We live in a world of Big Data. Petabytes, Zettabytes,
Yottabytes of data. The Internet of Things (IoT) increasingly
connects day-to-day appliances, machines and equipment with each
other. Billions of devices constantly recording data: sensors,
cameras, microphones, thermostats, pressure gauges, RFID chips,
attached to everything from mobile phones to industrial
Contrast this with early Business Interruption (BI) insurance in
the 1860's when obtaining reliable records to quantify losses
would have been difficult, long before the advent of modern
accounting and reporting standards. Most records were manually
created until as recently as the 1990's when transition from
paper to computer accelerated. Even then a 3 1/2" floppy disk
only stored 1.44MB and joining multiple Excel sheets was not
Nowadays datasets grow rapidly, to the extent they cannot be
easily managed, stored, shared, analysed or queried with previously
applied methods, and require new approaches.
Despite these previously unimaginable changes, the fundamental
principles of BI insurance have not changed significantly over the
past 50 years. But what potential effects can increased data have
on the insurance industry?
Could big data mean less losses?
GE, for example, remotely monitors vibrations, temperature, and
pressure on thousands of turbines around the world 24/7/365. The
data interpreted by its proprietary software Predix, allows
operators improved efficiencies and enables preventative
maintenance before units malfunction, mitigating both uninsured and
Could big data provide assistance for underwriters?
Could underwriters follow telematics car insurers' lead and
harness industrial operational data to specifically price a product
for companies which provide access to this data?
Could big data mean faster settlement of claims?
Typically files provided on a large energy claim can run to
hundreds of megabytes, if not gigabytes. This can include refinery
Linear Programming, petrochemical production data or hourly power
plant data containing detailed statistics such as: ambient
temperature; availability; generation; flow rate; heat rate; fuel
usage; or utilities usage. Whilst arguably not in the realms of big
data, it is sufficiently large to require a forensic accountant to
mine and distil down for Insurers consumption.
Enterprise Resource Planning (ERP) systems allow extraction of
specifically tailored datasets of Production, Sales and Inventory
to assess BI losses, reducing the time spent by the Insured
manually preparing reports, which would require verification back
to source records anyway.
Time can be better spent on analysis issues relevant to the
quantum of loss, improving response time for updates, interim
payments and settlement figures.
Could big data mean more accurate loss measurements?
Increasing volumes and granularity of data allows quantification
of losses which would have been unsupportable historically, for
Analysis of heat rates and fuel burn
to identify turbine efficiency losses following non-catastrophic
Change in product mix following a
Identifying loss of capacity on a
Flaring of hydrocarbons in the
aftermath of an incident at a petrochemical complex
Correlation of availability and
output to ambient temperature during an outage
Increasingly complex models can be constructed reflecting
interdependencies of operational units allowing improved analysis
of issues, and more accurate loss measurements.
Is there a down side to big data?
As IoT connectivity and data volumes increase, so does the
potential for something to go wrong. Companies are under threat
from cyber-attack, be it disruptive (e.g. data breach) to causing
catastrophic damage (and resultant BI).
The need to extract, handle and analyse large volumes of data,
means it is important for underwriters and claims handlers to work
with forensic accountants who know the data to request, how to
analyse this data, and distil the data into a user-friendly
assessment, which stands up to scrutiny.
The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.
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