of personalised products and solutions.

Millennials, having grown up with smartphones and being used to digital interactions, want the ability to compare products quickly and easily and find value for money at the click of a button. They want the product best suited to them and their lifestyle and these are the things they are looking for from an insurer.  This is where data and technology will need to be harnessed effectively by insurers to create products for the next generation of customers. It is this need to adapt and evolve to match customer requirements and buying preferences that has led Aviva to recently launch their ‘Ask it Never’ initiative.  Aimed at Millennials, the idea is to eliminate the need for applicants to have to spend time answering lengthy questionnaires by pre-populating the fields using big data to streamline the application process, saving the customer time and making the service more efficient.

Agility and change need to be embraced by traditional insurers otherwise some may end up going the same way as Kodak, a market leading company that resisted change and saw its market share fall off a cliff when digital photography came along.

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.

 

Product Focus – Commercial and Residential Fire Insight Update

Fire is one of the few perils that consistently meets an insurer’s estimated maximum loss expectation.  Getting a greater understanding of the geographic variation in the risk of fire is becoming more important and something that many insurers are spending more time building into rating area files for property underwriting purposes.

There are many factors that influence the risk of fire ranging from property specific factors relating to the vulnerability of different types of building through to demographic and behavioural factors describing neighbourhoods and streets that are more prone to certain fire related incidents.

Business Insight has been researching and building geographic fire risk models for the last 8 years. Having a risk model that has been well researched and that can accurately differentiate risk across the UK can add considerable value to the accuracy of your buildings and contents rating area files.

Our residential fire model is based on extensive research into residential fires and assesses the relative risk and variation of deliberate and accidental fire claims across the UK.  Our commercial fire model assesses the risk of a fire claim by commercial business category, source & frequency. Both models utilise highly complex computer algorithms and vast quantities of data relating to residential and commercial properties, the local environment and the demographic make-up by area to estimate risk more precisely.

As part of our commitment to ensuring our models are continuously enhanced and kept up-to-date, we have recently recalibrated the residential and commercial fire models with enhanced data to provide a more granular level of detail and a more accurate assessment of risk.

Both models have been validated by a number of insurers using fire claims information and have shown a high degree of discrimination between high and low-risk areas.

Key benefits include:

  • Gaining a deeper understanding of your exposure to fire claims in the UK across your existing book of business.
  • Gaining insight into postcode areas where you have no experience data.
  • Discovering where you need to modify your rates to improve your fire loss ratio.

Contact our sales team for a demonstration 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.