A careful assessment of trade-offs between speed and quality of insights from AI-driven risk data platforms can lead to more effective risk evaluation and better outcomes for both insurers and policyholders.
To meet policyholder expectations, the insurance industry is increasingly focused on speed and efficiency in underwriting processes. While speed is crucial, accuracy in risk evaluation is equally important. Consider this scenario: an underwriter at an insurer uses an automated data-delivery solution to prefill underwriting questions. The data solution can populate answers in five seconds, but when the underwriter reviews the prefilled answers, they notice there are errors or inconsistencies. The underwriter then must spend hours doing manual research to find the correct information.
Cognitive technologies can help speed up the underwriting process, but insurance organizations must ensure that they not only deliver information fast but that it is also accurate. To identify technologies that can help, insurance organizations should ensure the technology is built for continuous improvement and adaptable to changing market demands and data requirements. Compliance with data privacy regulations, such as California’s Consumer Privacy Act and the proposed American Data Privacy and Protection Act, must also be a priority.
Insurers should also consider the trade-offs between speed and the quality of data received. Sometimes, a slightly longer turnaround of data can lead to better risk insights and results. When partnering with technology companies, insurers should carefully evaluate not only the speed of risk data delivery but also the quality, precision, and sourcing of data insights to enrich risk-assessment workflows. The best insurer/vendor partnerships for underwriting data are symbiotic, where vendors help advise on how to improve underwriting workflows with up-to-the minute data. At the same time, insurers can provide guidance for solid risk questions and feedback so vendors can improve their risk-quality data solutions.
To achieve the right balance of speed and accuracy, insurers can leverage intelligent data-acquisition technology powered by generative AI, machine learning and Large Language Models (LLM). They should be mindful of compliance with data privacy regulations and seek out partners that offer versatile and principled solutions. A careful assessment of trade-offs between speed and quality of insights from AI-driven risk data platforms can lead to more effective risk evaluation and better outcomes for both insurers and policyholders.
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