The Role of AI in Insurance
AI technology is being used by insurers to enhance their operational efficiency, manage risks and deliver personalized services.
- Overview
- Adoption Factors
- Applications of AI in Insurance
- Resources
Overview
AI has sparked a new era of innovation and efficiency in insurance, driven by a rapidly evolving risk landscape, unprecedented technological advancements and increasing customer demand for more personalized service. In this article, we’ll explore the dynamics of this transformation, providing an in-depth view of the factors fueling the rise of AI in insurance. We’ll also look at several practical examples of how this technology is being used by insurers to enhance their operational efficiency, manage risks and deliver personalized services.
Adoption Factors
AI-enabled solutions are helping insurers better manage risk, accelerate efficiencies and improve profitability. Several catalysts are fueling the adoption of this technology in the insurance sector.
Rapid advances in machine learning and AI technologies
The explosive growth of AI and machine learning has provided new opportunities to extract value from data. One example is large language modeling (LLM), a branch of artificial intelligence capable of intelligently processing data contained in documents. Using LLMs, insurers can accelerate their claims management workflow by processing, extracting and summarizing large unstructured text to help with claims triaging, litigation detection, claims evaluation and subrogation and claims closure. On the other hand, machine learning can help with fraud detection during first notice of loss (FNOL), predict claims complexities, support cost predictions and enhance the customer experience by predicting claims reopenings.
Availability of larger and more diverse data sources
The sharp rise in the quantity and variety of data sources means more raw material is available to mine for value. Third-party data also has taken on an increasingly important role, as insurers are supplementing their own data with consumer credit data, marketing data, social media posts, information on purchasing behavior, criminal records, past insurance claims and weather data. Third-party data helps insurers improve their policy underwriting, risk modeling, claims processing, marketing practices and more. LLMs also have enabled insurers to better ingest and transform large volumes of unstructured data, including claims data, customer interactions, legal documents, images and other reports.
Demand for enhanced customer service and experience
AI-powered solutions such as chatbots and virtual assistants are enabling insurers to meet customer demand for timely, personalized support that’s available 24/7. Natural language processing (NLP) — a type of AI that allows machines to understand and respond to written and spoken language — is powering front-line customer service solutions that provide always-on basic support to free human agents to focus on more complex customer issues. Additionally, by leveraging machine learning to power straight-through processing in underwriting and policy issuance, insurers can deliver a faster, more seamless onboarding experience, enabling their customers to quickly secure the policy that best fits their unique requirements.
Capabilities of the modern cloud data platform
Modern cloud data and AI platforms offer an efficient, affordable way to store and process massive quantities of data. With support for structured, semi-structured and unstructured data, insurers can use diverse data sets for their AI and ML model training and refinement. Elastically scalable compute power allows teams to effectively manage their AI and ML workflows.
Applications of AI in Insurance
As an early adopter of data analysis, the insurance industry is well-positioned to maximize AI’s potential. While AI has many powerful use cases for improving the insurance industry, let’s look at six that specifically highlight AI’s role in shaping the future of insurance.
More efficient claims processing
AI-powered processes streamline claims processing workflows, significantly reducing the time it takes for insurers to process and pay out claims. Artificial intelligence can analyze claim forms, documents and visual data such as images and video, and compile these into cohesive reports.
Improved risk management
ML-powered predictive models analyze vast amounts of data from many different sources, empowering insurers to forecast trends and identify potential risks with increased accuracy. These capabilities allow insurers to proactively adjust their strategies and pricing to better manage risk.
Superior customer service
NLP-enabled virtual agents and chatbots enhance customer interactions with intelligent responses to customer service queries and quick access to policy information. Around-the-clock access to customer support improves customer satisfaction and reduces the burden placed on human agents. AI is also used for sentiment analysis, the process of identifying and categorizing emotions expressed in digital text, including online reviews, social media posts and customer service chat messages. Using sentiment analysis, insurers can quickly identify and correct customer service and support issues.
Advanced fraud detection
Fraudulent property and casualty losses are a major source of lost revenue for insurers. From healthcare to business insurance use cases, AI-enabled predictive modeling and anomaly detection identify difficult-to-spot patterns and inconsistencies that can help human reviewers identify when a claim is fraudulent.
Cost savings through automation
AI-driven systems are incredibly useful for completing time-consuming manual tasks quickly and efficiently. The creation of highly automated systems reduces the opportunity for human error and frees up experienced underwriters and claims processors to focus on more complex and higher-level work. An example of this type of automation is the use of NLP to extract key information from claims documentation.
