The Great Storm of 1987 – 30 years on

After the devastating effects of Storms Harvey, Irma and Maria on the US and Caribbean Islands, we revisit the great storm of October 1987.  Experts are already saying that Storms Harvey, Irma and Maria could end up being three of the costliest storms in modern times. AIR Worldwide has put potential insured losses for the three storms in total at an astonishing $155bn. We are lucky in the UK that we don’t get storms of this type hitting our shores. Indeed, major storms causing losses in excess of £1bn are rare events in the UK. On the 16th October 2017, it will be thirty years on from the Great Storm of 1987.

Referred to in the industry as ‘87-J’, the storm took everyone by surprise and at the time, was classed as the UK’s worst storm since 1703. It still remains one of the most severe and costliest windstorms the UK has ever experienced. One in six households made a claim at the time and losses to the industry for commercial and residential cover exceeded £1.3bn.

Striking in the middle of the night, the 1 in 200-year storm left behind a trail of damage and devastation in the South East of England and Northern France with 18 people losing their lives and extensive damage to property and infrastructure. Many houses were without power for several days and fallen trees blocked roads and caused travel chaos. An estimated 15 million trees were uprooted and Seven Oaks famously became One Oak.

The worst affected areas were parts of Greater London, the Home Counties and the East of England. The South East of England experienced unusually strong wind gusts in excess of 81 mph lasting for 3 to 4 hours and gusts of up to 122 mph were recorded at Gorleston, Norfolk.

The exact path and severity of the storm were very difficult to predict using the forecasting methods and data available at the time.  The Met Office’s Michael Fish faced a backlash for dismissing a viewer who had asked about whether the UK could expect a hurricane but at the time it was hard to forecast the precise path the storm would take. The path of the storm and the direction of the wind were very unusual; running from south to north, with the storm striking the more densely populated areas of the South of England.  The South of England has higher concentrations of sums insured and this resulted in a large loss for the Insurance Industry. Subsequently, changes were made to the way forecasts are produced and the National Severe Weather Warning Service was created.

A better insight into windstorm risk

Data modelling and analytical tools to help underwrite and price property risks accurately for natural perils have come a long way since 1987 when data on individual properties was scarce and geographic risk assessed by postal district. Insurers are now much better equipped to gain an in-depth understanding of risk exposure with access to risk models that are based on up-to-date, accurate information and that take account of changing risk patterns.

Business Insight’s ‘Storm Insight’ risk rating model. is based on extensive research, huge volumes of explanatory input data and cutting-edge analytical techniques. Storm Insight utilises the largest source of storm claims information available in the UK, detailed property vulnerability data for every street and over 100 million historic windspeed data points recorded in urban areas across the UK.  We also have access to an archive of actual storm event footprints over the last 150 years to gain insight into rare events such as the 87-J Storm.

What would the industry loss be if 87-J were to happen again?

In 1987 the losses from the great storm on 17th October resulted in over £1 billion in insured losses to domestic property as well as significant damage to commercial property. Things have moved on since then, in terms of housing development, levels of affluence and insured values at risk. Over the last 30 years, there have been significant increases in housing development across the South of England in areas that were in the path of the storm in 1987.

Official figures from ONS show the number of residential properties in England increased by 28% between 1987 and 2017. In London (Outer and Inner) the increase has been 32%. Coupled with that inflation has more than doubled over the last thirty years and, perhaps more significantly, the wealth across the South East of England and London has increased enormously. Many more properties across the housing stock have been extended in 2017 compared with 1987 and the total insured values at risk is of an order of magnitude higher. The level of wealth is also far higher with one in ten households now reported as having assets worth more a £1 million.

If the UK were to encounter the same storm again in October 2017, the loss to the UK Insurance Industry would not be in the same league as recently reported losses in the USA and Caribbean though it would still break all previous UK records. In our view, it is likely that losses to the UK insurance industry for such an event would exceed £6bn.

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.

Long range Winter forecast 2016/17

The past few days have been distinctly chilly and have fuelled speculation of a harsh winter to come.  After last year’s mild and very wet winter, we look at early indications as to whether this is an accurate reflection.

There is an art to reading and understanding seasonal forecasts issued by the various weather services of the world.   Very few of the available forecasts use the same metrics making a consensus very difficult.

The Met Office released their 3-month outlook the beginning of November and in it they highlighted the risk of a cold start to the winter, but they were quick to point out that “This does not necessarily imply that the UK will experience cold and snow – in fact, the most likely outcome is for conditions to be relatively normal on average over the next 3 months.”

We asked our data partners at Weathernet if there were any indicators to suggest we are heading for the severe winter the press is speculating about.  The Weathernet team advise that beyond two weeks ahead, all forecasts should be treated as very speculative.

However, they report that certainly cold days – and night time frosts – are set to persist for at least another week. According to Steve Roberts of Weathernet this is due to a combination of factors and these include ENSO (El Nino Southern Oscillation) in a neutral state, QBO (Quasi-biennial Oscillation) in its easterly phase, SST (Sea Surface Temperatures – around Newfoundland) that are very warm, and the record lack of Arctic Ice.  So, the odds are already stacked significantly in favour of a December that is considerably colder (and drier) than normal.

Beyond then, from late January into February, things are less clear, or certain – but there are some grounds to believe conditions might revert to stormy and wet, leaving winter 2016-17 as a whole only a little colder and drier.

Beyond then, from late January into February, things are less clear, or certain – but there are some grounds to believe conditions might revert to stormy and wet, leaving winter 2016-17 as a whole only a little colder and drier.

If we do see temperatures as low as those of winter 2010/11, the insurance industry should be ready to brace themselves for a large number of Freeze claims.

Insurance and fire risk – 350 years on from the Great Fire of London

fire riskSeptember 2016 marks the 350th anniversary of the Great Fire of London.  The fire, which started in the early hours of Sunday 2nd September 1666 on Pudding Lane and lasted several days, devastated London.

Over 13,000 buildings were destroyed in the fire, including many homes, commercial buildings and other well-known landmarks such as St. Paul’s Cathedral, the Royal Exchange and Newgate Prison.  Miraculously, there was little loss of human life.

As the long and arduous task of rebuilding London commenced, to try and ensure that London would not face such devastation from a fire again, a number of changes were made to laws and Parliament set up the Fire Court.

The Court was established to settle differences arising between landlords and tenants in relation to burnt buildings and decide who should pay.  A year later, physician Nicholas Barbon set up the first insurance company, the Fire Office, whose sole purpose was to insure houses against loss due to fire.

The ABI have calculated that if that particular area of London were to be hit by a similar fire today, repairing the damage caused would cost somewhere in the region of £37 billion.

The insurance industry has come a long way since 1667 but is still dependent on a proper understanding of risks. With ABI figures showing that the average claim for domestic fire damage is around £11,000 and the average claim for commercial fire around £25,000, fire is an important peril for insurance companies to consider.

To help insurance companies better understand their exposure to fire claims and likely accumulations of risk in urban locations, Business Insight has a range of data enrichment models and a mapping and accumulation management application called ‘Location Matters’. The Fire Insight data enrichment models help to assess the relative risk and variation of deliberate and accidental fire claims across the UK; both for commercial property insurance and for home insurance. The models utilise highly complex computer algorithms and vast quantities of data relating to residential and commercial property, the local environment and the demographic make-up by area to estimate risk more precisely.

Accumulation management with ‘Location Matters’ enables an insurer to monitor policy accumulations by location to gain greater insight and understanding of risk exposure, allowing insurers to answer the question ‘should another Great Fire ever happen in London again, what is my probable maximum loss’?

To find out more contact our sales team on 01926 421408.

Product Focus – Escape of Water

perils and escape of waterEscape of Water (Non Freeze) claims currently account for around 25% of domestic claim costs, so having an accurate measure of escape of water risk is vital for insurers.

The cost of insurance claims resulting from escape of water claims such as plumbing equipment failure, and burst pipes and leaks can be significant.   Business Insight’s Escape of Water (Non-Freeze) model has been designed to predict the relative risk of escape of water claims across the UK.

Working closely with a number of insurers and data partners, Business Insight has utilised PhD level mathematical modelling to analyse highly detailed datasets against historic claims patterns to estimate risk by postcode. Over 100 million data records, 26 million properties, 1.7 million postcodes and heavy computing power has resulted in the most detailed project undertaken into this type of insured peril in the UK insurance industry.

Comprehensive information relating to property, the typical demographic make-up of the street and other key predictors has been combined to more precisely calculate the risk of an escape of water claim.  The output provides insurers with a deeper insight into the risk of an escape of water claim for enhanced risk selection and better pricing accuracy.

The model has been independently validated by a number of insurers against their experience data and has shown a high degree of predictive discrimination and potential for use as a rating factor.

Benefits include:

  • Better assessment of risk by location.
  • More precise pricing and rating.
  • Gaining insight into postcode areas where you have no experience data.
  • Discovering where you need to modify your rates to reduce exposure in higher risk areas and to optimise your profitability.

To find out more, please contact us on 01926 421408.

Product Focus – Accidental Damage

Gain an unrivalled perspective into the distribution of accidental damage risk across the UK

Business Insight has launched a new data model to help predict the relative risk of accidental damage claims across the UK covering lifestyle, property and geodemographic incidents.

Working closely with a number of insurers and data partners, Business Insight has utilised PhD level mathematical modelling to analyse highly detailed datasets against historic claims patterns to estimate risk by postcode. Based on a large sample of actual claims experience data, Business Insight’s AD Insight© uses the latest technology to analyse claims and predict annualised claims frequency by postcode unit.

This model outputs have been independently validated and tested against insurer experience data and have shown a high degree of predictive discrimination and potential for use as a rating factor.

The state-of-the-art maths modelling and computing power behind the model means that the model allows its users to extract the maximum predictive potential from the underlying data.  You can quickly discover how your rates compare in the low and high risk areas.  AD Insight’s powerful modelling capabilities provide users with:

  • Enhanced understanding of accidental damage risk
  • More accurate underwriting of accidental damage risk
  • Better decision making on risk selection, valuation and pricing.

Data quality and models

Whether it is collecting live information on drivers through telematics, the use of geographic risk mapping data to assess property underwriting risk or having access to weather and event data that includes claims and loss data, there is no doubt that data coupled with technology solutions is driving change in the insurance industry.

At Business Insight, we understand how important good quality, accurate data is. Data and models need to be regularly maintained and updated as the quality of data on which a predictive model is built and run will have an impact on the quality of the predictions it makes.  Without reliable and accurate data, you could be basing your underwriting and pricing decisions on old or out-of-date information.  The saying ‘garbage in, garbage out’ is certainly true and having the best technology, mapping or systems is pointless if the underlying data is substandard.

Effective data management and data quality are core components of Solvency II.  Article 121 which governs statistical quality standards sets out the requirements that insurers should perform regular data quality assessments.

With this in mind, there are a number of factors which you should take in to consideration before licensing software, data and models.  These include:

  • When the model was built
  • How often it is updated
  • Who built the model
  • Whether they have they done this before
  • Level of analytical experience and qualificationsWhat factors an insurer should consider Final version smaller.png