# The Role of Data-Driven Methodologies in Weather Index Insurance
## 1 Introduction
There are few worldwide ongoing index insurance programs that use artificial intelligence and machine learning frameworks to derive insurance programs [5]. One example is the Pradhan Mantri Fasal Bima Yojana (PMFBY) program offered by the company Cropin [14]. As one of the big challenges in index insurance design is the availability of data, this company started in 2018 piloting small subplots within cultivated fields in five different states in India through the PMFBY program. The program relied on remote sensing with ground sensors and satellite images to measure yield performance among other crop development variables. One of the main goals of the program was to improve crop-cutting experiments (CCEs). Usually, CCEs are followed by inconsistent processes which can be translated into delayed and complicated yield data acquisition and forecasting processes [15; 16]. Through artificial intelligence and machine learning techniques, the program provides reliable, cost-effective estimates of crop yields, which usually represents essential information in the conformation of index insurance programs. This program has benefited more than 250,000 villages across India. In the case of Colombia, there is a lack of sufficient availability of crop yield data and other variables that measure soil and crop quality for index insurance research purposes. As crop production relies on high-dimensional weather conditions, the selection of weather variables for constructing the index insurance contract is crucial for the successful implementation of the hedging instrument. Moreover, crop development relieson weather conditions in a non-linear way [17; 18; 19]. Therefore, we propose an adaptation to an index insurance model in which we used a proxy variable for crop yield loss and satellite weather variables. This adaptation could be incorporated in further designs of index insurance contracts. We use neural networks to show that data-driven methodologies usually demonstrate superior performances to capture non-linear relationships in weather index insurance data. These models demonstrate their capability to capture the intricate relations between the weather variables and the loss, with the goal of lowering basis risk and costs and, therefore, increasing future willingness of farmers to acquire insurance. According to [PERSON] et al. [20], risk can be classified into five types: production, market, institutional, personal, and financial. Production risk stems from the uncertainty associated with the natural growth process of crops, which is related to climatic events and pests [20]. Of the literature associated with weather insurance published from 1974 to 2019, 66% focuses on this type of risk [20]. Several methods have been employed in the literature to estimate the dependence between production and the index used. These methods seek to study the extent to which the variation in performance can be explained using the weather index. Rainfall and temperature are two of the most important factors that contribute to explaining variations in production [21]. Among these, the most widely used have been ordinary least squares regression and correlation [5]. Despite their frequent use, they have several drawbacks as they assume a constant relationship between all weather event scenarios and make assumptions that do not fit yield behaviour [5]. To overcome these drawbacks, QR, GAM, and copulas approaches have been explored for index insurance, which has been shown to be superior to OLS regression [5]. One approach to address the non-linear relationship between crop loss and the weather index is the implementation of machine learning models, which have great flexibility and can be adapted to complex information. [PERSON] et al. [22], proposed a neural network-based design of an index insurance contract for production losses which helps to reduce basis risk and a utility function to derive the cost-effectiveness of index insurance.