By Patrick O’Neill, Founder and President, Redhand Advisors
While the world is still 40 years away from the space-living future of The Jetsons and their iconic flying car, deep learning technologies and artificial intelligence (AI) that exists today are quickly changing the way people live, work, and manage risk.
Historically, the risk and insurance industry has operated as a resource to respond to incidents after they occur. A RMIS of the not-so-distant future will focus more on distribution, underwriting, pricing, predicting and preventing claims, all enabled by the immense amount of data being collected by connected AI.
But the benefits of this digitization are not just relegated to customers. According to McKinsey, AI implementations have an opportunity to improve insurance processes and reduce operational expenses in the industry by as much as 40 percent by 2030.
The impact of technology could not come at a better time, as many businesses are experiencing an industry-wide staffing shortage, a consequence of both a retiring workforce and the post-COVID phenomenon known as the Great Resignation.
The question is no longer if technology can fill in the gaps of a human workforce, but how?
5 ways to leverage RMIS technology to improve productivity and reduce expenses
While the insurance industry has been slow to change and adopt current technologies, there is opportunity in automating processes that have historically been paper intensive. Here are five specific areas a RMIS can transform how we predict and process risk.
- Prediction based on historical outcomes
Automation allows the risk and insurance industry to work smarter. Instead of human time spent investigating where and when incidents are taking place, machine learning analyzes historical outcomes based on priorities and criteria you’ve programmed in to form predictions and recognize where clients have significant risk. This in turn streamlines auto-adjudication, where the RMIS can determine which claims should be paid and denied without a claims adjuster having to review each claim manually. This is already being done by large carriers and some TPAs.
We believe the capability and ease of use of auto-adjudication will become more mainstream in the coming years and can help ease the burden of a growing gap of claims administration professionals due to both the Great Resignation and Baby Boomer retirement.
2. Streamlining the claims process for claims handlers
An intelligent RMIS can build efficiency into the claims process by auto-assigning claims to the right people based on claim severity and staffing experience. Internally, this can mean sorting more routine claims to lower level or less experienced professionals for processing. This also pertains to claims handled by third-party administrators. For basic claims that do not need to be reviewed by an adjuster, the RMIS can engage auto-adjudication, again reducing the necessity for human touch.
3. Fraud detection
Historically, fraud detection has been managed through adjuster experience. Industry professionals know what to look for on a claim that may involve fraud. However, this process can also be streamlined by AI. Once you define fraud criteria and red flags for the system it can do the research for you. This process not only saves the adjuster time but can also actively find fraudulent claims that may have been missed by the human eye.
4. Added layer to your existing claims system
If your existing claims system doesn’t have AI capabilities, there are AI platforms that can be “bolted” onto your current RMIS. Essentially this means that the AI platform would pull information from your claims and underwriting systems to analyze for programmed parameters, such as fraud indicators and push the analyzed data back into your system with a rating or score. This entire process is automated and doesn’t require any additional staffing.
5. Self-service tools
Technology can serve as a portal for team members throughout the organization into the business’s core system, extending beyond claims into risk management tasks as well. For the many tasks that require individuals to work with others in the organization, the tech can be used to find or collect reports instead of reaching out to other departments. It can also provide a channel through which other parties can access self-service tools instead of sending out an email to trigger human action. For example, reporting a claim or requesting a Certificate of Insurance can be automated as long as the parameters are established within the self-service tool. Simplifying these small tasks can free up time for employees to work on more consultative problems and items that move the envelope on business priorities.
In today’s job market, technology and AI can provide a competitive edge to the risk and insurance industry by removing some of the tedious tasks that could prevent staff from focusing on more consultative relationship building, while also improving a business’ operational efficiency and accuracy.
For more information on how today’s RMIS systems can transform your productivity and lower your operational expenses, schedule an inquiry call with Patrick.
Footnote: 3 Basic Types of AI to Know
Not all artificial intelligence (AI) is created equal. In other words, finding the right AI for your organization starts with understanding the different types. Here are some basic terms to understand before deciding what technology is right for your business.
Machine Learning — a form of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Natural Language Processing (NLP) — a type of AI that helps machines process and understand the human language so that they can automatically perform repetitive tasks.
Robotic Process Automation (RPA) — an AI software that automates tedious, manual digital tasks.