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Insurance CIO Outlook | Tuesday, April 23, 2024
New applications of advanced analytics techniques are improving customer service, streamlining claims, and optimizing policy prices.
FREMONT, CA: Using advanced analytics tools and techniques, insurance companies are exploring new solutions for improving policy pricing, enhancing claims processes, and providing better customer service. Insurtech providers compete against companies using insurance analytics to power innovation, while companies that have yet to implement a data-driven approach to their business workflows are disadvantaged.
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As a result of the current dynamic risk environment and the threat of industry disrupters such as InsurTech, insurers face a challenging environment. Here are some of how insurance analytics is helping modern insurers address these challenges and stay competitive:
Commercial underwriting: By leveraging third-party data, commercial insurance underwriting can be made more profitable and efficient. Insurance companies that offer commercial policies now have access to information about applicants' risks. For example, geospatial analytics and ESG data can enable underwriters to better understand natural disaster risks for homeowner properties and certain agricultural businesses. To determine how much risk the insurer should take on, actuarial teams use risk insights in the actuarial workflow.
Analytics tools: Decision-makers can better understand where to focus their resources to improve customer service by tracking the prevalence of certain customer service requests with analytics tools. Computing demographics, credit scores, mobile app usage, current insurance products, and insurance analytics data can also create a 360-degree view of customers. Insurers can increase the effectiveness of cross-selling and upselling opportunities by developing a more in-depth understanding of their customers, which helps them retain customers and increase the relevance of their recommendations.
Automating claims: The cost of manually processing claims is high for insurers and frustrating for customers waiting long periods. With intelligent document processing and machine learning (ML)-enabled adjudication and fraud detection, insurance analytics can automate large portions of the claims process and allow self-service claims submission. By reducing touchpoints and speeding up payouts, insurers can increase customer experience while reducing operational costs.
Fraud: Insurers face a significant problem with fraudulent claims. Predictive analytics tools and techniques can accurately assess the likelihood of fraudulent claims using data from internal business sources and third-party sources. These algorithms can automatically flag claims that are likely to be fraudulent by combining internal data, like the frequency of past claims, with data from third parties, such as credit agencies.
Real-time data: Insurers can optimize policy pricing to maintain profitability with insights based on near real-time data. Insurers can better understand their level of risk by monitoring factors like construction material costs, labor costs, and the prevalence of similar claims in the vicinity, enabling them to price their policies more accurately.
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