Enhancing the Underwriting Process with AI in Insurance

Context

In the competitive and complex world of insurance, the underwriting process is a critical phase where risks are assessed, and policies are drafted in compliance with established guidelines.

However, a notable challenge had emerged for our client: insurance agents were finding loopholes in the underwriting rules to get policies approved that might not meet the company’s strict risk and compliance standards. This manipulation not only undermined the integrity of the underwriting process but also introduced unwarranted risks into the portfolio, potentially impacting the company’s financial health.

The Challenge

The primary challenge was two-fold. Firstly, there was a significant delay in identifying non-compliance with underwriting rules, with the existing process taking up to eight weeks to detect whether agents were manipulating policy approvals. This lag meant that policy manipulations were often not caught until they had already impacted the portfolio. Secondly, agents were gaming the system by submitting multiple variations of policies for the same client until one was approved, subsequently adding omitted information post-approval, thereby increasing the insurer’s risk exposure without proper assessment.

Solution

To address these challenges, our Data & Intelligence Managing Director developed an AI-powered solution with a focus on consolidating data from various sources, including risk assessments, current claims processing, and policy details. This comprehensive data consolidation allowed for more sophisticated analysis and monitoring of the underwriting process.

The solution involved two key components

Prescriptive Analysis for Rule Compliance

By harnessing the power of AI for prescriptive analysis, the company could understand the patterns and behaviors of agents who were attempting to circumvent underwriting rules. This analysis provided insights into how policies were being manipulated, enabling the insurer to quickly identify and address non-compliance. For example, the system could detect when an agent submitted multiple policy variations for the same client, each time slightly altering the details to find a loophole that would lead to approval.
Enhanced Claims and Policy Validation

The AI system also integrated claims history and policy details to assess the risk of renewing policies automatically. This integration meant that the AI could flag policies due for auto-renewal that, based on new information or claims history, should be re-evaluated. This capability ensured that policies were not renewed under outdated terms that no longer reflected the client’s risk profile or compliance with the latest underwriting rules.

Results

The implementation of AI in the underwriting process led to significant improvements in efficiency and risk management

Reduced Detection Time

The time required to identify non-compliance with underwriting rules was dramatically reduced from eight weeks to a matter of days. This rapid detection allowed for timely interventions and adjustments to policy approvals, significantly mitigating risk exposure.
Prevention of Policy Manipulation

The AI system’s ability to analyze patterns and behaviors prevented agents from gaming the underwriting process, ensuring that all policies met the company’s risk and compliance standards before approval.
Improved Policy Renewal Process

Integrating claims history into the policy renewal process enabled more informed decisions on renewals, preventing automatic renewals of policies that no longer met underwriting standards due to changes in the client’s risk profile or claims history.

Conclusion

By leveraging AI for data consolidation, prescriptive analysis, and enhanced validation, the insurance company was able to streamline its underwriting process, ensuring compliance with rules and minimizing unwarranted risk exposure. This case study demonstrates the transformative potential of AI in refining core operational processes within the insurance industry, leading to better risk management, operational efficiency, and compliance integrity.