## 1 Introduction
For index-based insurance programs, satellite weather data has been increasingly employed in developed economies [34]. However, in low-income economies, satellite information is difficult to access. In Colombia, the average penetration rate of crop insurance between 2010 and 2015 was only 1.76% (hectares seeded vs. insured hectares) [35]. The limited traditional compensation processes available for Colombian crops have typically relied on traditional insurance frameworks based on yield loss or damage [11]. Some reasons for the low coverage rates in the country are related to local conditions, such as regional conflict and the resulting insecurity, which makes field inspections difficult [36]. Although there are many studies on weather-indexed insurance in the literature, there is still a lack of research that includes product design in low-income economies [19]. State-of-the-art studies show diverse types of index insurance models where indices are mostly built from mereological, temperature, meteorological drought, hydrological drought, climate, and vegetation indices. For the methods of yield-index relationship estimation, the most common methods used to build indices are regression, correlation, copulas, production functions, and, more recently, machine-learning methods [20]. Regarding the risk evaluation methods, most studies focus on risk-reducing effectiveness evaluation methods, such as variance or semi-variance deviation, value at risk, or conditional value at risk [37; 38; 39; 40; 41]. However, few of these methods address the relevance of measuring future financial risks in highly volatile regions. For example, [PERSON] [42] used a Gerrit Skill Score (GSS) to assess the way indices forecast payouts or non-payouts during loss and non-loss years. This was seen as advantageous, as the GSS score weighs both sides of basis risk, assigning false payout and failure to pay. Recommendations from these state-of-the-art reviews include more in-depth studies emphasizing index derivation during critical crop growth periods and the employment of models to aid in index design that considers future climate conditions. This study addresses these two recommendations. Referring to the costs for index insurance programs, [PERSON] [43] states that one of the main shortcomings in these programs is the high cost for low-income farmers in developing countries, as most of their income is absorbed by their basic needs such as food and housing. This results in low uptake of this insurance tool. [PERSON] [24] states that while high premium prices for weather index insurance programs are the main constraint when adopting weather index insurance, this is offset by public subsidies and support donors who help to promote insurance expansion. Some studies discuss whether public subsidies are the best way to promote the use of index insurance. Instead, these authors promote the use of credit subsidies and fertilizer subsidies in conjunction or separately. [PERSON] et al. [44] showed first that the potential benefits of public subsidized weather index insurance are more evident in the driest areas. Second, in zones with extreme precipitation, insurance premiums that are publicly subsidized by 60% induced farmers to adopt insurance; however, this incentive was less efficient than subsidizing credits and technologies such as fertilizers, or just cash transfers. One Colombian index insurance example of expansion is the Cafe Seguro program. In this program, one of the key factors that promote the adoption of index insurance in coffee crops has been the close work by insurers with farmers. Farmers receive clear information about the rationale of the insurance tool, and questions are answered regarding their needs and the expectations of the insurance program. In addition, these insurance programs are successful because of recent public and private subsidies [33].