To evolve into the insurer of tomorrow, insurance has to transition from its reactive state of ‘identify and repair’ to a proactive ‘foresee and prevent’ approach. And it is artificial intelligence (AI) that has emerged as the key differentiator in this transformation. AI isn’t new in insurance with various use cases evident in processes like data forecasting, risk modeling, and claims handling.
However, relying on legacy systems and holding on to traditional insurance products can make it difficult for insurers to keep pace with emerging customer lifestyles. An Accenture survey revealed that underwriters dedicate 40% of their time to non-core and administrative tasks. AI offers a valuable opportunity to minimize the time underwriters spend on manual processes, administrative tasks, and redundant data entry. AI technologies offer an abundance of potential opportunities and will likely force innovation in many areas of insurance.
How AI is Transforming Insurance Underwriting?
Traditional underwriting has long posed challenges for insurance firms due to time-consuming decision-making and data analysis processes. AI is transforming various aspects of the underwriting process including managing customer applications, streamlining risk assessment, improving casework management for underwriters, and improving communication with policyholders.
The integration of deep learning models and machine learning automates much of the underwriting process. These models are powered by internal and external data and aggregated into a variety of data repositories that can enable underwriters to make decisions based on predictive analysis and take a proactive approach.
AI empowers underwriters to quickly extract data, automating the collection and analysis process to accelerate the time it takes to generate a quote. AI-powered underwriting algorithms can also sift through unstructured data to uncover patterns that may have otherwise gone unnoticed, and the algorithms can be trained to analyze and assess the risk profiles much more quickly. AI and ML can also construct pricing models for policies based on client attributes and risk variables. Efficient pricing models are all largely dependent on data analytics and intelligent automation solutions.
Benefits of Using AI in Insurance Agencies
A Capgemini report finds that 62% of executives recognize AI/ML is elevating underwriting quality and decreasing fraud. The rapid adoption of AI is due to the number of benefits it delivers, including:
- Reduce human error: By automating data entry, insurers can minimize the risk of manual errors. Enhanced data analysis can identify patterns that users might have overlooked, preventing errors in risk evaluation and pricing.
- Decrease time to quote: AI can reduce the turnaround time for underwriting quotes through automated data extraction and analysis.
- Eliminate manual processes: AI takes automation a step further by skipping standard workflow steps and providing recommendations for the next best actions, reducing manual interventions.
- Boost customer satisfaction: AI can extract conversational insights from data sets so insurers can offer tailored offerings. It can also develop long-term retention roadmaps based on the data.
- Improve risk assessment: Algorithms, big data, and predictive analytics can quickly process vast amounts of data to identify patterns, provide accurate predictions, and uncover potential risks.
The Bottom Line
Integrating AI in underwriting enables insurers to streamline operations and make precision-driven decisions. Newgen’s AI-enabled underwriting workbench is a comprehensive platform that transforms a wide range of underwriting functions, providing users with access to all the essential tools, data, and insights in one place. The intelligent, user-friendly platform streamlines workflows and enhances the accuracy of underwriting decisions.
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