Insurance projects.

Identifying Relevant Risks

Difficulty in identifying and responding to potential risks and trends promptly.

Data Overload

Managing vast amounts of unstructured data to find actionable insights.

Predictive Accuracy

Need for more accurate and timely predictions to improve underwriting and pricing strategies.

Signal Identification Technology (Early Warning Signals)

Implemented EWS to detect relevant risks and opportunities earlier.

Advanced Data Science Techniques

Utilized AI to process and analyze unstructured data for signal detection.

Real-Time Data Analysis

Leveraged real-time data to enhance predictive accuracy and decision-making.

Validated Method

Real-time data validation with experts finding 85.7% of signals as important.

Early Detection

Identified important events earlier than existing models, improving response time.

Improved Underwriting and Pricing

Better risk assessment and trend analysis led to optimized strategies.

Manual Verification

Labor-intensive process of verifying claim information against multiple sources.

Fraud Risk

High risk of fraud due to inaccuracies in claims and supporting documents.

Regulatory Compliance

Ensuring claims processing aligns with all applicable regulations while maintaining efficiency.

AI-Powered Document Ranking

Implemented AI to automate document classification, data extraction, and risk analysis.

Comprehensive Verification

Automated the verification of claims, medical records, financial documents, and more.

Risk Profile Assessment

Used AI to accurately assess risk profiles and ensure compliance with regulations.

Improved Efficiency

Streamlined the claims verification process, reducing the time and effort required by 60%, which led to faster claim resolutions.

Enhanced Accuracy

Reduced errors in data entry and verification by 70%, improving the accuracy of claims processing and decreasing rework.

Compliance Assurance

Ensured comprehensive checks and regulatory compliance with minimal manual intervention.

Understanding the Market

Difficulty in predicting premiums for small and medium-sized company groups, leading to a lower market share in this segment.

Data Integration

Combining and analyzing data from multiple sources, including incomplete internal and external datasets.

Operational Efficiency

Enhancing the efficiency of the insurance brokerage process through better data-driven insights.

Dynamic Reporting

Created a dynamic report encapsulating all gathered information, allowing them to review sales data from the past five years based on various segmentations.

Premium Predictions

Developed predictive models using machine learning to estimate market premiums for different company groups and product types, with confidence flags indicating prediction reliability.

Insight Generation

Provided a web-based platform to access insights from dynamic reporting and predictions, enabling them to understand market positioning and identify profitable prospects.

Market Share Increase

Achieved significant market share growth, increasing the market share for companies with revenues under EUR 300k from less than 20% to a higher percentage.

Revenue Growth

Enhanced the ability to forecast premiums resulted in better negotiation strategies with insurers, increasing gross production and premium revenues by up to 15%.

Improved Decision-Making

Enabled data-driven decision-making through comprehensive insights and predictive analytics, leading to optimized resource allocation and operational efficiency.