Enhancing Claim Accuracy in Property & Casualty (P&C) Insurance with Machine Learning

In the highly competitive P&C insurance sector, efficient claim processing is crucial, particularly for motor claims. This case study explores how a machine learning (ML) model was integrated into the claims payment process to address manual data entry errors, streamline operations, and reduce costs. The implementation resulted in significant improvements in accuracy, operational efficiency, and overall cost savings.

Challenge

In the P&C insurance sector, efficient and accurate claim processing is critical, especially for motor (auto) claims. Insurers often have conventions for direct payments based on damage amounts, reducing procedures. However, manual input by claim operators can lead to selecting the wrong third-party insurer. Correcting these mistakes is time-consuming and costly, requiring interaction with the incorrect insurer for reimbursement. Key challenges included:

• Manual Data Entry Errors: Operators occasionally select the wrong insurer, triggering incorrect automated payments.
• Correction Delays: Mistakes lead to significant delays and administrative effort to rectify.
• Operational Efficiency: The process undermines efficiency and increases the workload, potentially leading to customer dissatisfaction.

Delivery

To tackle these challenges, we developed a machine learning (ML) model to enhance accuracy and efficiency in the claim payment process:

• Data Collection and Preprocessing:
  • Historical Data Analysis: Collected historical claim data, including incorrect selections.
  • Data Cleaning: Ensured data consistency by removing inaccuracies and handling missing values.
• Model Development:
  • Feature Engineering: Identified key features like claim amount, damage type, location, and error patterns.
  • Model Selection: Chose models capable of handling complex datasets with high accuracy.
  • Training and Validation: Trained on historical data and validated using cross-validation techniques.
• Integration with Claim System:
  • Real-Time Analysis via API: Integrated the model with the claim system through an API. The system sends claim inputs to the model for analysis.
  • Alert Mechanism: The model alerts the system if it detects a likely error, triggering a pop-up for the operator to review the insurer code.
• Testing and Deployment:
  • Pilot Testing: Deployed initially with a small team to identify integration issues and refine the model.
  • Full Deployment: Trained operators and fully deployed the system with the new alert mechanisms.

Value

Implementing the ML model significantly improved the claim payment process:

• Error Reduction:
  • Improved Accuracy: Reduced incorrect insurer selections through real-time alerts, catching most errors before payment processing.
  • Operational Efficiency: Fewer post-payment errors reduced the administrative burden, allowing staff to focus on complex tasks.
• Cost Savings:
  • Reduced Reimbursement Delays: Avoided incorrect payments, minimizing the time and cost of reimbursement processes and improving cash flow.
  • Resource Optimization: Freed up resources from manual correction tasks, enhancing overall efficiency.

Integrating a machine learning model into the motor claims payment process in the P&C insurance sector secured payments, reduced errors, and enhanced efficiency and cost savings. This case study highlights the potential of ML in improving business processes and delivering substantial value in the insurance industry.