Introduction
Artificial Intelligence in Insurance Industry is revolutionizing how companies assess risk, handle claims, and enhance customer experiences. AI-powered tools can analyze vast amounts of data to detect fraud, optimize underwriting processes, and automate customer service. “The KPMG 2023 Insurance CEO Outlook also highlights a significant degree of trust in AI with 58 percent of CEOs in insurance feeling confident about achieving returns on investment within five years.” The numerous ways in which AI has become a part of the insurance industry are given below.
Use Cases
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1. Decode outdated COBOL or PL/1 software
In developed countries, many medium and large insurance companies still rely on outdated COBOL or PL/1 software, which has significant limitations. AI systems are now employed to decode these programs by training on input and output data, achieving over 98.5% accuracy. This process reduces manual labor by 75% and prevents errors from being carried over during conversion to modern systems, ensuring a smoother transition. For more details, visit www.scryai.com/datatio.
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2. Offer tailored insurance plans
AI techniques are applied across various insurance types to optimize pricing, manage claims efficiently, and enhance customer satisfaction while cutting costs. Insurance companies group customers with similar characteristics and behaviors to assess risk profiles accurately and offer tailored insurance plans with appropriate coverage and premiums. This approach ensures that customers receive fair pricing based on their specific risk factors.
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3. Data conversion, extraction and organization
Life insurance policies often span over thirty years, with much of the data stored in hard-to-read paper formats. Converting this data to electronic format is labor-intensive and costly, and traditional OCR systems are ineffective. Therefore, companies use advanced Computer Vision and AI techniques to extract and organize data chronologically. This enables AI systems to handle over half of the claims with minimal human involvement, significantly reducing costs and labor. These AI-based Intelligent Document Processing (IDP) systems ensure accurate data extraction and organization, streamlining the claims process.
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4. Estimate vehicle damage from accidents
Insurance companies leverage Computer Vision and Natural Language Processing (NLP) to estimate vehicle damage from accidents. AI tools analyze photos of claims to quickly assess damage and provide repair estimates, which are then sent to insurers for approval and customer confirmation. Some companies are extending these AI systems to automate the entire claims process, ensuring faster and more accurate damage assessments and repair estimates, ultimately improving customer satisfaction and operational efficiency.
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5. Accelerate the claim submission process
AI systems streamline and expedite the claim submission process. Users complete a basic questionnaire and submit accident photos, after which they receive AI-generated quotes and claims. This allows users to quickly and easily choose the option that best fits their needs, enhancing the overall customer experience and reducing the time and effort required for claim submissions.
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6. Assist underwriters in accurately assessing risks
Similar to the banking sector, insurance companies are utilizing AI to assist underwriters in accurately assessing risks. Some AI systems can automatically process over 50% of simple claims, forwarding only the more complex cases to underwriters. This improves efficiency and allows underwriters to focus on more challenging assessments, ultimately enhancing the accuracy and speed of the underwriting process.
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7. Match customers with the best coverage
Healthcare companies are leveraging AI to make purchasing health insurance easier. Customers start by filling out a short form with details such as age, health history, and benefit preferences. The AI system then matches each individual or group with the most suitable benefits plan, often providing several options for customers to choose from. This personalized approach ensures that customers receive the best possible coverage based on their specific needs and circumstances.
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8. Improve communication between customers and insurance providers
Hi Marley utilizes a comprehensive cloud platform to enhance communication between customers and insurance providers. The platform is equipped with AI features that help customer service representatives operate more efficiently. For instance, Hi Marley’s platform can translate text into different languages and provide real-time coaching to improve interactions between representatives and customers. This ensures faster and more effective customer service.
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9. Quick and accurate responses to customer queries
Similar to banks, insurance companies are training open-source Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) on internal data. This enables customer representatives to quickly and accurately respond to customer queries, improving the overall efficiency and effectiveness of customer service.
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10. Detect fraudulent claims
AI systems are employed to detect fraudulent claims, which are prevalent in the insurance industry and result in higher premiums for honest customers. Life insurance fraud is particularly widespread, costing companies $74.7 billion annually, with approximately 15% of claims being fraudulent. Policyholders also commit $35.1 billion in fraud each year by lying on their insurance applications to get better rates. AI systems are integrated into decision support systems to identify potentially fraudulent claims, which can then be thoroughly investigated by insurance company analysts.
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11. In-depth assessments of properties
AI and Data Science are utilized to conduct in-depth assessments of over 100 million properties, taking into account variables such as wind direction, proximity to flood zones, wildfire zones, and highways. These systems provide valuable insights to insurance companies, homeowners, and local fire departments, especially in cases of vegetation overgrowth that may lead to wildfires. Customers can take pictures and short videos, which AI systems analyze using Deep Learning Networks and Computer Vision techniques to assess potential risks. This helps insurance providers determine coverage and allows homeowners to understand what actions they can take to lower their premiums.
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12. Identify risks associated with potential customers
Life insurance companies, similar to banks and diversified financial industries, are leveraging AI to understand risks associated with potential customers and to reach new customers. They use both traditional and non-traditional data, such as AI-based credit scoring models, to make better predictions. The primary goal is to help companies boost underwriting profits while mitigating risk, ensuring more accurate and profitable underwriting decisions.
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13. Create unified data related to property, claims, and risk management
Insurance companies are utilizing Large Language Models (LLMs), Computer Vision systems, and detailed property reports to expedite the underwriting process. These AI systems offer underwriters a holistic view of unified data related to property, claims, and risk management. This comprehensive perspective enables underwriters to deliver more accurate and efficient assessments, improving the overall underwriting process.
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14. Pair customers with the right customer service representatives
AI systems, particularly Large Language Models (LLMs), utilize past data to pair customers with the most suitable customer service representatives. This approach enhances customer satisfaction by improving the quality of conversations, as customers are matched with representatives based on the best fit rather than call order. This personalized matching ensures more effective and satisfactory customer interactions.
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15. Deliver personalized customer service
AI assistants, powered by AI systems, deliver efficient and personalized customer service. These AI assistants help customers find answers to their questions and locate critical documents through chatbots or onscreen avatars that engage in human-like conversations with insurance agents. This technology enhances the customer experience by providing quick and accurate assistance.
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16. Detect and prevent risky behavior in drivers
AI systems are being developed to mitigate distracted driving. Equipped with cameras, Computer Vision, and proprietary algorithms, these systems monitor how drivers interact with their vehicles and the road. They can detect and prevent potentially risky or sleepy behavior in real time, enhancing road safety and reducing the likelihood of accidents caused by distracted driving.
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17. Assess the level of risk associated with insurance policies
Collect internal and external data, including market information and individuals’ medical data, to assess the level of risk associated with insurance policies. This data is used to calculate the expected internal margin, ensuring that policies are priced accurately and reflect the true level of risk. This comprehensive data collection helps insurance companies make informed decisions and manage risk effectively.
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18. Decision support for pricing insurable risks
Assess product types, medical information, and continuous variables (e.g., weight, height, employment salary) to provide decision support for pricing insurable risks. Suggest the likelihood, margin level, and competition risk associated with the suggested pricing. Perform sensitivity analysis to identify the variables that impact the revenue generated by the policy. This comprehensive assessment ensures accurate pricing and helps insurance companies understand the factors influencing their revenue.
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19. Enhance technical pricing and loss prediction modeling
Enhance technical pricing and loss prediction modeling by utilizing non-linear models and external data to develop the most accurate AI models. Use these AI models to create explainable or interpretable models that act as “surrogates” and can satisfy local government requirements. This approach ensures that the AI models are not only accurate but also transparent and compliant with regulatory standards.
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20. Process scanned or PDF claim documents
Utilize IDP to process scanned or PDF claim documents, matching their details, value, and complexity with existing records in the database. This enables real-time approval or rejection of claims, streamlining the claims processing workflow.
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21. Instantly assess uploaded claims
Leverage IDP and AI models to assess uploaded claims instantly, evaluating them for potential fraud and eligibility against set criteria. This ensures quick and accurate claim assessments, enhancing fraud detection and eligibility verification.
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22. Determine the eligibility for a claim using a picture
In real-time, determine the eligibility, insurance coverage, and payout for a claim using a picture, like a damaged car. This is done using an algorithm trained on a large dataset of images showing different damage levels and their corresponding payouts. The claims management process is further supported by an electronic workflow that handles task routing, prioritization, monitoring, and escalation of exceptions.
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23. Direct denied claims into a designated workflow
Direct denied claims into a designated workflow, which automatically sends an email to the client detailing the reasons for denial and suggesting possible remediation actions. This ensures clear communication and guidance for clients on how to address the denial.
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24. Early prediction of claim severity
Early prediction of claim severity allows for efficient triage and handling, including scenarios like total loss, subrogation, or litigation. This also helps in predicting the necessary reserves, ensuring better resource allocation and management.
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25. Enhance the onboarding process
Enhance the onboarding process by using an automated workflow that handles task routing, prioritization, monitoring, and escalation of exceptions. This ensures a smooth and efficient onboarding experience for new clients.
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26. Gather client data
Enrich client profiles by gathering data from social media and other public web sources. Use workflows to guide clients through the onboarding process and help them select appropriate services. Additionally, leverage LLM-based chatbots to assist clients in choosing the right policies, enhancing their overall experience.
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27. Ensure secure client connections
Ensure secure client connections using voice recognition and other biometric authentication methods. Address customer queries related to services, transactions, and account details through a self-service digital platform that utilizes LLM-based chatbots, providing quick and efficient support.
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28. Predict customer churn
Use demographic and transactional data to identify variables that most accurately predict customer churn. Define profiles of churners, determine the root causes driving churn, and develop strategies to prevent it. This includes creating tailored campaigns and offers delivered through the most effective channels to retain customers.
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29. Create customer categories and analyze consumption patterns
Analyze transactional and demographic data to create detailed customer categories and understand their consumption patterns. Utilize AI-based recommendation systems to personalize product offerings and promotions, thereby enhancing clients’ lifetime value and driving business growth.
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30. Identify the next insurance product
Analyze customer data, including services and products used, demographic information, transactional behavior, and contact history, to identify the next insurance product to offer. This targeted approach helps in meeting customer needs more effectively and increasing sales opportunities.