Disaster Response and Reconnaissance Services

Capture the power of accurate real-world damage data as soon as disaster strikes to avoid misleading loss estimations or potential downgrades

eCityRisk’s Visual Business Intelligence and Field Reconnaissance Services provide the most accurate picture in the aftermath of natural disasters – vital for accurate loss estimation

One of the major challenges for catastrophe risk assessment is producing accurate initial loss estimates in the immediate aftermath of major disasters.

eCityRisk’s pioneering time-critical visual business intelligence and reconnaissance services provide decision support for property and casualty lines of business in the hours and days after major events strike. Floods, earthquakes, hurricanes, tornados, tsunamis and wildfires can have devastating effects for not only those involved in the disasters but also for those companies with financial assets exposed to these catastrophes.

Poorly informed capital management decisions, dramatic falls in share prices and widely fluctuating payment expectations can all be the result of inaccurate loss estimations arising from undue reliance on modelled results, not to mention the risk of being downgraded by ratings agencies.

Accuracy more important than speed

Companies are under great pressure to report loss estimations as soon as disaster strikes, however, speed is no longer the most important factor, as many CROs are now revealing. Going public with accurate loss estimations is far more important than releasing early inaccurate figures produced from unreliable modelled estimates.

This growing outlook is also supported by ratings agencies. In a recent survey conducted by eCityRisk, an AM Best representative commented:

"In the case of extreme events, accurate loss estimations make our job easier and are beneficial to the reinsurer because if we are comfortable that their numbers are reasonable, it can help avoid being placed under review."

Real-world losses using real-world data

During the record 2005 hurricane season in which Katrina became the most costliest and deadliest hurricane to date in the history of the USA, and the events of 2008 where the Atlantic basin saw a hurricane a month from July to November, probabilistic models did not accurately capture how the insurance industry sustained losses and therefore real-world losses were considerably higher than stochastic models predicted. Indeed, actual losses differed from the AIR, RMS and EQECAT modelled estimates by many millions of dollars for many portfolios and, in the case of industry losses, the margin stretched to as much as 100 billion dollars » view article

eCityRisk’s disaster response data and in-field reconnaissance services offer an independent, real-world viewpoint of damage to structures and high-value facilities. Add confidence to your response activities, accumulation analyses, claims management process, and capital management decisions, choosing from eCityRisk’s data options to obtain ground-up loss estimates based on actual damage rather than modelled estimates.

Demonstrating ERM Accountability

Employing best practise for catastrophe loss estimation is essential for insurers, reinsurers, brokers and the financial markets; indeed it is an essential tool for any company that can have a potential loss associated with a natural disaster as part of Enterprise Risk Management (ERM) accountability.

eCityRisk’s GIS-compatible datasets provide clients with a means of identifying impacted contracts first hand, verifying potential losses based on observed damage, and in line with ERM Solvency II requirements offers an objective external comparison with modelled results.

Amidst the confusion and widely varying reports of damage in the hours after a disaster strikes, eCityRisk provides the intelligence needed with specialist access to impacted zones. Using aerial and satellite imagery, data mining and advanced geospatial field survey techniques, eCityRisk penetrates the disaster zone to offer a range of visual business intelligence and field reconnaissance data options.