What is shared online about big data and marine insurance? Observers say that it can “disrupt the future of post pandemic underwriting including blockchain, … AI and machine learning: learn how to leverage data to support augmented underwriting”, and so on…

If mass disruption is to take place, how can we square the circle of partnering innovation with traditional insurance business? And what is the balance between concrete added value and challenges?

Perhaps analysing some business cases will help illuminate this problem.

1/ Business case:

Who? Marine insurer actuaries

Pain point: Lack of accurate insured values impacting the insurance pricing and the amount of indemnification negatively.

Solution: Proceed to clean internal data of vessel references, hunt external data (values) and aggregate internal and external data.

Added value: A better pricing and indemnification process, as well as increased productivity and better interactions between actuaries and underwriting.

The next step is to correlate vessel characteristics with loss data to discriminate risk profiles.

 

2 / Business Case: 

Who? A marine underwriter.

Pain point: Lack of visibility of actual risk per vessel and lack of KPI regarding portfolio risk profile.

Solution: An insurtech platform automatically connected with global data sources, supported by data science and providing a Risk Rating and an Emission Estimation of the vessel, fleet and portfolio, together with a detailed risk radar explaining the rating.

Added value: A quick and much more accurate risk analysis along with improved productivity. Moreover, Emission Estimation, as an underwriting criterion, supports the operational declination of ESG, aligned with the Poseidon Principles.

To conclude, here are key transversal learnings we are happy to share as an insurtech:

• The status of historical data is a challenge but not a blocking point.
• The leverage of data science is huge regarding the whole combined ratio: productivity, losses (right amount/avoidance), premium (better matching with each risk profile).
• In the end, innovation should serve every stakeholder with the democratisation of data science: insurers, clients and brokers, with more transparency about risks.