Insurance Claims Settlement Automation using AI

A top insurance company, struggled with errors and delays in claims settlement, causing customer dissatisfaction. Using our AI and machine learning expertise, our client automated their claims processing.

This change reduced errors, sped up processing, and boosted efficiency and customer satisfaction. Now, with Agile Lab's AI-driven automation, the client has a streamlined claims process, setting a new industry standard.

Customer Context

The insurance industry has historically been burdened by complex and time-consuming claim settlement processes, particularly when it involves health insurance payments. Traditional manual procedures often result in waiting periods, administrative overhead and human errors.

Such was the case of one of our major European Insurance clients, who wished to scale their claims settlement processes, while at the same time reduce their duration, and increase customer satisfaction.

With advancements in Artificial Intelligence (AI) and Machine Learning (ML), insurers can now automate critical steps in the claims process, dramatically increasing efficiency, accuracy, and client loyalty. Embracing AI technologies positioned our client at the forefront of the insurance industry in a climate characterized by increasing customer expectations and regulatory complexity.

The Challenge

Insurance claims management, especially for health-related policies, has long posed several challenges. These often amount to:

Delays due to the sheer volume of documentation and the necessity for careful validation
Extended processing time as a single health claim includes medical documentation and expenses reviews, documents classification, and anomaly and fraud detection
Risk of human error because of the manual-intensive approach, potentially affecting the accuracy and consistency of decisions
Reduced customer loyalty and increased churn due to rapid service expectations
Inadequate traditional methods for sophisticated fraud detection expose insurers to significant financial losses, reputational harm, and regulatory non-compliance, further underscoring the urgency for more advanced and precise technological solutions.

With thousands of health claims managed monthly, insurers need innovative solutions that can automatically handle a substantial fraction of these cases swiftly and accurately.

The pressure on insurers to deliver rapid yet accurate service has intensified due to heightened competition and the increasing ease with which consumers can switch providers.

Agile Lab has provided a sophisticated AI-driven solution designed explicitly for automating insurance claim settlements, with a particular focus on health insurance policies. The primary components of this solution include optical character recognition (OCR), machine learning techniques, document classification, anomaly detection, and semantic understanding to automate the claims handling process effectively.

The architecture of this solution is built around a layered data lake strategy, starting from raw data ingestion (landing layer) to refined and structured data (Layer 1 to N). Master Data Management (MDM) ensures high-quality and accurate customer data, feeding into operational data science processes.

Solution Stages

The AI/ML pipeline consists of three primary stages

Data Preparation

Documents related to claims are digitized and cleaned through OCR technology. This transforms unstructured medical and expense data into structured, actionable insights. Advanced OCR technologies significantly reduce errors, providing consistently reliable digitized information.

Build and Train

Machine learning models classify and evaluate documents, automatically checking their consistency and compliance. Advanced anomaly detection methods identify fraudulent claims, monitoring unusual patterns or irregularities in real-time. This component leverages biometric authentication and data enrichment to enhance fraud detection further, enabling a comprehensive, layered approach to security.

Model Deployment

The trained models actively manage the claim processing phase, autonomously handling about 20% of the claims without human intervention. This significantly reduces processing times, with each claim typically resolved within approximately 60 seconds, a substantial improvement compared to traditional methods. Such rapid processing drastically improves customer experience and operational efficiency.

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ACCELERATED DECISION-MAKING

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COST SAVING

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DATA QUALITY IMPROVEMENT

Accelerated Decision-Making

Data-driven organizations are three times more likely to report significant improvements in decision-making speed, helping them to respond faster to market changes

(Source: HARVARD BUSINESS SCHOOL)

Cost Saving

Data Platforms can allow companies to realize cost savings of up to 15% through minimized redundancies, optimized resource utilization and streamlined processes.

(Source: McKinsey&Company)

Data Quality Improvement

Companies focusing on structured data management can improve data accuracy and consistency by 10-20% through centralized data platforms

(Source: McKinsey&Company)

Cost Saving

Our approach resulted in lower storage, data integration costs and data transaction costs. This reduction in expenses has enhanced the organization's financial efficiency and resource allocation.

Efficiency

We achieve streamlined Data Management processes and improved Governance by implementing structured guidelines and technical solutions. This led to smoother operations and better utilization of resources across the organization.

Stakeholders Confidence

Demonstrable improvements in Data Management increased stakeholder trust. This support was crucial for securing ongoing investments and resources for future Data Management initiatives.

Additionally, Agile Lab's solution includes a generative AI chatbot tailored specifically to the insurance sector. This chatbot provides real-time, accurate responses to policy-related queries by integrating with a detailed knowledge base segmented into personal, heritage, and animal-related insurance information. Such a feature enhances user interaction and satisfaction by delivering instant and accurate policy explanations and facilitating smoother claim submissions.

The system further utilizes a semantic search and discovery tool to navigate insurance documentation effectively, retrieving semantically coherent and contextually relevant information. Furthermore, Agile Lab applies semantic transaction classification, leveraging a small-sized large language model (LLM) to classify financial transactions efficiently. This classification is instrumental in improving internal processes and customer profiling capabilities, offering uniform input representation and resilience to noisy data.

The continuous improvement of the solution is ensured by active learning and weak learning techniques, which help refine the model over time. This guarantees ongoing enhancement of accuracy, resilience to new fraud schemes, and an adaptive approach to emerging data trends. Insurers leveraging this solution benefit from both immediate operational improvements and sustained, long-term advancements in claim processing capabilities.

Real-World Impact and Benefits

Operational Area	Before AI Automation Solution	After AI Automation Solution
Processing Time	Claims took days or weeks to process	Claims typically resolved within approximately 60 seconds, a dramatic reduction from days or weeks
Automation Rate	Manual review of medical documentation, verification of expenses, document classification	Autonomously handling about 20% of the claims without human intervention
Fraud Detection	Traditional methods inadequate in detecting sophisticated fraudulent activities	Advanced anomaly detection methods identify fraudulent claims in real-time, with resilience to new fraud schemes
Customer Experience	Delays and inconsistencies negatively impacting customer experience	Significantly improved customer satisfaction through timely and accurate claims settlement
Regulatory Compliance	Vulnerability to regulatory non-compliance	Compliance with legal and regulatory frameworks such as GDPR and IVASS
Business Intelligence	Limited customer insights	AI-driven insights into customer behavior and transaction patterns, enabling personalized services
Strategic Positioning	Competitive disadvantage	Positioned for sustainable growth, improved market reputation, and mitigated operational risks

Implementing Agile Lab’s AI-driven automation has tangible real-world impacts, especially in health insurance. The immediate benefit observed is a dramatic reduction in claims processing times, from days or weeks to mere seconds in many cases. The automation handles repetitive, error-prone tasks, allowing human adjusters to focus on more complex claims, thus optimizing workforce efficiency.

Customer satisfaction is significantly improved through timely and accurate claims settlement, increasing customer retention and enhancing brand reputation. Additionally, automated fraud detection protects insurers against fraudulent claims, substantially reducing financial risks and safeguarding regulatory compliance.

The advanced AI tools embedded within Agile Lab's solution further empower insurers to understand their clients better, enabling more personalized and targeted services. By leveraging AI-driven insights into customer behavior and transaction patterns, insurers can develop proactive strategies for customer engagement, product innovation, and market differentiation.

Implementing Agile Lab’s AI-driven solution for claim settlement automation offers substantial benefits to insurers. By significantly reducing processing times and automating routine claim validations, insurers can deliver faster, more consistent, and more reliable services with an average of 1500 claims per month processed. The AI and ML-driven approach ensures robust fraud detection, enhanced accuracy, and compliance with legal and regulatory frameworks such as GDPR and IVASS. Ultimately, automation increases customer satisfaction and loyalty, reducing churn rates and boosting the likelihood of policy renewals. The technology positions insurance providers for sustainable growth, improves their market reputation, and mitigates operational risks.