[by Andrea Paschetta]
Parametric insurance is an innovative form of insurance that relies on pre-defined parameters (clear from the outset) to determine compensation, rather than the actual losses suffered by the end customer.
This approach allows claims to be processed more quickly and transparently, minimising disputes and providing more efficient cover for catastrophic events such as flight delays, agricultural damage and natural disasters.
However, the use of AI in areas such as parametric insurance presents significant challenges in ensuring accuracy and consistency:
Data quality (real-time updates for pricing), to achieve accuracy, AI requires high quality data. In the case of parametric insurance, data from sources such as weather stations, satellite technology and blockchain must be accurate and reliable.
Risk modelling: Parametric insurance relies on statistical models and algorithms to assess and predict risk. Where it is difficult to create accurate models that take into account all relevant variables, tools such as neural networks and machine learning must be used.
Interpret unstructured data: AI must be able to effectively interpret and analyse this data to extract relevant information for use in risk assessment, actuarial pricing and underwriting.
Predictive interpretation and analysis: Weather forecasts or other predictive analysis may be subject to error or uncertainty. The IA must be able to critically assess the validity of forecasts and integrate this assessment into underwriting decisions.
Buying traditional indemnity-based insurance is not always an option, nor is it always the most effective way to manage exposure to natural catastrophes. A growing number of parametric insurance solutions are available, mainly from reinsurers such as SwissRe, MunichRe and Scor, where the amount of loss is agreed in advance for a specific risk profile.
Once a pre-defined threshold is reached, a reimbursement is made to protect the company’s cash flow and profits.
