This article is part of Carrier Management’s series on the Future of Insurance.
Arun Balakrishnan, CEO, Xceedance, believes more personalized risk assessment is possible in the future with IoT data sources informing decisions and underwriters transforming institutional knowledge to machines.
Q: What major changes do you see on the horizon for the property/casualty insurance industry in the next 10 years? What will insurance companies, insurance leaders, the industry and its workforce look like in the next decade? What risks will they insure?
Balakrishnan (Xceedance): Looking ahead, it’s a new day for both insurance organizations and policyholders. The insurance industry increasingly realizes that traditional business operations, as well as channel, product and service offerings, must all concentrate on customer-centricity. This realization is driven by an appreciation that modern policyholders are much more than an attribute of policies. So, a refined and disciplined focus on policyholder needs will open doors to new insights, processes and partnerships for higher service standards and productivity across the insurance value chain.
In the spirit of InsurTech innovation and competitive differentiation, there is also interest by insurance organizations to partner with experienced, industry-focused managed services providers—predominantly to ease operational pressures and the mounting costs of product and service delivery to a much more sophisticated insured client base.
In a snapshot view of the future reinsurance market, there will likely be a recurring need for “burst resource capacity” at critical renewal periods, especially where reinsurers rely heavily on expert cat modeling and portfolio analysis. Resource optimization is often best accomplished by temporarily deploying a team of well-trained cat analysts and modelers supplied by a service partner. And, if the services partner can build on-demand, accountable teams for data cleansing, model runs and key exposure management tasks, reinsurers can create highly responsive decisioning visibility and service value for their clients. In part, that’s because machine-learning automation can streamline the complexities of model management, creating greater process efficiencies and data-driven precision for reinsurance risk assessment.
Reinsurers are also realizing the value of distributed ledger technology (DLT), or blockchain, in supporting syndicated, layered risk placements, such as excess business, facultative and nonproportional treaty reinsurance.
Such paradigm shifts essentially herald the emergence of a completely new kind of insurance organization, or at least a significant transformation in the way traditional insurance organizations do business.
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