The world of insurance has always been dependent on data to calculate risk and come up with personalized ratings. Today, the sector is undergoing a profound digital transformation thanks to technologies such as machine learning.
Insurers are using machine learning to increase their operational efficiency, boost customer service, and even detect fraud. And there is a plethora of insurtech startups, eager to take a slice of an insurance industry.
Therefore, in this blog post, we are going to share with you several key factors driving machine learning in insurance, some ways machine learning is changing the insurance landscape and challenges in implementing machine learning.
Table of contents
- Key enablers of machine learning in insurance
- Potential Use Cases of Machine Learning in Insurance
- Challenges for insurers when using machine learning
Key enablers of machine learning in insurance
1. Ability to talk back
Natural-language processing algorithms are continuously advancing. AI is becoming proficient at understanding spoken language and at facial recognition, helping to make it more useful and intuitive. These algorithms are evolving in unexpected ways, as Google found when Google Translate invented its own language to help it translate more effectively.
2. Making Use of Internet of things (IoT) data
The volume and velocity of data from IoT will drive the need to automate the generation of actionable insight using advanced machine learning tools. According to Gartner, in 2020, approximately one fifth of enterprises employed dedicated people to monitor and guide machine learning (such as neural networks). The notion of training rather than programming systems will become increasingly important.
3. Open source everywhere
As data becomes omnipresent, open-source protocols will emerge to ensure data is shared and used across. Different public and private entities will come together to create ecosystems for sharing data on multiple use cases under a common regulatory and cybersecurity framework.
4. Smart everything
Enterprises are looking to use advanced machine learning to drive smart, automated applications in fields such as healthcare diagnosis, predictive maintenance, customer service, automated data centres, self-driving cars and smart homes.
Potential Use Cases of Machine Learning in Insurance
1. Claims processing
Insurers are using machine learning to improve operational efficiency, from claims registration to claims settlement.
Many carriers have already started to automate their claims processes, thereby enhancing the customer experience while reducing the claims settlement time. Machine learning and predictive models can also equip insurers with a better understanding of claims costs.
These insights can help a carrier save millions of dollars in claim costs through proactive management, fast settlement, targeted investigations and better case management. Insurers can also be more confident about how much funding they allocate to claim reserves.
For instance, Tokio Marine has an AI-assisted claim document recognition system that helps to handle handwritten claims notice documents using a cloud-based AI optical character recognition (OCR) service.
It reduces 50 percent of the document input load as well as complies with privacy regulations. AI is used to read complicated, ambiguous Chinese characters (Kanji), and the “packet-like” data transfer system protects customer privacy.
The results: over 90 percent recognition rate, 50 percent reduction in input time, 80 percent reduction in human error, and faster and hassle-free claims payments.
2. Fraud prevention
In accordance with Accenture, insurance providers experience a loss of an estimated US$30 billion a year to fraudulent claims. Machine learning helps them identify potential fraudulent claims faster and more accurately, and flag them for investigation.
Machine learning algorithms are superior to traditional predictive models for this application because they can tap into unstructured and semi-structured data such as claims notes and documents as well as structured data, to identify potential fraud.
3. Insurance advice
Machines will play a significant role in customer service, from managing the initial interaction to determining which cover a customer requires. With reference to a recent survey, a majority of consumers are happy to receive such computer-generated insurance advice.
Consumers are seeking personalised solutions—made possible by machine learning algorithms that review their profiles and recommend tailor-made products.
At the front end, insurers are making wider use of chatbots on messaging apps to resolve claims queries and answer simple questions.
For instance, Allstate, which partnered with EIS (Earley Information Science) to develop a virtual assistant, called ABle (the Allstate Business Insurance Expert).
ABIe assists Allstate agents seeking information on Allstate Business Insurance (ABI) commercial insurance products. Before ABle was deployed, agents were accustomed to selling personal lines products such as health or homeowners insurance.
However, when the company decided to shift its focus to selling commercial insurance, many agents had a slow learning curve and encountered challenges in accessing the information they needed to effectively communicate with potential clients.
As a result, Allstate’s sales support call centre was consistently flooded with inquiries from agents. Ultimately, “long wait times” translated to “lost business opportunities.” ABle provides agents with step-by-step guidance on “quoting and issuing ABI products,” using natural language. EIS claims that ABle processes 25,000 inquiries per month.
4. Risk management
Insurers use machine learning to predict premiums and losses for their policies. Detecting risks early in the process enables insurers to make better use of underwriters’ time and gives them a huge competitive advantage.
Progressive Insurance is reportedly leveraging machine learning algorithms for predictive analytics based on data collected from client drivers.
The car insurer claims that its telematics (integration of telecommunications and IT to operate remote devices over a network) mobile app, Snapshot, has collected 14 billion miles of driving data. To encourage the use of Snapshot, Progressive offers “most drivers” an auto insurance discount averaging US$130 after six months of use.
Challenges for insurers when using machine learning
1. Correct data source
The quality of data used to train predictive models is equally important as the quantity, in the case of machine learning. The datasets need to be representative and balanced so that they can give a better picture and avoid bias. This is important to train predictive models. Generally, insurers struggle to provide relevant data for training AI models.
2. Data security
The huge amount of data used for machine learning algorithms has created an additional security risk for insurance companies. With such an increase in collected data and connectivity among applications, there is a risk of data leaks and security breaches. A security incident could lead to personal information falling into the wrong hands. This creates fear in the minds of insurers.
3. Difficulty in predicting returns
It’s not very easy to predict improvements that machine learning can bring to a project. For example, it’s not easy to plan or budget a project using machine learning, as the funding needs may vary during the project, based on the findings. Therefore, it is almost impossible to predict the return on investment. This makes it hard to get everyone on board the concept and invest in it.
4. Training requirements
AI-powered intellectual systems must be trained in a domain, e.g., claims or billing for an insurer. This requires a separate training system, which insurers find hard to provide for training the AI model. Models need to be trained with huge volumes of documents/transactions to cover all possible scenarios.