Machine learning and artificial intelligence are hot topics right now – and for good reason. Machine learning (ML) and artificial intelligence (AI) are unlocking new insights, capabilities, efficiencies, and opportunities across industries and sectors.
Life insurance is no exception. Getting a grasp of what it is and how it can impact life insurance is critical to rethink challenges, spot solutions, and adapt in a changing industry.
What is Artificial Intelligence?
ML and AI are often used interchangeably but they are not the same thing. AI is the development of systems to replicate human intelligence (Forbes). It typically uses algorithms to decide the best course of action. Machine Learning is one of many components that can act as a decision maker within AI.
What is Machine Learning?
Machine learning (ML) is a manifestation of the more generic artificial intelligence concept. Essentially, it is a system that takes in data from its surroundings, extracts information out of them, and performs some optimal action in response. It is an application of pattern recognition algorithms that has the ability to adjust its classification model based on the data presented. That means that over time, with exposure to more data, the system will become a better classifier and thus make better predictions and carry out an intelligent action.
Think of a ML system like training a new staff member: by providing examples of how to complete a specific task, ML systems become capable of repeating similar judgement efficiently and with a high degree of accuracy. The difference with machines is that they also bring exponential processing power, zero downtime, and scalable automation to the team.
Why are Machine Learning and Artificial Intelligence Optimal for Life Insurance?
Machine learning has been an exciting proposition for some time and the current Big Data initiatives have made it the modern-day gold rush for every industry. There are countless applications, but the biggest barrier to success is access to data.
The huge amount of data that life insurance providers and reinsurers have from clients, claims, and coverage ideally position the industry to enhance services and expand capabilities. Plus, the industry’s strong regulations create a baseline that is well-established and easy to teach. Furthermore it provides a viable solution that can scale without increasing the strain on resources.
The Capability and Accuracy are Available Now
AI is no longer a distant dream - efficient ML systems have achieved unprecedented success with accuracy rates that are about to overtake human accuracy. The systems are powerful, and the capabilities are flexible, meaning they can be applied to a wide variety of processing challenges and administrative tasks that currently cause delays.
Many emerging ML systems use natural language processing (NLP) to automate services or approvals. You have probably encountered them in automated chat bots on websites offering help or document scanning technology that tells you where to sign digital agreements. In the insurance industry, these systems can be designed to immediately assess risks, calculate payouts, automatically complete claims audits, perform administrative tasks, and more.
How are These Technologies Being Used in the Life Insurance Industry Today?
1) To reduce inefficiencies with Attending Physician Statements (APS) in the underwriting process
The leading cause of delays in life and health insurance policy issue is often due to time lags in receiving the doctor’s reports that impact the underwriting process. Cookhouse Lab, an open innovation space based in Toronto, brought together a group of insurers and reinsurers to build a prototype solution incorporating Machine Learning to digitize, index and summarize data that was proven to reduce the current 60 days+ process significantly.
2) To provide instantaneous life insurance quotes
Legal & General America is leveraging the facial and image recognition capabilities of machine learning to provide instantaneous, automated assessments to individuals through selfies. In exchange for a selfie (a photo someone takes of themselves), applicants are provided with an estimate for life insurance based on an estimate of age, gender, and BMI.
3) To improve operational efficiencies and the customer experience
According to Forrester and Digital Insurance, insurers are actively experimenting with AI and ML to improve operational efficiencies and the customer experience. A few examples include:
- Using virtual assistants, speech analytics, and recommendation engines to aid customers in product selection
- Leveraging facial recognition to mitigate risk exposure within organizations
- Creating industry pre-trained solutions to provide internal training to customer facing roles
Life Insurance Consumers are Ready for Artificial Intelligence and Machine Learning
It’s not just the technology that is ready – consumers are eager to manage their coverage digitally and interact with insurance companies online. 74% of customers would be happy to get computer-generated insurance advice and millennials are twice as likely to buy their insurance online instead of dealing with a local agent. Trust in machines is growing as people opt for convenience and speed in their insurance services and expectations rise for personalized products and services, all with essentially no risk to decision accuracy.
Given the readiness of consumers and the current movement towards AI and ML in life insurance, the question is, are you ready to incorporate these technologies into your organization? My colleague will be sharing best practices for making this technology work for your organization in an upcoming blog. Subscribe to our blog so you don’t miss it.