Last week I attended the Deep Learning Finance conference organized by RE•WORK. As the event was smaller compared to many AI conferences I have attended, it had a more casual vibe which allowed for more open conversations about how people are using AI, some of their data challenges, and their overall goals.
The insurance industry is transforming through the use of AI. Huma Lodhi from the Direct Line Group shared the various ways that deep learning — which is a subset of machine learning focused on identifying data patterns and classifying information — is transforming the industry. The insurance industry is data-rich and based on rules that are centuries old. Lodhi explained that most insurance companies have data living in various silos, including text, image, and voice, but by extracting it and integrating it into a data lake, firms can use deep learning to automate processes that have traditionally relied on human involvement.
Direct Line uses deep learning to improve four key areas of their business:
1. Claims Management
Automating the claims management process with machine learning allows claims to be processed much faster than with traditional methods. According to a report from April 2018 by McKinsey entitled “Insurance 2030 — The impact of AI on the future of insurance,” machine learning will allow insurance companies to reduce headcount associated with claims management by 70-90% in 2030, even though it will remain a primary function for carriers. With processing time shrinking from days to hours, insurance firms will increase customer satisfaction while also creating internal efficiency. Some firms are already seeing this in action after deploying chatbots to interact with customers, and using machine learning to assess the total loss and process the claim. In some cases these technologies can remove the need for any human interaction whatsoever.
2. Fraud Detection
Fraud detection is another important application for machine learning in insurance. With the amount of payment channels on the rise leading to rapid growth in the number of overall transactions occurring worldwide, machine learning algorithms are used to develop automated fraud screening systems that are faster and more accurate than systems that rely on transaction rules combined with human reviews. Machine learning distinguishes between normal and fraudulent behavior, and adapts over time based on variations of fraud patterns in the data. This is the true power of machine learning as compared to traditional analytics methods — the ability to detect types of fraud that are similar but not identical to existing patterns, as well as the ability to spot completely new types of fraud altogether.
When it comes to pricing in the insurance industry, machine learning is used to assess customer risk, as well as optimize price based on customer segments. Algorithms are used to determine cost vs.risk based on the past behavior of each customer segment, which then hel