The insurance industry has long been data-intensive, relying on vast amounts of information to assess risks, set premiums, and process claims. Now, next-gen technologies are enabling close to real-time delivery of large, complex data sets known as ‘big data’. Simultaneously, quality data is increasingly seen as the connective tissue of the insurance ecosystem and has shown to be critical to effectively leveraging artificial intelligence (AI). Instead of functioning independently, big data, analytics, and AI are converging into a unified strategy.
According to Deloitte, “insurers’ AI and analytical capabilities are only as strong as the underlying data sources supplying it.”1 So, it’s not surprising that recent Celent research signifies that data and analytics is a consistently strong focus area for P&C insurers, with more than 62% of carriers acting on data initiatives in 2024.2
The coalescence of big data, advanced analytics, and AI is driving modernization, enabling efficiency, and enhancing customer experiences. Data-driven innovation is at the forefront of revolutionizing the industry, unlocking new opportunities, and reshaping traditional practices:
One of the most significant impacts of data-driven innovation in insurance is the enhancement of risk assessment. Traditional underwriting relied heavily on historical data and manual assessments. But today, insurers have access to vast amounts of third-party data that provide deeper insights into risk factors and allow for more accurate and dynamic underwriting.
Big data analytics enables insurers to collect, process, and analyze large, complex datasets to identify patterns and trends that were previously undetectable. Predictive analytics, powered by machine learning algorithms, allows insurers to forecast future risks with greater precision. For instance, telematics data from connected vehicles provides real-time information on driving behavior, enabling insurers to tailor premiums based on individual risk profiles. Similarly, IoT devices in homes can monitor for potential hazards like water leaks or fire risks, allowing for proactive risk management and prevention.
Artificial intelligence (AI) and machine learning (ML) are revolutionizing underwriting by automating and optimizing the risk assessment process. These technologies can analyze complex data sets at speeds and accuracies beyond human capabilities. AI-driven algorithms evaluate various risk factors and provide underwriters with more precise risk scores, leading to better pricing models and risk mitigation strategies.
AI and ML are automating many aspects of claims processing – handling initial claims intake and assessing the damage through image recognition. This not only speeds up the claims process but also reduces the potential for errors and fraud. For instance, AI can cross-reference claims data with historical patterns and external databases to flag suspicious activities, mitigating fraud risk and saving insurers significant amounts of money.
Data-driven innovation and predictive analytics enable insurers to anticipate customer needs and provide proactive support. During natural disasters, insurers can use real-time data to identify affected policyholders and initiate the claims process proactively. Moreover, AI-powered chatbots and virtual assistants can provide 24/7 support, guiding customers through the claims process and addressing their queries promptly, to create a more seamless and satisfying experience.
By analyzing customer data, insurers can gain insights into individual preferences and behaviors, allowing them to offer customized insurance products. For example, data from wearable devices can provide information about a customer’s lifestyle, enabling insurers to tailor health and life insurance products accordingly. This level of personalization enhances customer satisfaction and loyalty, as policyholders feel their unique needs are being met.
Data analytics also allows insurers to communicate with customers more effectively. Tailored communication strategies, powered by predictive analytics, ensure that customers receive relevant information and offers at the right times. For instance, insurers can send individualized alerts and reminders about policy renewals, coverage options, and risk mitigation tips based on customer profiles. This targeted approach not only improves customer engagement but also fosters long-term relationships.
To remain on the forefront of data analytics trends and further advance their predictive capabilities, Nationwide forges strong partnerships with next-gen technology companies.
Two of these partnerships include:
Liberty Mutual5 has acquired over 200 million data points from processing more than 5 million claims. This extensive volume of data enables the insurer to better leverage AI to improve risk assessment and create industry-leading predictive claim models.
Additionally, the insurer partnered with MIT in a $25M, five-year collaboration in 2019 to support AI research in topics that included computer vision, data privacy and security, and risk-aware decision making.6 And in March of 2024, Liberty joined MIT’s newly formed working group on ‘Generative AI and the Work of the Future’. Liberty Mutual CIO Adam L’Italien explained: “In a year of extraordinary advancements in AI, there is no doubt that it will continue shaping the future — and the future of work — at a rapid pace.”7
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