Underwriting - The term is derived from Lloyd's of london insurance market, where insurers will literally write their names under the risk information slips. The term indicates careful evaluation and ownership.
In this era of low interest rates, insurance companies need strong real-time analytics capabilities to achieve the elusive underwriting profit and sustained growth,” explained Amit Unde, chief architect and director of insurance solutions for L&T Infotech. “Going forward, the competitive battles will be played on the data turf. It’s the companies that leverage both external and internal Big Data, predictive analytics and adoptive underwriting models that will come out on top.
With Google Maps and location intelligence services, the underwriter can view a property from all angles and assess distance from a coastline, flood plain or other potential hazards. Online access to hundreds of different data sources—from videos to photos to loss trends and other documents— is now just a few clicks away,” Unde said. “But, without the right tools, mining this data is still a highly manual process.
I wouldn’t be surprised if, in the next five years, the next big player in the commercial insurance industry was a new company with a Big Data-driven automated policy issuance and claims payout model,” Unde said. “Automated decision-making has the potential to transform the industry, enabling small players to compete with large insurers, based on their technology.
In the insurance industry, companies should validate against a set of rules or cross-verify against multiple sources,” Unde said. “However, in most cases, it doesn’t make sense for insurers to boil the ocean to get 100 percent data accuracy. It makes better sense to apply the 80/20 rule to achieve the desired accuracy for the 80 percent of the dataset without having to invest intensive efforts—then asking ‘did you mean’ questions in the remaining 20 percent of cases.