How DOT identified potential Maritime incidents before they happened


Central government


Data strategy
Data analytics
Predictive analytics

DOT ran AI across past Maritime incidents to understand what contributes to incidents, and to predict where they may occur in the future. This will enable Maritime to improve their processes around safety within domestic commercial vessels in New Zealand.

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Maritime NZ (MNZ) is the national maritime regulatory, compliance and response agency responsible for safety in New Zealand’s maritime environment. This relies heavily on collecting up-to-date and relevant data to understand accident risk on NZ waters DOT helped MNZ leverage untapped potential in their data as part of a shift to a more strategic and proactive risk assessment approach by developing a predictive, data-led risk model.

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DOT developed machine learning models to predict maritime risk among commercial operators and identify potential reporting bias. Model performance was enhanced by combining the predictive power of MNZ and external datasets. Alongside our modelling work, DOT also conducted a data stocktake that provided targeted insights and actionable recommendations to support MNZ’s long and medium-term goals in data analytics and governance.

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The new risk assessment model resulted in a more balanced distribution of risk among commercial operators that better reflected actual risk and business needs. It deepened organisational insight about maritime risk by challenging some long-held assumptions while supporting others and identified trends among high and low risk groups. It also identified segments of the commercial fleet that may be underreporting and in need of targeted interventions. The new model enables smarter decision making, better targeted and designed safety interventions, more efficient business operations, and provides a means of measuring and monitoring risk within domestic commercial vessels in New Zealand.