Policy Application Decision Assistant for Underwriters

Azati designed an automated policy application decision assistant for insurance underwriters. With machine learning tools and improving computing capacities, this has now become a reality. The assistant processes policy applications and provides recommendations to approve, decline, or manually review them, helping underwriters make faster, more informed decisions. The system utilizes historical data for predictive modeling, significantly enhancing the underwriting process.

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Motivation

To automate and streamline the underwriting decision-making process, improving efficiency, accuracy, and speed.

Main Challenges

Challenge 1
Manual Decision-making Bottlenecks

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.

Challenge 2
Time-consuming Underwriting Process

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.

Challenge 3
Inconsistent Decision-making

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.

Challenge 4
Limited Use of Historical Data

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

Machine Learning-Based Predictive Model
We developed a machine learning-based predictive model that processes policy applications, assigns them scores, and provides recommendations for approval, rejection, or manual review.
Historical Data Training
The model was trained using historical data, including previous underwriting decisions, policy submissions, and claims, allowing the system to learn from past patterns and make informed decisions.
Architecture Planning
Identified limitations of GraphQL as a standalone solution and proposed a hybrid approach combining GraphQL and standard JSON API to ensure flexibility and comprehensive data handling.
Automation and Efficiency
By automating the decision-making process, the system reduces manual intervention and significantly increases the speed and accuracy of underwriting decisions.

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.

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