Cognitive Computing: Taming Big Data

Cognitive computing improves customer self-service, call-center assistance, underwriting, claims management and regulatory compliance.

In the complex, diverse insurance industry, it can be hard to reconcile theory and practice. Adapting to new processes, systems, and strategies is always challenging. However, with the arrival of new opportunities, cultural transformation will go more smoothly. Insurance companies that are considering how to plug into the insurtech landscape should understand the various models within the innovation ecosystem. Carriers have to weigh their options carefully before choosing between incubators and accelerators, or venture capital and partnerships, when creating their best internal and external teams. The key elements disrupting the insurance industry include the Internet of Things (IoT), wearables, big data, artificial intelligence and on-demand insurance. Although well-established business models, processes and organizations are being forced to adapt, insurtech can be more collaborative than disruptive. It is no secret that the insurance industry is responding to changing market dynamics such as new regulations, legislation and technology. With digital transformation, there are numerous ways technology can improve and streamline current insurance processes. See also: Rise of the Machines in Insurance   Cognitive Computing Cognitive computing, a subset of AI, mimics human intelligence. It can be deployed to radically streamline industry processes. According to the 2016 IBM Institute for Business Value survey, 90% of insurance executives believe that cognitive technologies will have an impact on their revenue models. The ability of cognitive technologies to handle both structured and unstructured data in new ways will foster advanced models of business operations and processes. Insurance carriers can use this technology for improved customer self-service, call-center assistance, underwriting, claims management and regulatory compliance. Big Data Unstructured data is rapidly growing every day. For instance, wearables can provide insurance companies with massive amounts of data that can yield insights about their markets. Social media also produces a flood of data. To harvest this data intelligently, insurers need to adopt the right analytical solutions to analyze, clean and verify data to customize their offerings according to their clients’ individual needs. Predictive analytics evaluates the trends found in big data to determine risk, set premiums, quote individual and group insurance policies and target key markets more accurately. Linking the Two Insurance organizations may have more data than they realize or know what to do with. Existing data is coming in from different core systems, and new data is being captured with IoT devices like wearables and sensors. Cognitive computing is the link to organizing and optimizing this data for use. See also: Strategies to Master Massively Big Data   Whether it is used to predict risk and determine premiums, flag fraudulent claims or identify what products a customer is likely to buy, cognitive computing is the way to ensure these goals are achieved. Sorting these trends among reams of data makes them more manageable and ensures that a business’s IT objectives link back to business strategies. Over the years, systems will evolve through learning processes to a level of intelligence that can adequately support more complex business functions. Schedule a meeting with your executive team to examine risks, opportunities and insurtech synergies that can take your organization beyond the competition.

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