AI-Based Underwriting for Risk Analysis with Azure Services

Modernizing Insurance Underwriting: Leveraging Azure AI for Enhanced Risk Analysis and Efficiency

Use Case Briefing

Our client is an insurance company who was interested in implementing an AI-based underwriting system built on Microsoft Azure.

The Process

1. Initial Contact: After the insurance company reached out to MA Technologies, our team promptly set up a discovery call. During this call, we gained an initial understanding of their current situation, their underwriting process, and the structure of their existing IT setup.
2. In-depth Discovery: In a series of follow-up meetings, our team conducted a thorough examination of their insurance operations, specifically focusing on the challenges and bottlenecks in the underwriting process. This allowed us to identify the key areas where AI and automation could add significant value.
3. Custom Solution Design: With a comprehensive understanding of their needs and the potential of Azure AI services, we designed a custom AI-based underwriting solution tailored to their specific needs. We laid out a detailed implementation plan that included steps to integrate Azure AI services with their current IT infrastructure, a roadmap for the transition process, and a projection of the expected improvements and return on investment.
4. Stakeholder Presentation and Approval: We presented the proposed solution and implementation plan to the insurance company’s key stakeholders. During this presentation, we clarified their queries, discussed potential challenges, and ensured that our solution aligned with their business objectives and IT strategy. After in-depth discussions and necessary revisions, the stakeholders approved the proposed plan and green-lighted the project.
5. Project Initiation and Setup: With the project plan approved, we assigned a dedicated project team consisting of Azure specialists, AI experts, data scientists, and insurance industry consultants. This team was responsible for the development and integration of the AI-based underwriting system, following the detailed project roadmap.
6. System Development, Integration, and Testing: The project team carried out the development and integration of the AI-based underwriting system, leveraging Azure AI services. Rigorous testing was conducted to ensure the system functioned as expected and was ready to handle real-world underwriting scenarios. Feedback from the insurance company’s team and the results of the testing phase led to further refinements and enhancements of the system.
7. Training and System Handover: We provided extensive training to the insurance company’s underwriting and IT teams to ensure they could effectively operate and maintain the new AI-based underwriting system. After the training phase, the system was formally handed over for live operations.
8. Post-Implementation Support: Following the successful deployment of the system, MA Technologies provided continuous support to promptly address any issues that surfaced during the initial period of operation. We also set up a plan for periodic review and continuous improvement of the system, enabling it to adapt to changing business needs and regulatory requirements.

Underwriting System Breakdown

1. An applicant, Jane, completes her application for a life insurance policy and submits it through the Insurance Company website. The form includes comprehensive information such as medical history, occupation, lifestyle habits, and more.
2. Azure AI services, backed by Azure Machine Learning, automatically initiates the underwriting process. The AI system retrieves Jane’s application data, extracts relevant information, and consolidates it with data from other sources stored in Azure Data Lake Storage.
3. Leveraging its trained model, the Azure AI system analyzes Jane’s data. It computes a risk score based on her profile, considering factors such as age, medical history, occupation, and lifestyle habits. It also uses historical data to predict the probability of her making a claim in the future.
4. The results are compiled and visualized in a risk profile dashboard on Azure Power BI. This detailed report, including Jane’s risk score and potential claim predictions, is then made available to John Doe, the underwriter.
5. John reviews the detailed risk profile provided by Azure AI services. Based on this analysis, he decides that Jane’s application can be accepted, but due to a slightly elevated risk score, her premium rates would be a bit higher than the average.
6. John’s decision is recorded in the system, which Azure Machine Learning uses as new data to enhance its predictive model. Jane is then notified of the underwriting decision and her policy premium.

Alternate Flow

During step 3, if Azure AI services encounter inconsistencies in Jane’s data or if information is missing, the system flags her application for manual review. John then collaborates with the data management team to address these issues before rerunning the application through the AI underwriting process.

Exceptional Flow

If Azure AI services encounter a system error or malfunction during the underwriting process, our team is notified immediately. While the issue is being resolved, John and his team revert to their traditional underwriting methods to ensure business continuity.

Outcome

Implementing the AI-based underwriting system brought transformative change to our client, the Insurance Company. The Azure AI services significantly enhanced the efficiency and accuracy of the underwriting process, leading to several positive outcomes:

1. Improved Accuracy: The AI system provides a more detailed and accurate risk assessment, which leads to more precise premium pricing. This results in reduced losses for the company and fairer premiums for customers.
2. Efficiency Gain: The AI system automates data extraction and risk analysis, freeing up the underwriters’ time for more complex tasks and decision-making. This reduces the underwriting time and enhances the overall process efficiency.
3. Better Risk Management: With the AI system’s ability to predict future claims based on past data and trends, XYZ Insurance gains a more nuanced understanding of their risk profile, leading to improved risk management and strategic decision-making.
4. Customer Satisfaction: With faster underwriting and fairer premium pricing, customer satisfaction increases. This leads to a better reputation in the market and growth in the customer base for XYZ Insurance.
5. Continuous Learning and Improvement: The AI system continually learns from the underwriters’ decisions, enhancing its prediction accuracy over time. This results in continuous improvement in the underwriting process, making it more refined and reliable.
6. Regulatory Compliance: Regular audits ensure that the AI system operates as expected, adhering to privacy standards and regulations. This mitigates potential legal and reputational risks for XYZ Insurance.

By building and deploying an AI-based underwriting system with Azure AI services, the Insurance company has significantly modernized their underwriting process, making it more accurate, efficient, and compliant.