Claims Service-as-Software Blueprint: From Strategy to Production and Optimization
This blog outlines a step-by-step blueprint for delivering a Claims Service-as-Software solution using MAS (multi-agent systems). The lifecycle spans from initial assessment and design through deployment and continuous improvement.
1. Assessment & Strategy
Business Case & KPI Alignment
Use the systems thinking from our AI Strategy approach to identify which claims metrics need improvement (e.g. cycle time, accuracy, customer satisfaction, fraud loss). Align the MAS solution with specific, measurable goals, such as raising the straight-through processing (STP) rate from X% to Y% and cutting loss adjustment expenses (LAE) by a target percentage.
Process Mapping
Map the existing claims process end-to-end. Identify high-volume workflows (simple vs. complex claims) and critical pain points where AI could deliver the most value (e.g., backlogs at FNOL, manual document review, inconsistent damage assessments).
Data Audit
Make sure an inventory of data and systems is available. Ensure the data quality and accessibility of historical claims data, policy data, and external sources (e.g. vehicle databases, geospatial data, and medical billing codes). This data supports accurate analysis and informed decision-making. MAS relies on rich data for training and real-time inference. Hence, identifying gaps (e.g., missing digital claim forms and low-quality images) and planning to address them through data consolidation and labelling is crucial for success.
Technology Review
Examine the IT landscape (legacy systems, claims platforms, cloud readiness). MAS solution will need to integrate with core systems via APIs, so architectural constraints and opportunities must be evaluated early.
Risk and Regulatory Assessment
Determine all applicable compliance requirements. Automated claims decisions are subject to high scrutiny, particularly under frameworks like the EU AI Act (where they are likely a "high-risk" application) and the UK FCA's Consumer Duty. It is essential to define ethical guardrails and human-in-the-loop (HITL) checkpoints from the outset. Cybersecurity needs must also be assessed to protect sensitive customer data.
2. Design
Define Agent Roles
Based on the process map, design a set of specialized AI agents, as described in our previous article.
Prioritize a pilot scope to prove value quickly. Typically, this involves starting with one claim type (e.g., low-complexity auto accidents) or one process slice (e.g., First Notice of Loss (FNOL) and initial triage).
Architecture Blueprint
Architect the MAS ecosystem. This includes choosing the orchestration pattern (e.g., a central supervisor agent vs. a decentralized network) and selecting the right platforms or frameworks (e.g., LangChain/LangGraph, Microsoft AutoGen, or other open-source agent frameworks) to build the agents.
Design the integration layers that will connect the MAS to document storage, policy databases, CRM systems, and other relevant applications, ensuring each agent’s input and output formats are clearly defined.
Security & Governance Model
Embed security into the design from day one, including authentication between agents and encrypted data flows. Define the governance framework, including detailed logging, audit trails for each agent's decisions, and performance monitoring dashboards. Incorporate ethical AI requirements, such as automatic triggers for human review for high-risk or high-value choices.
Proof of Concept (PoC)
Design and build a small-scale PoC to test the core concept. This could involve, for example, an intake agent processing sample claims data and a document agent extracting key details. The PoC is used to evaluate technical feasibility, refine agent definitions, and build stakeholder confidence.
3. Development & Integration
Iterative Agent Development
Develop each agent using modern AI tools and agile methodologies. Document agents may use LLMs fine-tuned on insurance documents, replacing older, less flexible Optical Character Recognition/Natural Language Processing (OCR/NLP) models. Decision agents will utilize a combination of rule engines and predictive models (e.g., claim approval classifiers) to make informed decisions. For agents that interact with customers or explain decisions, fine-tuning or advanced prompt engineering on claims-specific dialogues is critical. Each agent must be tested individually before being integrated into the broader system.
Workflow Orchestration
Implement the control flow that connects the agents. This is often a state machine or a workflow engine that routes a claim through the agents in the correct order. For example, once the intake agent completes its task, it triggers the fraud agent and the document agent to run in parallel. The orchestration layer must be robust enough to handle these parallel branches and manage dependencies.
Data Integration
Connect the MAS to essential data systems. This involves building real-time or near-real-time API connections to policy databases, allowing the coverage agent to fetch policy terms or external data providers for vehicle valuation or weather data. Middleware may be required to connect the modern MAS layer to legacy core systems.
Compliance Controls
Before any rollout, ensure all compliance controls are hardened. This includes embedding explainability features (e.g., an agent providing a rationale or logging its reasoning steps), implementing bias checks (testing agents on diverse demographics and claim types), and building in manual audit hooks for regulators and internal auditors.
4. Testing & Validation
Functional Testing
Verify each agent and the end-to-end workflow under a wide range of controlled scenarios. Use historical claims data to test the accuracy and performance (speed, correctness) of the system's decisions against known outcomes.
Regulatory Validation
Review the system against all relevant regulations. For example, simulate decisions and check if the generated explanations meet transparency requirements. Validate that data privacy controls adhere to GDPR or other local data protection rules.
User Acceptance Testing (UAT)
Have human claims handlers review the MAS outputs, including decisions, communications, and recommendations. Collect their feedback to adjust agent logic or add human-in-the-loop (HITL) steps where the system has not yet achieved the desired quality benchmarks.
5. Deployment & Rollout
Phased Launch
Do not attempt a "big bang" launch. Start with a limited subset of claims (e.g., low-complexity, low-value auto claims) in a single region or business unit. Monitor the system's performance closely in the production environment. Gradually expand to more claim types or higher volumes as confidence in the system grows.
Monitoring & Feedback Loops
Constantly supervise key performance indicators (KPIs) like cycle time, STP rate, customer satisfaction (NPS), and cost per claim. Use real-time dashboards to catch any issues, such as an agent starting to misclassify a new type of claim. Implement feedback loops that allow human adjusters to flag when the MAS has made an error easily and use this data to retrain and continuously improve the agents.
6. Ongoing Optimization
Learning and Improvement
Periodically retrain or fine-tune agents on new claims data, adjusting for seasonality or emerging patterns (like new fraud schemes). Consider reinforcement learning for agents that can improve with rewards (e.g., a claims settlement agent optimizing for minimal payouts without unjust denials).
Scaling and Evolution
As the MAS matures, new agents or capabilities are introduced. For example, add a predictive agent that forecasts claim durations or a cross-sell agent that can offer products to satisfied claimants. To enhance agents, continuously evaluate emerging AI tech (e.g., multimodal vision–LLM models).
Governance Iteration
Update governance and compliance measures as regulations evolve. Review that the MAS stays aligned with business objectives. Conduct regular “AI ethics audits” to ensure fairness and compliance in decisions.
Learning and Improvement
Periodically retrain or fine-tune agents on new claims data to adapt to seasonality, emerging fraud schemes, or changes in customer behavior. Consider using reinforcement learning for agents that can improve with direct feedback, such as a claims settlement agent rewarded for optimizing payouts without unjust denials.
Scaling and Evolution
As the MAS matures, introduce new agents or capabilities to expand its value and enhance its capabilities. For example, add a predictive agent that forecasts claim durations to improve resource planning, or a cross-sell agent that can identify and offer relevant products to satisfied claimants. Continuously evaluate emerging AI technologies (e.g., new multimodal vision-LLM models) to enhance agent capabilities.
Governance Iteration
Regularly update governance and compliance measures as regulations evolve. Conduct periodic "AI ethics audits" to ensure fairness, transparency, and continued alignment with business objectives and societal expectations.
Conclusion and Next Steps
This blueprint provides a systematic and risk-managed pathway for transforming the claims function from a manual cost center to a highly efficient, automated Claims-as-Software service. Encora’s experts are equipped to work closely with insurers throughout this lifecycle to manage project governance, the technical build, and crucial change management. The MAS architecture and workflow steps outlined are adaptable globally, irrespective of an insurer’s lines of business or region. The following blog highlights Encora’s unique strengths in executing this blueprint, even in this pioneering space where established case studies are still emerging.
Ready to modernize your claims function? Connect with our experts today to explore how this blueprint can be tailored to your organization’s needs and accelerate your transformation journey. Contact us to learn more.
Further reads
This article is part of a series dedicated to the application of Multi Agent AI systems in claims.
Part 1: Transforming Claims with “Service-as-Software”
Part 2: Multi-Agent Systems – The Engine of Claims Automation