AI has the potential to revolutionize the insurance industry, enabling companies to improve customer experiences, enhance risk management and streamline operations. More specifically, AI can assist with underwriting automation, fraud detection, customer support, claims processing and risk assessment.?
People often think of AI as this magical thing that just works. However, the truth is that AI is a tool that requires multiple technologies to all work together as a cohesive platform. This AI platform is built using infrastructure, data, algorithms, and supporting tools.??
How to prepare an AI platform?
Before you can build an AI platform, the necessary pre-work preparations need to be made. These include:?
- Business objectives: You need to understand what you’re trying to achieve as a business and how AI can help achieve your objectives. Are you focusing on automating claims, enhancing customer service or improving risk assessments? Without understanding why and how you can use AI, then you won’t be able to maximize the benefits that it can provide.?
- Data usage: What some people don’t fully understand is that AI requires large amounts of high-quality data. This requires not only having the technical ability to collect, store and manage structured and unstructured data (e.g., claims data, customer profiles and market data), but also having the necessary governance in place to ensure it is high quality. This is an area that traditional insurance companies usually struggle the most.?
- Compliance: Due to insurance’s highly regulated nature, you need to ensure that the AI platform complies with legal frameworks like GDPR, HIPAA and local insurance regulations regarding data handling and security. As such, you need to initially have a good understanding of the regulations you need to comply with and if AI can or cannot meet these requirements.?
- Skills: Building and maintaining an AI platform requires skilled professionals such as data scientists, AI engineers, machine learning experts and domain specialists who understand both the insurance industry and AI technology. If you don’t already have these skills, you need to create a plan to acquire or develop them.?
Building the AI platform?
Once you’ve prepared for AI, the next step is to actually build the AI platform. This involves the following steps:?
- Data consolidation: Insurance companies typically have access to a wealth of data, including claims history, customer data, market trends and even third-party data. However, this data usually sits in disparate systems. You need to consolidate the data into a single container (i.e., a data lake) that can handle both structured and unstructured data.?
- Data preparation: Raw data is rarely clean or ready for AI models as it is. You’ll need to prepare the data through cleansing, normalization and transformation. This can often be a laborious and tedious task, but it is one that is well-worth doing.?
- AI/ML tools: Once you have the data prepared, you are now ready for your first true steps towards AI. This involves selecting the appropriate AI tools. You can build custom models using tools such as TensorFlow or PyTorch, or you can jumpstart your efforts by going with a pre-built model tailored for insurance use cases, such as the ones provided by H2O.ai
, DataRobot and IBM Watson.?
- Model training: Once you have selected the platform, you need to begin training your AI models. There are two different types of learning tasks: supervised and unsupervised. For supervised learning tasks, such as claims processing or fraud detection, historical data is used to train models. For unsupervised learning, such as customer segmentation, the model will analyze patterns within the data. This step often requires significant computing power.?
- Integration with existing systems: To ensure that AI solutions provide value, they must integrate seamlessly with the company’s existing systems. Use APIs and middleware to integrate the AI platform with your existing systems, such as policy management, claims management and underwriting tools. Insurance companies that are using legacy technologies will find it more difficult to build these integrations than those that use more modern technologies.?
- Model deployment: Once deployed, models need to be continuously monitored to ensure they remain accurate and effective over time. Otherwise, you can encounter model drift, which is where the model gets out of sync with reality and becomes increasingly less useful.?
Final thoughts?
As AI continues to advance, insurance companies that invest in building a robust and scalable AI platform will be well-positioned to stay competitive in an increasingly data-driven industry. Whether through automation, personalized services, or advanced risk management, AI offers many opportunities for insurers.?
However, building an AI platform for an insurance company is a multi-step process that involves careful consideration and planning. If you want to deploy an AI platform, I recommend finding the right partner that can help you navigate all the potential challenges and maximize your investment.?
For a deeper dive into this subject, sign up for our upcoming webinar
with Insurance Business America!?
