China and India together account for almost two billion mobile subscribers, far ahead of the United States at 330 million subscribers. The combined advantage of low cost and sophisticated devices along with affordable monthly plans, works as a lucrative vehicle for improving lifestyle choices, especially in emerging economies.
Accurately determining the latitude and longitude of a property (location) is a key factor in calculating exposures in Property Insurance. Predictive modeling accounts for property characteristics (location data, building material, building height, etc.) to arrive at possible loss and associated premium. Hence low accuracy of location data results in high uncertainty in the results of predictive modeling.
Location data quality in advanced countries like the United States is high owing to readily available, high resolution location information. Lack of this accurate information is a setback for emerging economies. Unlike many other successful solutions, this unique problem calls for a customized solution that leverages existing capabilities while delivering value.
Location-enabled mobile transactions and location-tagging in social media can potentially offset the challenges of accurately determining location. By mandating client locations to submit requests through mobile devices, insurers can capture location data along with any property characteristic that might be relevant for modeling. By scaling this function, data across multiple locations can be accumulated through the collective efforts of individual submissions from each location. Outsourcing data collection to the location level not only reduces effort associated with data collection and management, but also ensures data accountability. This presents a unique opportunity for insurers to pass the accountability to their clients and brokers for supplying high quality data.
Arguably, the current mobile ecosystem falls short of capturing all the information relevant for modeling. For example, GPS devices do not have the inherent capability to capture the height of a building and its construction type. Lack of this information could cause a significant level of uncertainty (and inaccuracy) in premium calculations. Also, ensuring accurate location data is solving only half the puzzle.
Maturity of a catastrophic model in advanced countries might be self-evident from the existence of region-specific models in the United States, whereas most Asian economies have national level models. Despite these shortcomings, a momentary SWOT analysis reveals the opportunity of automated location data capture by leveraging mobile capabilities. The day is not far where mobile technologies can be collectively sourced for building real-time collaborative modeling!
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