Have you used a vendor to provide Artificial Intelligence (AI)-generated risk modelling and pricing based on your portfolio? Were the results unexpected? Could you understand the AI model behind the outcomes? Did you have the quality of data needed for effective AI modelling?
Artificial intelligence (AI) is slowly transforming marine insurance; it promises speed, precision and new insights for risk management, pricing, claims handling and workflow improvements. However, as the industry embraces automation and advanced analytics, the fundamental need for robust standards and quality of data has never been greater. Without these, AI-generated output risks becoming inconsistent and unusable. Data quality, interoperability and transparency must be prioritised to ensure that AI applications genuinely reflect reality. Approved standards will define acceptable data sources and model validation processes, making sure that different AI tools produce comparable, reliable results. These are vital for underwriting decisions, claims handling and compliance.
The dangers of an over-reliance on AI in risk management and pricing are significant. AI models, if unchecked, may amplify historical biases present in data, leading to inaccurate risk assessments. Limited model transparency can obscure errors or weaknesses – difficult to detect until they cause costly losses. Moreover, unquestioning trust in AI could foster complacency, diminishing the human oversight and expertise that remain essential for interpreting complex scenarios and risks.
By establishing and adhering to standards and improved data quality, the industry can harness the power of AI while reducing its pitfalls. In this way, AI should enhance – not replace – the judgement and work of marine insurance professionals.
