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Insurance CIO Outlook | Tuesday, November 22, 2022
Insurance companies are exploring ways to integrate artificial intelligence (AI) and machine learning (ML) into their businesses.
FREMONT, CA: Auxiliary underwriting programs can benefit from human underwriters' expertise, knowledge, and experience, although machines can perform certain underwriting tasks to speed up underwriting. What best way can underwriters engage with rapid data science and technology advancements? Underwriters must have a baseline of knowledge as companies automate parts of their underwriting programs.
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COVID-19 has disrupted traditional processes, but progress seems to be making quick strides. Data technology is undergoing many exciting changes, and its potential impact on underwriting is just beginning to be realized.
Machines cannot evaluate an application as complex as a facultative in the same way that highly skilled underwriters can. Facultative cases require in-depth knowledge of complex medical conditions and inconsistencies for risk classification and approval. The analysis of such cases often requires comprehensive knowledge and nuanced judgment only possible by human beings.
The current technology does not allow for an effective end-to-end AI-based underwriting solution. The most cost-efficient route is to find AI applications for focused areas of the process. A data scientist helps to identify an underwriter's workflow by working closely with them and examining the process, the data, and any challenges they may encounter.
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Manually reviewing imaged underwriting requirements requires both visual review and transcription, which is time-consuming for underwriters. Reading and processing information on the page requires two different brain functions - vision and brain to read the page. OCR, a form of artificial intelligence that can identify images and patterns, and natural language processing (NLP), which analyzes the text for sequences of words, can be used to perform these tasks. These AI functions facilitate the case review by converting information into a concise, easily understandable format.
AI can improve underwriting efficiency in many ways. Underwriting systems can incorporate unstructured data sources, such as questionnaires, motor vehicle records, prescription histories, and attending physician statements. Identification, locating signatures, and classifying pages make the cases easier for the underwriter to review and decide.
AI technologies can improve repetitive tasks by working with data scientists and underwriters. Underwriters can become more integral to a company's evolving strategy by learning new tools and data sources.
A successful augmented underwriting program depends on the underwriters. Underwriters are most knowledgeable about integrating data technologies into existing business processes. Data scientists may be experts in various data technologies. Underwriters and data scientists can identify and build solutions that improve the underwriting process while addressing challenges that could hinder progress.
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