はじめに
人工知能（AI）は、特に保険金請求管理の分野で、保険業界を急速に変え、様々なプロセスを統合し、効率を改善しています。自社の保険金請求部門にAIを実装することで、効率および生産性の大幅な改善をもたらし、最終的に、費用の削減および最適化された結果につながる可能性があります。本ガイドでは、自社の保険金請求部門にAIを採用するためのロードマップを提供し、AIのパワーを利用する手助けをします。
Introduction
Artificial intelligence (AI) is rapidly transforming the insurance industry, streamlining processes, and improving efficiency, particularly in claims management. Implementing AI within your claims organization can yield significant improvements in efficiency and productivity, ultimately leading to reduced costs and optimized outcomes. This guide offers a roadmap to adopt AI in your claims organization, helping you to harness the power of AI.
Step 1: Identify pain points and opportunities
Before implementing AI solutions, it is crucial to understand the challenges your organization faces and where AI can bring the most value. While not an exhaustive list, here are several key areas where AI can improve claims processes:
- Claims triage, for example prioritize potential high-cost claims
- Fraud detection, for example identify anomalies suggestive of fraud
- Document processing (OCR) and data extraction (NLP), for example data extraction from unstructured, freeform text data such as adjuster notes
- Loss estimation, for example estimated repair costs
- Claimant experience and communication, for example support chatbots
- Claims leakage, for example find instances where costs exceed industry benchmarks
- Legal spend management, for example identify best performing defense firms
- Provider management, for example identify best performing providers in workers’ comp
- Generative text, for example using ChatGPT, or similar tools, to draft correspondence
Step 2: Set clear goals and objectives
Establish clear, relevant goals and objectives for AI adoption in your claims organization. Ensure that these goals align with your organization’s broader objectives and strategic vision. Make sure these goals are quantifiable, measurable, and specific, as this will enable you to effectively evaluate success and monitor progress. Many organizations struggle to assess the impact of AI due to confounding variables, which can complicate measurements and obscure the true cause and effect relationship. Measuring ROI, for example, requires deep knowledge of both AI and insurance to adjust for confounding variables.
Step 3: Batch versus stream processing
When adopting AI, it is essential to determine the most suitable approach for a given use case: batch processing or stream processing. Understanding the differences between the two will help you choose the right method for your organization's needs.
Batch processing:
- Involves processing large sets of data at scheduled intervals (such as daily, weekly, monthly, etc.).
- Suitable for tasks that do not require instant, real-time insights.
- Typically, more straightforward to implement and maintain, as data is processed in a predictable manner.
Stream Processing:
- Involves processing data in real-time as it is generated or received.
- Suitable for tasks that require immediate action with a focus on low latency.
- Can be more complex to implement and maintain, as data processing needs to be efficient, scalable, and resilient to handle continuous data streams.
The appropriate approach for your claims organization depends on the specific use case, requirements, and nature of the data at hand.
Step 4: Evaluate AI solutions and vendors
Explore various AI solutions and vendors available in the market, considering factors such as:
- Compatibility with your existing systems
- Customization and scalability
- Proven track record in the insurance industry
- Security and data privacy compliance
- Post-implementation support and maintenance
Step 5: Build a team
Depending on whether you build or buy an AI system, you may need some or all of the following:
- Data Engineers to build and maintain data pipelines
- Data Scientists to research and develop models
- MLOps Engineers to deploy and maintain models
- Software Engineers to develop user interfaces and integrate systems
- Project Managers to supervise resources and progress
- Quality Assurance (QA) Engineers to ensure quality
- Business Analysts to translate business needs into technical requirements
- SMEs such as Claims Professionals, Clinicians, Actuaries, and other specialists
Building an AI solution in-house requires a multidisciplinary team with a range of expertise. Although there are parallels with traditional software development, AI development requires specialized skills and presents its own set of challenges. Filling these roles can be difficult and costly given the high demand for such expertise.
Step 6: Build an infrastructure
AI in research is significantly different than in production. In practice, several complex systems work in parallel to create a data pipeline. These interconnected components form the backbone of a robust data pipeline:
- Data Storage to collect and store structured and unstructured claims data
- Data Processing to transform, clean, and process data for downstream tasks
- Compute Resources either on-premises hardware or on the cloud
- Model Management to manage and version models
- Data Security to protect sensitive information and ensure compliance with privacy regulations
By establishing a robust infrastructure, claims organizations can ensure the reliable, scalable, and maintainable deployment of AI solutions. If selecting a vendor, it is important to evaluate their offerings based on these criteria to ensure a successful implementation.
Step 7: Implement the AI solution
Before fully committing to an AI solution, develop a proof of concept (PoC) to test its effectiveness in addressing your organization’s goals and objectives. Select a representative sample of claims to test the AI solution and evaluate its performance.
Once the PoC is successful, proceed with implementing the AI solution. This involves:
- Developing an implementation plan, including milestones
- Establishing data pipelines to automate the flow of data to the AI solution
- Integrating the AI solution with your existing claims systems
- Conducting thorough testing to ensure functionality and compatibility
- Training users on the AI solution’s capabilities
- Deploying the AI solution to ensure minimal disruption to workflows
Step 8: Monitor and iterate
Continuously monitor the performance of the AI solution against your organization’s established goals and objectives. Monitoring should also include the AI models to ensure continued performance. Gather feedback from users to identify areas for improvement. Adjust the AI solution as needed to maximize its value and ensure adaptability to evolving business needs and market conditions. As new technology and AI models emerge, your internal AI team and/or vendors should leverage new tools or technology to improve the AI solution’s functionality. The effort to ensure continuous success does not stop once an AI solution is successfully deployed.
Conclusion
Adopting AI in your claims organization can lead to substantial improvements in efficiency and productivity. The steps outlined in this guide provide a roadmap for organizations to identify opportunities, establish goals, evaluate solutions, and successfully implement AI in their claims processes. By assembling a skilled team, building a robust infrastructure, and continuously monitoring and iterating on the AI solution, your claims organization can remain agile and competitive in an ever-evolving industry landscape.