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Summary – Claims management suffers from slow processing, opaque case files, manual errors, long setup times, ad-hoc prioritization, unpredictable costs and timelines, fraud risks, support overload, and customer dissatisfaction; Solution: automated extraction and classification → cost/timeline prediction and real-time tracking → flexible conversational personalization.
Claims handling is a critical area for insurers, often perceived as slow and opaque, leading to frustration and loss of trust. Artificial intelligence is changing the game by offering cognitive and generative processing capabilities, as well as large language models (LLMs) capable of automating and enhancing every step of the claims process.
Beyond operational efficiency, the true value of AI lies in its ability to restore transparency, accelerate settlements, and strengthen policyholder loyalty. This article explores how AI technologies are transforming claims into a faster, clearer, and smoother process while controlling costs and risks.
AI-Accelerated Claims Management
Cognitive AI can extract and structure claims information in record time. Algorithms automatically identify key data to speed up each file.
Intelligent Data Extraction
Cognitive AI solutions scan attachments (photos, forms, expert reports) to extract relevant information.
This process eliminates manual tasks and reduces input errors. The Claims Processing teams can focus on business analysis rather than data collection.
Time savings are immediate, with up to a 70% reduction in file initialization time.
Automated Classification and Prioritization
Machine learning models categorize claims based on complexity, estimated cost, and fraud risk. They assign priority to urgent or sensitive claims, ensuring each case receives appropriate handling.
This approach ensures critical claims are addressed first, minimizing delays in high-stakes cases. Performance indicators are monitored continuously to refine sorting criteria.
Prioritization automation frees up experts’ time while ensuring a smoother workflow.
Example: Speeding Up Turnaround for a Swiss Insurer
A mid-sized Swiss insurance company deployed an open-source cognitive solution to extract information from over 10,000 annual claims. The project was built on a modular architecture that integrated AI modules into their existing system without vendor lock-in.
Result: The average time to receive key data dropped from three days to two hours, reducing initial analysis time by 85%. This rapid turnaround became a powerful driver for reducing internal disputes and improving the Internal Satisfaction Rate (ISR).
This case demonstrates that contextually and incrementally deployed AI significantly accelerates claims management while relying on secure open-source solutions.
Transparency and Predictability in Claims
AI models generate accurate forecasts and provide real-time monitoring of each claim, delivering clarity and visibility to all stakeholders.
Real-Time Claim Tracking
Thanks to dashboards powered by LLMs, every step of the claim is tracked and updated automatically. Managers can view progress, bottlenecks, and remaining timelines without manual intervention.
This transparency reduces calls to the call center and follow-up inquiries, as policyholders and partners can see exactly where their claim stands. Traceability improves and internal audits are streamlined.
Automated tracking strengthens customer trust and decreases the number of complaints related to process opacity.
Cost and Timeline Prediction
Predictive algorithms analyze claims history to estimate costs and settlement times for new claims. They calculate the likelihood of approval, expert referral, or legal dispute.
Teams can thus proactively allocate resources and prepare fairer, faster settlement offers. This foresight helps reduce uncertainty and better manage financial reserves.
Predictive AI helps stabilize claims budgets and optimize team staffing according to activity waves.
Example: Improved Visibility for a Swiss Player
A Swiss general insurer integrated an LLM module into its claims management system to automatically generate progress reports. Every employee and policyholder has access to a simple interface detailing the current status, next steps, and any missing elements.
In six months, calls for status updates dropped by 60% and proactive issue resolution reduced overall processing time by 20%. The project was built on a local cloud infrastructure to meet Swiss regulatory requirements and scaled thanks to modular design.
This initiative demonstrated that increased visibility is a key factor in reducing frustration and strengthening customer relationships.
Edana: strategic digital partner in Switzerland
We support companies and organizations in their digital transformation
AI-Driven Personalization and Customer Satisfaction
Generative AI enables personalized interactions and communications around claims. Chatbots and virtual assistants provide human-like support 24/7.
Contextual Conversational Dialogues
LLM-based chatbots understand the context of the claim and respond precisely to policyholders’ questions. They guide users through the steps, collect missing information, and offer tailored advice.
These virtual assistants reduce customer support load by handling simple requests and automatically escalating complex cases to human agents. The experience becomes seamless and responsive.
The tone is calibrated to remain professional, reassuring, and in line with the insurer’s communication guidelines.
Clear Summaries and Reports Creation
LLMs can draft readable summaries of expert reports, cost estimates, and settlement notes in seconds. These documents are structured and tailored to the recipient’s profile, whether a manager or an end customer.
This helps reduce misunderstandings and clarification requests, enhancing perceived service quality. Reports include automatically generated charts to illustrate cost and timeline trends.
Automated writing ensures terminological consistency and a constant level of detail, regardless of the volume of cases handled.
Example: Boosting Satisfaction at a Swiss Health Insurer
A Swiss health insurer implemented an internal virtual assistant to interact with policyholders and update them on claim reimbursements. The system uses a ChatGPT assistant hosted on a hybrid infrastructure, ensuring compliance and scalability.
The internal Net Promoter Score (NPS) rose from 45 to 68 in three months, and self-service adoption exceeded 80%. Policyholders praised the quality of interactions and the sense of clear, personalized support.
This case illustrates how generative AI can transform each interaction into a moment of strengthened trust.
Cost Reduction and Operational Efficiency
Intelligent automation and predictive analytics reduce management costs and limit fraud risks. AI delivers measurable and sustainable efficiency gains.
Automation of Repetitive Tasks
Robotic process automation (RPA) coupled with AI handles repetitive tasks such as sending acknowledgments, verifying attachments, and updating statuses. This delegation enables business process automation, reducing manual errors and increasing productivity.
Staff can then focus on high-value activities like complex analysis and customer relations. The end-to-end process becomes faster and more reliable.
Per-claim processing costs can decrease by 30% to 50% without compromising service quality.
Predictive Analytics for Fraud Prevention
AI detects fraud patterns by analyzing historical data and identifying risky behaviors (unusual limits, unlikely correlations, fraud networks). Alerts are generated in real time for investigation.
Proactive monitoring limits financial losses and deters fraud attempts. Models continuously improve through supervised learning and investigator feedback.
The return on investment is rapid, as each prevented fraud case translates directly into savings on indemnities and litigation costs.
Example: Cost Optimization for a Swiss Life Insurer
A Swiss life insurer integrated an open-source RPA engine with machine learning models to automate 60% of recurring tasks in the claims department. The architecture is based on containerized microservices, promoting component reuse and evolution.
After one year of operation, the average cost per claim decreased by 40% and detected fraud rose by 25%, with an estimated 18-month payback period. Teams gained confidence and capacity to handle complex cases.
This project illustrates that a modular, open-source approach ensures sustainable ROI while avoiding prohibitive licensing costs.
Strengthening Customer Trust in AI-Driven Claims
Cognitive, generative, and LLM-based AI technologies are revolutionizing every step of the claims process by accelerating handling, clarifying communication, and personalizing the experience. They also deliver measurable efficiency gains and better risk control.
Our experts are available to assess your context and define an AI roadmap that restores transparency, speed, and customer satisfaction while optimizing costs. Together, turn your claims management into a sustainable competitive advantage.
