The rate of transformation over the past decade has been particularly remarkable. The integration of big data and analytics, predictive models and automated underwriting has forever changed day-to-day work for today鈥檚 underwriters.
As with any change, opportunities come with risks, and subsequent trepidation. The fear of technology replacing underwriters, for example, is not a new one 鈥 the advent of both automated and accelerated underwriting sparked this now familiar concern. However, years later, we now know that underwriters and their analytical skills are more necessary than ever.
So how will artificial intelligence (AI) shape the future of underwriting? Answering that starts with defining AI and envisioning its potential.
In simplest terms, AI is the simulation of human intelligence by machines. Based on that definition, it is easy to see why concerns over job stability come to mind: If machines can simulate human intelligence, why can鈥檛 they replace underwriters?
For one, underwriting is an extremely client-centric occupation. Maintaining strong client relationships and effectively communicating decisions are key attributes of the job. AI lacks empathy and the ability to connect on a human level.
Additionally, AI鈥檚 decisioning is only as good as the data those decisions are based on. Bias and proxy discrimination remain a major concern for regulators, underwriters and consumers alike. Stakeholders need to exhibit significant caution or avoid utilizing nonmedical or nontraditional factors as inputs into AI models.
While AI will not be replacing underwriters anytime soon, or perhaps ever, AI is already augmenting and enhancing the decision-making process at many insurers. This will only increase as new digital underwriting evidence becomes more available and more standard.
AI holds great potential for automating and optimizing data-driven processes and facilitating underwriting decisions. For instance, AI can be used to sift through, normalize and de-duplicate large volumes of data to produce summaries underwriters can easily use to assess risk.
Additionally, AI can enhance predictive modeling by analyzing data to predict future outcomes. Predictive AI can provide consistent decisioning with populations for which clinical literature does not address certain correlations or is not up to date with medical advancements.
At 69色情片, we recently launched a major initiative to leverage generative AI capabilities in underwriting. The focus is squarely on providing efficiencies for the most rote tasks, freeing up underwriters to tackle more complex assignments and operate at the highest levels of their skill sets.