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Indigenous knowledge is an essential element for unveiling the evolutionary journey of socio-culture phenomena. One of the key challenges in foresight exercises is to incorporate social-culture issues such as culture, lifestyle and behavior (referred as indigenous knowledge) into the study. However, the statistical trends of those factors tend to be either not available or limited unlike the population or economic related factors. The purpose of this study is to present the use of valuable data from indigenous knowledge to enhance the foresight exercise through the better understanding of social dynamics and changes.

The fragmented form of indigenous knowledge is analyzed and converted into a structured data format and then interpreted to unveil the evolutionary journey of socio-cultural phenomena. This study applies a scenario development method to visualize the results of foresight by comparing before and after the integration of indigenous knowledge. Finally, an assessment was conducted to reflect the value enhancement resulting from the integration of indigenous knowledge into the foresight process.

With the proposed approach, the foresight study on the future development of Thai food was demonstrated. The findings of this study show that the use of indigenous knowledge on eating behavior, cooking style and food flavor helps improve the alternative scenarios for the future development of Thai foods.

Indigenous knowledge can be applied to develop plausible scenarios and future images in foresight exercises. However, by nature, indigenous knowledge is not well-structured and, therefore, needs to be analyzed and turned into structured data so that it can be interpreted before integrating into the foresight process.

This study is one of few studies addressing the opportunities for integrating indigenous knowledge into foresight process. Indigenous knowledge can unveil the evolution of socio-cultural changes to improve the results of foresight study, especially the cases where statistical data and trends may not be sufficient to foresee future development.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.