Transforming Claims for MGAs with Multi-Agent Systems

By Marc Hollyoak

In 2025’s rapidly changing insurance tech landscape, multi-agent AI systems are transforming claims processing like never before—faster, more efficient, more accurate, and more customer-satisfied. As MGAs look to differentiate themselves in an increasingly competitive market, these advanced technological ecosystems are becoming essential, not optional.

Encora, the specialist global digital engineering company with a cloud-first, data-first, and AI-first approach to Insurance, helps MGAs drive efficiency, market expansion, and differentiation. The divide between tech-enabled MGAs and traditional players is widening, creating significant opportunities for those embracing digital transformation. With nearshore agility in Europe and expertise-at-scale from Latin America and India, Encora delivers targeted value across the P&C, Specialty, and Life insurance value chain.

From Single-AI to Multi-Agent Systems

Traditional claims processing involved manual or single AI tools performing specific tasks. Today’s multi-agent systems are a big leap forward—integrated networks of specialized AI agents working across the complete claims journey. Each agent has a specific capability, such as document analysis, fraud detection, customer communication, or settlement calculation.

The key differentiator of multi-agent systems is that they can coordinate complex workflows autonomously while maintaining human-like reasoning. According to a recent study published in the Open Journal of Business and Management,  a multi-agent claims system has reduced processing time from 1-2 days with human methods to 40 seconds and achieved a 30% improvement in accuracy over monolithic AI systems.

The Building Blocks of Multi-Agent Claims Systems

Modern multi-agent claims architectures have several specialized AI entities, for example:

• Intake Agents: Process claims notifications across multiple channels, extract key info, and verify policy coverage
• Documentation Agents: Analyse submitted materials – photos, videos, pdfs, spreadsheets, forms – to validate claims and identify missing info
• Fraud Detection Agents: Evaluate claims against historical patterns and known fraud indicators using advanced anomaly detection
• Damage Assessment Agents: Analyse visual evidence and repair estimates to determine settlement amounts
• Communication Agents: Engage with claimants through natural language, provide updates and answer questions
• Orchestration Agents: Coordinate the entire process, assign tasks, resolve conflicts and ensure regulatory compliance

This way, each domain has deep expertise and seamless coordination across the claims’ lifecycle.

Why it’s Happening Fast

The insurance industry is adopting multi-agent claims systems for several reasons:

Better Accuracy and Consistency: Multi-agent systems reduce human error and consistently apply claims policies and procedures. Using standardized evaluation criteria and maintaining detailed audit trails of decision-making, these systems minimize outcome variation for similar claims, which is fairer and reduces regulatory risk.

Scalable Processing Capacity: Unlike human adjusters or single-AI systems that get overwhelmed during catastrophes, multi-agent systems can scale dynamically to handle surge volumes. During recent natural disasters, MGAs with multi-agent systems could maintain standard processing times even as claim volumes increased compared to 3-4 week backlogs with traditional methods.

Continuous Improvement Through Specialized Learning: Each agent within the system can continuously improve its specific function through focused machine learning, creating compounding benefits. For example, damage assessment agents analysing thousands of vehicle crash images become increasingly precise in estimating repair costs, while fraud detection agents improve at pattern recognition with each claim.

Better Customer Experience: Most importantly, multi-agent systems change claimant experience. With 24/7 availability, transparent communication, and faster resolution, these systems can deliver customer satisfaction scores 30% higher than traditional claims processes[i]. Modern systems can provide instant initial response, regular proactive updates, and human-like interaction that makes claimants feel heard.

Implementation Challenges and Solutions

Despite the obvious benefits, implementing multi-agent claims systems also has challenges:

Integration Complexity: Connecting multi-agent systems to legacy claims platforms, policy admin systems and external data sources requires sophisticated integration strategies. Successful implementations use API-first architectures and middleware solutions designed for insurance.

Balancing Automation and Human Judgment: Deciding what to automate and leave to human expertise is tough. MGAs can adopt hybrid approaches, where multi-agent systems handle the routine and seamlessly escalate complex decisions to human adjusters with context and recommendations.

Ensuring Explainability and Compliance: As regulatory scrutiny of AI in insurance increases, multi-agent systems must explain their decisions. Advanced implementations now include compliance agents that document reasoning pathways and generate human-readable justifications for all determinations.

Future Directions: What’s Next

This year several trends will be shaping the future of multi-agent claims systems:

Cross-MGA Collaboration: Industry consortiums will explore collaborative multi-agent networks that can share fraud patterns and claim insights across MGAs while protecting proprietary information, potentially saving the industry billions in fraudulent claims.

Predictive Resolution Pathways: Next-gen multi-agent systems are starting to predict the best resolution strategy from the first notice of loss, dynamically adjusting processing based on claim characteristics and claimant profiles to maximize satisfaction and minimize expense ratios.

Embedded Subrogation Intelligence: Advanced systems now have agents dedicated to identifying and pursuing subrogation opportunities throughout the claims process, recovering funds that would otherwise be lost.

Conclusion: A Strategic Imperative for Competitive MGAs

By the end of 2025, multi-agent claims systems will have moved from experimental to strategic. MGAs implementing these systems will significantly improve efficiency, accuracy, customer satisfaction gains, and competitive advantage.The most successful implementations treat multi-agent systems not as cost savers but as a fundamental change to the claimant experience. By combining the power of AI with thoughtfully designed human touchpoints, these systems are redefining what claimants expect from MGAs. The clock is ticking if you’re an MGA still using traditional claims processing.

———————————————————————————————————————————

[1] Sajid, M. I. (2025) ‘Multi-Agentic Automation for Evaluating Property Claims in Insurance Underwriting’, Open Journal of Business and Management, 13(3), pp. 123–145. Available at: https://www.scirp.org/journal/paperinformation?paperid=141685

[1] Keybe (2023) What are Multi-Agent Chats? – Keybe – Keybe AI [Online]. Available at: https://keybe.us/blog/multi-agent-chat-the-future-of-sales-and-customer-service/

For a demonstration of how Multi-Agent Systems can help transform your claims processing, please contact: insurance@encora.com

About Encora

Encora, with a specialised workforce of 9,500 engineers across 20 countries, is recognized as a Leader in “Next-Gen Application Development & Maintenance, Data Engineering & Data Science” by the ISG Provider Lens. As a member of the exclusive Microsoft Azure AI Foundry Partner Council, Encora leverages exclusive resources, focused consultancy and AI-driven application development services. By leveraging focused innovation, multi-agent AI system design and composable enterprise architectures, Encora enables MGAs to reshape the future of insurance claims.