PROBLEM STATEMENT
This case study highlights the benefits, challenges, and outcomes of implementing AI-driven underwriting in an insurance firm.
Underwriting plays a crucial role in assessing and managing risks associated with insurance policies. By leveraging artificial intelligence (AI) technologies, insurance companies can automate and streamline the underwriting process, improving efficiency and accuracy. Traditionally, underwriting involved manual assessment of insurance applications, which was time-consuming and prone to errors. Seeking to enhance efficiency, reduce processing time, and improve accuracy, ABC Insurance decided to leverage AI technologies.
PILOT TESTING & REFINEMENT
The AI-driven underwriting system underwent rigorous pilot testing. Insurance applications were processed simultaneously using both the manual method and the AI model. Results were compared, and the model was refined iteratively based on feedback from underwriters.
Our team of data scientists and machine learning experts developed an AI model for underwriting. Using supervised learning techniques, particularly classification algorithms, the model was trained on the historical dataset. It learned to identify patterns and correlations between variables to predict policy risks and determine premium rates. APIs and data connectors were developed to facilitate data exchange and communication between different systems.
Data used: historical insurance applications, including customer information, medical records, risk factors, and claim history
STARTING POINT
EMPLOYEE TRAINING
Thorough training programs were conducted to familiarize underwriters with the AI-driven system. The training aimed to ensure effective collaboration between human underwriters and the AI model, leveraging their respective strengths.
RESULTS
TeraCrunch solution allowed the department to enhance efficiency, improve accuracy and improved customer experience
Cost Savings:
Implementing AI-driven underwriting allowed the customer to optimize resource allocation. Automation reduced the need for manual labor, enabling the firm to allocate human resources to other value-added tasks. This resulted in cost savings and increased operational efficiency.
Improved Accuracy:
Leveraging machine learning algorithms, ABC Insurance achieved greater accuracy in risk assessment. The AI model considered a wide range of factors and variables, resulting in more reliable predictions of policy risks and appropriate premium rates.
Enhanced customer experience:
The reduction in policy approval turnaround time significantly improved the overall customer experience. Policyholders experienced faster processing and decision-making, increasing satisfaction and loyalty.
