Mapping the customer centric weather index insurance service design using quality function deployment

PurposeThe present paper aims to propose a framework on weather index insurance (WII) service design by using quality function deployment (QFD).Design/methodology/approachThis study utilizes QFD technique to propose a customer oriented framework on WII service design. In initial phase, customer and design requirements were gathered to derive the relationship between customers' and managers' voice for construct the house of quality (HOQ). Later on, prioritized customer and design requirements as QFD outcome were utilized to develop the action plan matrix in order to suggest the future action plans.FindingsThis study proposed a customer centric framework on WII service design to address the customer requirements. Findings show that adequate claim payments, hassle free prompt claim payment and transparency in losses computation are prioritized customer requirements with highest importance rating, whereas, accurate claim estimation, claim management system and advancement of technology are prioritized service design necessities with highest importance rating.Research limitations/implicationsThe proposed WII service design can enhance the quality of WII service by attain the higher standards of WII service in order to completely satisfy the customers.Practical implicationsThe proposed WII service design can provide a solution to the problems faced by WII industry by improve the customer's service experience and satisfaction.Originality/valueBased on best of author's knowledge, this paper first proposed a framework on WII service design by integrating customer and design requirements by using QFD.

Modelling the barriers of weather index insurance service adoption integrating expert mining and ISM Fuzzy-MICMAC

PurposeThe purpose of this study is to explore and prioritize the barriers that affect weather index-insurance (WII) adoption among customers by utilizing interpretive structural modelling (ISM) and fuzzy-MICMAC.Design/methodology/approachThis paper utilized the combined approach in two phases. In first phase comprehensive literature study and expert mining method have been performed to identify and validate WII adoption barriers. In second phase, ISM has been utilized to examine the direct relationships among WII adoption barriers in order to develop a structural model. Further, fuzzy-MICMAC method has been utilized to analyse indirect relationships among barriers to explore dependence and driver power.FindingsThis study has identified 15 key barriers of WII adoption among customers and developed a structural model based on binary direct relationship using ISM. Later, the outcomes of ISM model have been utilized for analysing the dependence and driver power of each WII adoption barriers in cluster form using fuzzy-MICMAC. The customer awareness related WII adoption barrier are mainly at the top level, WII demand related barriers are in the centre and WII supply related barriers at the bottom level in ISM model.Practical implicationsThe findings offered important insights for WII insurers to understand mutual relationships amongst WII adoption barriers and assists in developing strategy to eliminate dominant key barriers in order to enhance their customer base.Originality/valueBased on best of author's knowledge this paper firstly integrates the ISM fuzzy-MICMAC method into identification and prioritization of barriers that affects WII adoption among customers.

Evaluating the quality of remote sensing products for agricultural index insurance

Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

Sequenced crop evapotranspiration and water requirement in developing a multi-trigger rainfall index insurance and risk-contingent credit

Weather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intra-seasonal basis risk. To minimize intra-seasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have shown to be ineffective in some cases as they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agro-meteorology we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage, that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modelling to price an operational WII and WII-linked credit product. The risk modelling approach we develop includes incorporation of correlation between rainfall and evapotranspiration indexes that can minimise significant intertemporal basis risk in WII.

The effect of COVID-19 on Qatar and Italy stock exchanges

The aim of this research is to study the effect of Covid-19 on both Qatar and Italy stock exchanges between 2018 and 2020. Based on a sample derived from five different indexes from Qatar and Italy stock exchange (Banking index, Industrial index, Insurance index, Goods and Services index, Telecommunication index), the results reveal that Covid-19 has a negative effect on both stock exchange indexes. It is also revealed that Italy stock exchange was more volatile to changes caused by Covid-19 than Qatar stock exchange.

The potential of machine learning for weather index insurance

Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.