Higher Throughput
Straight-through processing increased by 45%, allowing faster application approvals without human intervention.
Motivation
To address manual decision bottlenecks, inconsistent underwriting outcomes, and time-consuming processing by leveraging historical data and machine learning, enabling faster, more accurate, and consistent policy application decisions while freeing underwriters to focus on complex or high-value cases.
Main challenges
Challenge 01
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.
#1
Challenge 02
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.
#2
Challenge 03
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.
#3
Challenge 04
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.
#4
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.
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Inquire for more infoSolution
01
Automated Decision Engine
Processes incoming policy applications using a machine learning-based predictive model that scores each application and provides recommendations to approve, decline, or manually review. Eliminates repetitive manual assessment and speeds up underwriting while maintaining high accuracy.
Key capabilities:
- Automatic classification of applications into green, yellow, and orange zones
- Prediction of approval or decline probability
- High recall for declined applications to minimize risk
- Stratified 10-fold cross-validation for model validation
02
Historical Data Integration
Utilizes extensive historical data from past applications, decisions, policies, and claims to train the predictive model. Ensures that the system learns from past patterns, improving consistency and reliability of recommendations over time.
Key capabilities:
- Training dataset with over 140k applications and 1000+ features
- Recursive feature elimination to optimize model performance
- Integration of prior policy outcomes and claims data
- Enhanced predictive scoring for new applications
03
Configurable Decision Thresholds
Allows administrators to adjust thresholds for approval, review, and decline zones, balancing risk and application throughput. Enables the business to control the level of automation and fine-tune system performance based on risk appetite.
Key capabilities:
- Green zone for auto-approval
- Yellow zone for manual review
- Orange zone for high probability of decline
- Thresholds adjustable by line of business or risk tolerance
04
Statistical Performance Analysis
Provides underwriters with statistical analysis of similar past decisions, showing historical effectiveness of approvals and declines. Supports informed decision-making and continuous improvement of underwriting strategies.
Key capabilities:
- Panel showing historical accuracy of similar decisions
- Measurement of algorithm performance using ROC AUC
- Supports continuous improvement of predictive models
- Facilitates informed operational adjustments
05
Hybrid API and Architecture
Implements a hybrid API architecture combining GraphQL and JSON endpoints to handle complex data needs of the underwriting system, ensuring flexible integration with internal and external platforms.
Key capabilities:
- Flexible data access and integration
- Scalable microservices architecture
- Support for high-volume application processing
- Seamless interaction with internal insurance systems
Results & business impact
Improved Underwriter Efficiency
Underwriters focus on high-value, complex cases, freeing time from routine applications.
Increased Processing Capacity
System processed 2.5 times more applications over the same period.
Consistent and Data-Driven Decisions
Machine learning-based recommendations reduce errors and variability in underwriting.
Flexible Risk Management
Configurable thresholds allow the insurance company to balance volume and risk according to business needs.
