Want Smarter Machines? Let Underwriters Train Them

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.

Arun Balakrishnan, CEO, Xceedance

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.

“Insurers typically have access to more information in one glance than an individual’s doctor, banker or financial adviser.”

Another big ongoing transformation is in the fundamental relationship between insurance operations and data. Insurers typically have access to more information in one glance than an individual’s doctor, banker or financial adviser. In serving the risk management needs of policyholders, insurers will increasingly leverage emerging technologies to optimize data access and analysis and be more open to integrating and utilizing InsurTech-oriented solutions, including IoT and blockchain concepts.

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.

“If underwriters are liberated from manual, repetitive tasks, the institutional knowledge inherent to the position can be leveraged to make machines and software-augmented risk assessment even smarter.”

The application of AI platforms, self-learning algorithms, data sciences and smart, aggressive data sourcing will inspire more efficient and cost-effective business processes. For example, if underwriters are liberated from manual, repetitive tasks, the institutional knowledge inherent to the position can be leveraged to make machines and software-augmented risk assessment even smarter. In an increasingly connected ecosystem, with streams of new, real-time data from IoT sources, past assumptions around underwriting complex specialty risks can be quickly outdated and inefficient. In the future, even more informed and personalized ways to underwrite risk will emerge.

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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