Generative artificial intelligence (GenAI) is rapidly reshaping the financial services sector, offering insurers and lenders new ways to enhance efficiency, improve accuracy, and personalise customer interactions. Earnix, a leader in AI-driven decision-making solutions, highlights how GenAI is automating routine tasks, allowing teams to focus on more strategic initiatives that drive business growth.
The evolution of GenAI is closely tied to copilots and large language models (LLMs), which make AI-powered capabilities more accessible to both technical and non-technical users.
These virtual agents work alongside human experts, accelerating problem-solving and optimising core business processes such as pricing, underwriting, and customer acquisition.
For financial services teams to fully harness GenAI’s potential, mastering prompt engineering is essential. Prompt engineering involves crafting precise and structured inputs to guide AI models in generating accurate, useful, and context-specific responses.
Key elements of prompt engineering
Effective prompt engineering relies on several principles:
- Clarity: Clearly define the desired output
- Context: Provide relevant background information
- Instructions: Use explicit directives like “list” or “explain”
- Constraints: Set parameters for the response
- Iteration: Refine prompts for improved accuracy
For instance, a generic prompt like “Tell me about climate change and its effect on insurance liability” may yield a broad response. In contrast, a well-structured prompt such as “Explain the main consequences of climate change and its effects on expected losses from hurricanes in the Gulf States of the US in less than 250 words” produces a more focused and actionable answer.
The benefits of prompt engineering
Prompt engineering enhances GenAI’s effectiveness, leading to improved business outcomes. Key benefits include:
- Greater control over AI outputs: Ensures responses are consistent and contextually relevant
- Enhanced user experience: Reduces trial-and-error in model training
- Improved collaboration: Enables knowledge-sharing across teams
- Democratised AI access: Allows non-technical users to apply AI without deep technical expertise
Applications in insurance and banking
The potential applications of GenAI copilots and prompt engineering in financial services are vast. While they streamline customer support and claims processing, their impact is particularly strong in pricing, rating, and underwriting.
Examples of how prompt engineering enhances workflows include:
- Training new users: “How do I run a pricing simulation for homeowner’s insurance?”
- Data navigation: “What are the possible data types for demographic variables inside my system?”
- Automated coding: “Generate code to create a GLM demand model for Illinois using vehicle age, repair history, and vehicle model type as predictors.”
GenAI is driving business growth in financial services by enhancing decision-making, optimising workflows, and reducing operational costs.
By integrating copilots and prompt engineering, forward-thinking teams can unlock AI’s full potential, improving their competitive edge. Now is the time for firms to embrace these innovations and invest in prompt engineering skills.
Read the full blog from Earnix here.
