Source: Emerj | December 6, 2018
Author: Kumba Sennaar
The insurance industry is a competitive sector representing an estimated $507 billion or 2.7 percent of the US Gross Domestic Product. As customers become increasingly selective about tailoring their insurance purchases to their unique needs, leading insurers are exploring how machine learning (ML) can improve business operations and customer satisfaction.
However, no sources have taken a comprehensive look at the impact of AI among the leading insurance companies in the U.S. We researched this sector in depth to help answer questions business leaders are asking today:
- What types of ML applications are currently in use by leading insurance companies such as Allstate and Progressive?
- What (if any) tangible results have been reported on ML applications implemented by leading insurance companies?
- Are there any common trends among their innovation efforts – and how could these trends affect the future of insurance?
This article aims to present a comprehensive look at the four leading insurance companies and their use of AI. Our “top 4” rankings are based on the National Association of Insurance Commissioners’ 2016 ranking of the top 25 insurance companies.
Through facts and figures we aim to provide pertinent insights for business leaders and professionals interested in how machine learning is impacting the insurance industry.
Before we begin exploring each company, we’ll present the common patterns that emerged throughout our research in this sector.
Machine Learning at Insurance Companies – Insights Up Front
The most popular AI application from the top four industry leaders currently using AI appear to be:
- Chatbots/AI assistants: Responding to internal agent inquiries and providing guidance on business protocols (see Allstate below, or see our previous article on customer service AI use-cases).
- Driver performance monitoring: Machine learning algorithms are being applied to client data to help inform the development of products for insurance clients. (see State Farm and Liberty Mutual below).
- Insurance market analytics: Machine learning algorithms are being applied to interpret driver data in an effort to monitor market trends and identify business opportunities (see Progressive below).
(Note: For readers with an interest in ML finance use-cases beyond insurance, please refer to our “overview” article of machine learning applications in finance.)
In the full article below, we’ll explore the AI applications of each insurance company individually. We will begin with State Farm, the #1 ranking insurance company based on the 2016 National Insurance Commissioners ranking.
State Farm
In an effort to explore the ability of computer vision to identify distracted drivers, State Farm launched an online competition in 2016. The competition resulted in 1,440 participants and the company offered a total of $65,000, divided into 3 prize levels.
The dataset provided by State Farm was comprised of photos of drivers described as “2D dashboard camera images.” Participants were challenged with the task of classifying the perceived behavior of each driver using a list ten categories including:
- Safe driving
- Texting
- Operating the radio
- Talking on the phone
Competition scores were calculated using a log loss metric ranging from a minimum value of 0 to a maximum value of 1. The goal of a machine learning model is to achieve a score that is as close to zero as possible, which indicates the level of accuracy of a given model.
The first place application which achieved a score of 0.08739 utilized two neural network models and focused image classification on two main photo regions: the head region and the bottom-right quarter where the driver’s hand normally appears.
From a business strategy perspective, a patent application and the company’s Drive Safe & Save program provide evidence which suggests that driver data collection and interpretation will play increasingly important roles in State Farms’ approach to customizing insurance options and providing customer discounts. This improved use of data is consistent with one of the most important broad trends in AI and insurance (which we’ve written about in depth previously).
Liberty Mutual
In January 2017, Liberty Mutual announced plans to develop automotive apps with AI capability and products aimed at improving driver safety. Solaria Labs, an innovation incubator established by Liberty Mutual, has launched an open API developer portal which integrates the company’s proprietary knowledge and public data to inform how these technologies will be developed. An Application Program Interface or API is essentially a toolkit which provides the blueprint for building software applications.
The insurance company is reportedly experimenting with a new app to help drivers involved in a car accident quickly assess the damage to their car in real-time using a smartphone camera. The app’s AI component would be trained on thousands of images from car crashes and as a result could also provide damage-specific repair cost estimates.
This is a timely initiative considering that motor-vehicle fatalities in 2016 peaked at 40,200; the highest amount recorded in nearly a decade. From an economic perspective, in a single year the estimated healthcare costs totalled over $80 billion. The Bureau of Labor statistics estimates that the median salary of an insurance adjuster who assess auto damage was $63,510 in 2016.
In May 2016, Liberty Mutual announced the launch of its $150 million venture capital initiative, Liberty Mutual Strategic Ventures (LMSV). The early-stage venture fund will focus on innovative technology and services specifically designed for the insurance industry.
The VC firm has invested in companies such as Snapsheet, a smartphone application that reportedly allows users to receive auto repair bids from local body shops within 24 hours. Snapsheet’s president CJ Przybyl has stated that AI and machine learning are used to support the company’s data analysis process.
“The technology sector has been among the most volatile sectors of the stock market. Technology companies involve greater risk because their revenue and/or earnings tend to be less predictable, and some companies may be experiencing significant losses…Nonetheless, the potential for future growth in areas such as cloud computing, digital advertising, artificial intelligence, and interconnected devices remains compelling. Therefore, [Liberty Mutual] has continued to emphasize investments in these and other areas where we see opportunities to capitalize on rapidly emerging trends in technology.” –2016 Annual Report