Cyence for Small Business Workers’ Compensation OverviewOn March 1, 2020 by Raul Dinwiddie
More and more small businesses are started every year. According to statistics by the US Small Business Administration, approximately 530,000 small businesses are added to the economy every year. This presents a tremendous opportunity for insurers. But it’s often hard to find the data to effectively evaluate how to price these policies. But these small businesses have unique needs that insurers have to address. For insurers, this means tough trade-offs between profitability, growth, and customer experience. The traditional process of manual underwriting may be trusted to provide the most accurate pricing. However, manual underwriting processes may maximize profitability and growth, but often result in slow turnaround times that frustrate channel partners and business owners. And the poor user experience limits the success of digital business models. Small-business owners typically prefer to do business online and expect to get a quote in minutes, not days. So insurers who want to succeed in this market have to provide a streamlined digital experience. Straight-through underwriting processes can maximize customer service and growth but can lead to potential adverse selection, putting book profitability at risk. Target market segmentation can maximize customer service and profitability but limits growth potential, while costs increased dramatically with scale. Cyence for Small Business for Workers’ Compensation is the solution even if you don’t currently use Guidewire. Cyence collects hundreds of sources of data on companies over a petabyte a month. Cyence then applies state-of-the-art data science techniques and machine learning to curate the data and make sense of it. Then we provide this sophisticated analytics and risk modeling via an easy-to-consume API. Cyence helps insurers with a non-trivial process of determining risk differentiation. For example, two hardware stores with the same number of employees and approximate revenue may look like similar risk. But when other, non-obvious data on the two stores is analyzed, such as services provided, business hours, neighborhood crime rate, street foot traffic, and proximity to a hospital, the risks may look very different. By having access to real-time, non-obvious data sources, insurers can more easily differentiate risk, automate underwriting, and focus customer acquisition spend, by fine-tuning market segmentation. Contact us to schedule a demo today.