A quick look at the evolution of surveying into the digital age.

Legacy of the past – joint hull survey

Condition surveying of vessels has not changed much over time. Recent years have seen surveyors adopting joint hull standards and considerations, but the survey itself is still very much left to the surveyor’s discretion. The surveyor may or may not be named in the policy, approved by the underwriters but instructed (and paid for) by the owner.

 

Data analytics

A hot topic at the moment is what role are marine insurers going to play in the future; will it be pure risk carriers or trusted partners offering comprehensive support and services to manage risks in a changing world? Digitalisation will have a big impact on the future definition of that role. By embracing data analytics, artificial intelligence (AI) and machine learning, data can be turned into invaluable knowledge, enabling the introduction of pre-emptive risk-mitigation measures. A traditional ‘analogue’ marine condition survey is merely a snapshot of the risk, as and when the vessel is visited and there is a huge opportunity in analysing vessel and owner performance, so-called behavioural data to assess the risk much deeper.

 

The survey today

Underwriters seek insight into the risk and search for items affecting the cover, potentially leading to claims. At the same time, for years now, the hull and machinery (H&M) industry has been in a soft market, where cost is a key factor in the decision-making process governing the survey instructions and scope. Two other ‘pain-points’ is the time required for a survey and the disruption to a vessel’s operation from a surveyor having to attend. A typical joint hull condition survey lasts about three days, inclusive of travel time and writing the report. This now can be done in a matter of hours, without compromising on the critical information required to assess the risk.

 

Tomorrow’s marine loss prevention survey

Imagine starting from the available data analytics on vessel performance identifying vessels of high risk leading to a self-survey for the chief engineer to perform, whereby the cost of the marine surveyor and lead-time for the report can be cut by up to 90%.

Imagine the automation of the surveyor search and instruction, building on capabilities and performance. Data analytics and machine learning from the survey information combined with the data of the on-scene information gathered - a blend of human and digital intelligence; the dawn of bionic loss prevention.