How insurers use AI to innovate at speed
With consumer duty, fraud detection, the identification of underinsurance, and the migration of manual processes high on the agenda of our insurance customers, the pressure of innovating in insurance is as high as ever. Through the development of ChatGPT and OpenAI, insurers can innovate even further than before. In this article you will learn how Artificial Intelligence (AI) can empower your organisation to improve employee and customer experiences.
Traditional AI versus OpenAI
Traditional AI, also known as rule-based AI, is a long-established tool within the insurance industry, producing pre-defined outcomes based on a certain set of rules coded by humans. It is often used for data processing, underwriting risks, price policies, pay claims, and prevent fraud.
OpenAI however, generates text, images or code in response to user imputs or prompts, drawing on vast sets of data. It represents a huge opportunity for insurance organisations regarding productivity and cost reduction, although organisations must have a mature database to reap the benefits of OpenAI.
Some examples of how OpenAI can empower your organisation:
- Recording and summarising customer calls within your contact centre, data which can further enrich your knowledge base and in turn your open AI database.
- Extracting information from claims forms and summarising their content, freeing employees from administrative tasks.
- Recalling important product or service information for employees when answering customer queries.
- Enhancing the answers of chatbots currently available to provide accurate information for customers.
Discover more: 3 AI demos to elevate insurance experiences
At Kainos we have developed 3 AI demos to identify key areas insurers can elevate their customer and employee experiences. Two of the demos were created using Microsoft’s Azure OpenAI tools while one was created using more traditional AI methods. Learn how AI can deliver better customer and employee engagement to drive your organisation’s competitive advantage.
Demo 1: SQL natural language search
Kainos’ SQL Natural Language Search leverages the power of Azure OpenAI to enable users to ask questions in natural language and seamlessly translate them into SQL queries. With the help of prompt engineering, our system efficiently converts these queries into SQL code, allowing users to easily access and manipulate data. Furthermore, the application generates code for visualising query results, enhancing the data analysis process for data analysts.
- Democratises access to data for users not familiar with SQL, allowing seamless interactions.
- Smart and responsive dashboards provide the ability to answer follow-up queries, enhancing productivity and streamlining the data exploration experience.
Demo 2: Enterprise document exploration
Enterprise document exploration allows your organisation to gain insights from documents using OpenAI in Azure. Employees can use natural language to perform actions on a dataset of documents such as summarisation, comparison, or information retrieval. This is enabled through integrating OpenAI into a wider document end-to-end pipeline using Azure components (Form recogniser, Cognitive search, Function app).
- Reduces risk of errors or omissions in policy wordings by leveraging comparative analysis available with this tool.
- Improves the customer experience by delivering policy summaries and key facts to your employees and agents in real time.
Demo 3: Underinsurance identifier
The Underinsurance identifier aims to enhance the decision-making process of insurance underwriters by providing them with informed and data-driven insights for estimating insurance premiums such as for home insurance. This is crucial for insurers to minimise excessive claim costs associated with property damage and reconstruction.
- Our approach utilises historical insurance data and publicly available information to create advanced analytics models which help estimate customised home insurance policy premiums for each policyholder.
- Offers a diverse range of visualisations, tailored to both specific data relating to the use case and general data pertaining to the construction market.