As technology advances and AI becomes more common within enterprises, the number of insurers that utilize the powerful insights that AI and machine learning can provide is increasing quickly. While AI is undoubtedly benefiting insurance organizations, there is still a risk in blindly trusting the recommendations, insights, or predictions AI provides.
Why?… Because when we don’t fully comprehend how AI algorithms (or processes) make decisions, we cannot optimize all that AI has to offer. This leads to potential biases, errors, and costs caused by AI.
I know what you’re thinking… AI is extremely complex and opaque to the majority of humans. How are we supposed to understand its decision-making processes, insights, predictions, and more?
The answer? Explainable AI (XAI).
What is Explainable AI (XAI)
XAI is a new and emerging area attempting to focus on increasing the transparency of AI processes. The overall goal of XAI is to help humans understand, trust, and effectively manage the results of AI technology.
XAI optimizes the use of AI in your environment through an in-depth model and data investigation of your current AI system(s). The results are what all insurance teams and companies like to see: increased efficiency, decreased costs, and ensured fairness of decisions.
XAI in Insurance
If you work in the insurance industry, you may understand the concept of AI but aren’t sure how insurance companies are leveraging it, how XAI can benefit them, or why they should invest in XAI.
Let’s take a look at some examples of ways Explainable AI can benefit insurers and their customers:
- Explainability and transparency of decisions: Insurers have to make decisions that affect their customers monetarily and often use AI to help make these decisions. As stated earlier, AI’s decision-making process is extremely complex. How can insurers explain to their customers and regulators why certain decisions were made?
One of the most attractive benefits for both the insurance companies and their customers is XAI’s ability to give insurers an understanding of how AI comes to a conclusion. So much so that insurers can explain precisely why or how a decision was made to their customers and regulators.
- Safely use AI in a regulated environment: In an industry where AI-based decisions have significant impacts on customers, insurers must follow and meet certain regulatory laws.
With XAI, insurance companies can ensure fairness in AI-based decisions, such as claim approvals and claim costs. XAI does this through an in-depth analysis of the current AI process, detecting any unknown biases or errors that lead to incorrect decisions.
- Improve Fraud detection: According to the Coalition Against Insurance Fraud, fraud schemes steal at least 80 billion dollars per year in the US alone. Due to this, insurance companies must have an AI-powered fraud detection system in place to prevent revenue loss. AI has the ability to self-learn and evolve with changing fraud schemes, but it can only adjust so much.
XAI provides a deep dive into the thinking process of the fraud detection in place, finding the areas in which the system is not performing optimally. As a result, the accuracy of true-positive fraud detections will increase, and false-positives will decrease, ultimately saving insurance companies money on unnecessary follow-up investigations.
The Future of XAI and Insurance
With the impact insurance companies can have on their customers, it is essential to have explainability, transparency, and understanding of the AI in place. XAI will ensure AI-based decisions are unbiased and fair, something insurers, customers, and regulators can all agree is critical. Although XAI is still early in its growth, it is already being implemented by large companies like Capital One and is backed by the US’s Defense Advanced Research Projects Agency and the EU’s European Commission Joint Research Centre. The future of AI in insurance is Explainable AI.
