The insurance sector relies on the capacity to manage risk and forecast future events. While numerous organizations are already evolving to meet the anticipated demands of regulatory requirements and consumer needs, new and emerging technologies present a wealth of potential advantages for those willing to embrace this change. Integrating these technologies enhances the precision of predictions, improves customer interactions, and expands personalized services and product offerings with unmatched accuracy and speed. So, how ready is the insurance industry to utilize the latest technologies to help shape its future?
Many successful insurance companies are capitalizing on this trend. Some are adapting their product offerings and distribution methods-consider policy comparison websites, the Internet of Things (IoT), and usage-based policies. And some are making the most out of Artificial Intelligence and Machine Learning.
AI, and its subset machine learning (ML), is not a novel concept in the realm of insurance. Existing use cases of AI in the insurance industry are evident across business processes. Here are some key use cases of AI in insurance.
Underwriting Automation
Automating underwriting processes is one of the first things insurance companies pursue when exploring AI and machine learning use cases in insurance. Typically, AI and machine learning systems support underwriters by providing actionable insights derived from risk predictions performed on various data sources, from third-party data to publicly available datasets. The objective is to maximize Straight Through Process (STP) rates.
Automated underwriting processes are replacing manual underwriting across the insurance industry, and those who achieve the highest level of automation come out ahead. Numerous out-of-the-box underwriting solutions offer frictionless AI-powered automation, ready for deployment. The combination of artificial intelligence and automation helps underwriters improve efficiency, make better decisions, and enhance customer interactions.
Claims Processing
Insurance companies must carefully balance their approach to claims processing. On one hand, they need to show empathy and resolve claims swiftly with minimal stress for the policyholders. On the other hand, they must protect themselves against litigation risks and fraud while keeping costs in check. AI makes it easier to achieve these objectives through mobile applications that offer benefits to both the client and the insurer in what is traditionally viewed as a bureaucratic and impersonal process.
Additionally, new and evolving data sources are contributing to these advancements. Some examples of these newer data sources include:
- Agent-client interaction data capture (from emails, chats, etc.)
- Cloud integration (offering more storage of customer data)
- Telematics
- Sensors
- IoT devices
- Social media
AI-enabled straight-through claims processing is driving a wave of innovations that are transforming claims handling. Some tangible benefits of these data-driven AI solutions for insurance claims processing include:
More accurate claims payouts
Reduction of human error in First Notice of Loss (FNOL)
Faster claims processing
Reduction in fraudulent claims
Risk Assessment with Synthetic Geospatial Imagery
Virtual remote risk assessments present a transformative opportunity for the insurance industry. With today’s advancements in computer vision technology, this is now achievable. Visual object detection models are capable of evaluating the risks associated with a property simply by analyzing its images. These models identify features such as a pool, rooftop, or courtyard and accurately estimate the size and location of the property.
To train these computer vision systems and enhance their speed and accuracy, synthetic images are used. Additionally, AI-powered, touchless damage inspections are available for car insurers, with ready-to-deploy AI solutions for insurance.
AI-Supported Customer Service
Natural Language Processing (NLP), a branch of AI, has seen significant growth in recent years, particularly in the context of insurance customer service. Call transcripts serve as a valuable source of intelligence, enabling insurance companies to identify dissatisfied policyholders through sentiment analysis, take pre-emptive actions to prevent churn, and ultimately reduce long-term costs.
By analyzing calls for prolonged pauses, insurers pinpoint customer service representatives who may require additional training to enhance customer experience. Customer service reps also benefit from AI-generated assistance in the form of automatically created summaries of customer histories, highlighting the most critical issues that need attention.
However, using transcripts containing sensitive information to train AI systems could pose a privacy risk. To mitigate these concerns, AI-generated synthetic text substitutes the original transcripts for training purposes. Additionally, conversational AI requires a substantial amount of meaningful training data; otherwise, the resulting chatbots could harm any insurer’s reputation faster than it can be rebuilt. To address this challenge, insurers must implement rigorous data encryption protocols. It will allow them to secure sensitive information during storage and processing. This ensures that customer data is protected throughout the AI training process.
Conclusion
These varied use cases of AI in the insurance industry offer a roadmap for insurers to navigate the constantly changing landscape. From delivering personalized product recommendations and predicting claim risks and values to automating insurance workflows and enhancing customer support, AI serves as a transformative force.
The versatility of AI solutions for insurance has the potential to revolutionize numerous business areas within the insurance industry. Industry leaders are confident about AI’s role in driving cost savings and fostering business growth.
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