# The Role of Data-Driven Methodologies in Weather Index Insurance
## 6 Conclusions  
In this study, we employed two data-driven frameworks to assess the relation between three weather indices: precipitation, run-off, and soil moisture. We used flood records as loss events. This adds nuance to the building processes of index insurance models, particularly in the field of basis risk and its reduction. We used data from the period between 3 June 2000 and 31 December 2021 from the department of Antioquia, Colombia. We used flood records as a proxy for crop production loss, which is the traditional variable in index insurance models because agricultural data in Colombia is difficult to obtain or in many cases, non-existent. We implemented a similar methodology to [PERSON] [24], by using neural networks and logistic regression frameworks and by using the F1 score and AUC score for the selection of the best models. Results show that neural networks with one hidden layer, 100 hidden units, 0.4 dropout rate, and the ReLU activation function performs better than traditional logistic regression models for the data used. Even though the F1 score and area under the precision-sensibility curve metrics perform better than logistic regression, performance metrics are lower than the state-of-the-art architectures. However, these results are coherent with previous studies that indicate that data-driven methodologies, such as neural networks, tend to perform better than traditional methods for explaining the relationships between the weather indices and the losses in index insurance models. Perhaps, one of the biggest restrictions in data-driven studies is the quality and availability of data. As is the case for low-income economies, low-performance results could be explained by the quality of the data, where one possible cause of such results could be related to the reporting processes for the catastrophic events in the region studied, in this case, the flood records. To support the selection process for best model performance and to understand related costs in a weather index insurance contract, we applied a utility model to derive a measure for the costs associated for both parties involved: the policyholder and the insurance provider [31]. A measure of value is more relevant than a measure of quality, as the end users are more concerned with the estimated benefits associated with the context of the problem. Results show that costs associated with the policyholder are lower for the most part of the range of cut-off values. This is related to an improvement in the predictive capacity of the model, which is in line with a reduction in the basis risk. This approach contributes to the future construction of weather insurance indices for the region where a decrease in the base risk would be expected and, thereby, a reduction in insurance costs. Figure 12: Insurance provider-related costs. This study provides a reference point for many practitioners and researchers in index insurance models in low-income economies where agricultural data availability is limited. We obtained coherent results which indicate the potential of data-driven methodologies such as neural networks. We compared the performance of neural networks to traditional models and demonstrated that with limited data, we could reach relevant conclusions that indicate that it is possible to improve the design of index insurance models, especially for low-income economies. The results presented here could be improved by creating a new loss event dataset by alternative means. For example, by cross-referencing multiple catastrophic news events, or acquiring production loss data by other means, such as field inspections. We believe this work serves as an interesting study to encourage more discussion and promote the development of agricultural insurance in Colombia. Conceptualization, L.F.H.-R., A.L.A.-P. and J.J.G.-C.; Methodology, L.F.H.-R., A.L.A.-P. and J.J.G.-C.; Validation, L.F.H.-R., F.E.L.M., C.F.V.-A., M.C.D.-J.