Motivation
To automate and streamline the underwriting decision-making process, improving efficiency, accuracy, and speed.
Main Challenges
Manual decision-making caused delays and bottlenecks in the underwriting process, preventing efficient handling of policy applications. Azati addressed this by automating the decision-making process, reducing the need for manual intervention and speeding up policy application processing.
The underwriting process was time-consuming due to repetitive tasks, making it difficult to quickly evaluate and process applications. Azati developed an automated system that analyzes policy applications and provides recommendations, significantly reducing processing time.
Human factors led to inconsistent decisions, affecting the quality of underwriting decisions and increasing the risk of errors. Azati solved this by using machine learning to analyze historical data and provide data-driven decision recommendations, ensuring consistency and accuracy in the process.
There was limited use of historical data, which hindered the potential to make data-driven decisions and improve the accuracy of policy assessments. Azati leveraged historical underwriting data to train the machine learning model, enhancing the decision-making process with predictive insights from past applications.
Key Features
- Automated Decision-Making: The assistant automatically processes policy applications, offering recommendations to approve, reject, or manually review applications based on predictive modeling.
- Prediction of Application Approval or Rejection: The system predicts the likelihood of application approval or rejection, improving decision-making consistency and accuracy.
- Historical Data-Driven Decision Recommendations: The system utilizes historical data to make more accurate and data-driven recommendations, reducing human error.
- Configurable Decision Thresholds: Underwriters can configure decision thresholds, allowing them to have better control over the decision-making process.
- Statistical Analysis of Decision Effectiveness: The system includes statistical analysis tools to measure the effectiveness of decisions over time, providing insights for continuous improvement.
Our Approach
Project Impact
Increased Straight-Through Processing: The insurance company saw a 45% increase in straight-through processing of new applications, streamlining operations.
Improved Focus on High-Value Cases: Underwriters were able to focus on more complex, high-value cases while the system handled routine applications, enhancing efficiency.
Increased Application Processing Capacity: The company was able to process 2.5 times more applications within the same time period, significantly boosting productivity.