The Importance of Data Preparedness for Insurers Looking to Implement AI Solutions
In today’s business landscape, many industries, including insurance, are embracing the use of advanced AI technologies such as generative AI. However, just like a Formula One car, these AI solutions require the proper data to reach their optimal performance. In this article, we will explore the five fundamental steps that insurers need to follow to ensure they are ready to start the AI supercar and deliver next-generation customer experiences and products that can transform their growth potential.
Step 1: Identify Relevant Data Sources
Insurers must identify and collect relevant data across all their core systems, including third-party external data that rounds out the data landscape. This requires immense internal resources across claims, underwriting, and other departments.
Step 2: Deep Clean Your Data
Insurers need to identify irrelevant data and find and correct errors. After that, they can then develop a standard format that will be fed into the AI system. Cleaning and preprocessing data are like preparing a high-quality meal for a premier athlete, but in this instance, the AI is consuming the feast of data.
Step 3: Categorize Your Information
Now that insurers have clean and standardized data, they need to segment the information into categories about their various business functions (claims, customer data, etc.). This labeling will enable insurers to better analyze claims for type, cost, and location to predict the impact on their revenue reserves or plug into an external data partner that will integrate via an API into a generative AI tool like ChatGPT.
Step 4: Have Good AI Values
Assuming an insurer did the previous steps correctly, one of the most important things to do next is to establish standards for ethically using customer data. The last decade was fraught with examples of bias in AI, so companies have an opportunity to use AI for good. That good can be customer satisfaction, revenue growth, or social impact for non-profit ventures. Whatever the activity, the people using the tools must have good AI values.
Step 5: Get Your Feet Wet and Keep Going Deeper into the Water
After an insurer has made their choice, then it is about training, testing, monitoring, and optimizing models while keeping their good AI values in mind. Insurers should conduct thoughtful research that considers what experts are saying in mediums like this article. Additionally, they should not assume that only the new AI tools from the most prominent names are the only options out there. They should look at the relevance to their industry from the company that built the AI tool.
By prioritizing data, insurers can best be prepared to handle the Formula One AI supercar. In turn, this can help to improve internal operational efficiency, boost profitability, identify new product or market opportunities, transform the customer experience, and ensure scalable business growth for years to come.
- Insurance data management
- AI data processing
- Data cleansing for AI
- Insurance industry analytics
- Data preparation for machine learning in insurance
News Source : Robert Clark
Source Link :How To Prepare Insurance Data For Generative AI Prime Time/