| dc.description.abstract | Digitalisation is making a growing appearance across all sectors, and traditional P&I insurance
is no exception. Marine insurance is said to be several years behind traditional land-based
insurance when it comes to digitalisation. This thesis is attempting to narrow the gap, by
investigating the potential of applying machine learning on AIS-information against the
extensive database on P&I insurance claims from the P&I Club Skuld.
The thesis aims at investigating the potential to predict P&I insurance claims based on variables
retrieved from AIS. AIS-information from 2013-2017 and Skuld's claims data for the same
period was combined, and a total of five machine learning methods were tested to assess the
predictive power of AIS-information. An extensive pre-processing was executed to make the
data available for machine learning, and this section provides detailed information to anyone
that aims at utilising AIS in their research.
The research finds that AIS-information has predictive power for claims, as it links claims to
activity level and operational patterns of the merchant fleet. The findings have implication for
two fields in marine insurance; risk assessment/ pricing and loss prevention. In relation to loss
prevention; average distance sailed, number of unique ports visited, and total distance sailed
were found to have the most predictive power. Regarding risk assessment, the strongest model
was able to predict 79 % of all cargo claims for Bulk & Cargo small.
The research has revealed that machine learning has potential to create significant value in P&I
insurance and that an extensive amount of data is ready to be applied in the pursuit of more
accurate risk assessments and more precise loss prevention measures. Estimates vary between
a potential yearly reduction in claims of 7-14%, in addition to increased revenue as a result of
correct pricing. | nb_NO |
