Underwriter Copilot: 16-24 Weeks – Implementation

Coforge Limited

Coforge’s proprietary framework to provide real time suggestions and recommendations to underwriters on policy /claim value of insurance application including GenAI chatbot for underwriter assistance

Underwriter Copilot is developed on Power Platform and integrates Azure OpenAI models for real time suggestions and recommendations to underwriters in insurance applications.

• Underwriters engaged in the commercial insurance often deal with complex decisioning to balance competing factors influencing the risk and price. This would help negotiate and close the deal with brokers and prospects. • Today this activity is performed in a sub optimal way with very little assistance from technology. Often the decisions are subjective and tribal knowledge of underwriter is used frequently. • The emergence and maturity of Large Language Processing (LLP) provides a significant opportunity to support the underwriter in dealing with ambiguity and provide real time suggestions and recommendations. This is enabled by bringing data from multiple sources, identifying trends and patterns from the past data, thereby predicting potential options for underwriter to price the risk optimally. • Coforge is proposing to partner with Microsoft to develop a solution to assist the Underwriter in such decisioning on Power platform and Azure AI models.

It includes following components:

-Underwriter Specific Historical performance data: • Ability to pull data from DW/ other applications and calculate ratios • Ability to rollup & drilldown data at different segments

-External data feeds: • Ability to gather external data pertaining to the risk and summarize using Gen AI

-Suggested underwriting: • Review by AI engine providing key parameters of the risk • Ability to compare the current risk data (from Underwriter Work Bench) & compare the with similar risk underwritten • Ability to compute critical ratios & suggest next best action

-Suggested Ratios: • Review by AI engine providing additional parameters for consideration • Ability to compute critical ratios & suggest next best action

-Risk Data: • Key submission summary • Risk data summary from Underwriter Work Bench

-Review Hub: • Review Hub to review documents and AI engine UW summary • Review Hub to review documents and summarize status

-Action Hub: • Action Hub for the underwriter to further action if required. (AI will learn and improve)

-Simulation Hub: • Underwriter can simulate by changing value against the parameters and review the impact

-Gen AI chatbot: • Azure OpenAI based chatbot that integrates to policy applications and provides real time assistance to underwriters on various queries

Key Benefits:

Readily available solution: Core solution and reusable components already created reducing implementation timeline significantly •Low Implementation cost: Reduced solution development cost •Easy to maintain: Due to Low code/No code solution, it is easier to maintain and support •Increased efficiency: Better recommendations leading to optimized premium suggestions •Increased security: Increased security by reducing the risk of data breaches and unauthorized access •Improved underwriter experience: Improve the underwriter experience by providing accurate recommendations and actionable insights

En snabbtitt