Adding Machine Learning to Claims Boosts Customer Experience

Original article appeared in LIMRA MarketFacts July 2022 

By: Megan Holstein, Esq., EVP Claims and Absence, FINEOS

Insurers are accelerating their digital strategies to serve the needs of customers who live in an Amazon and iPhone world. As insurers move forward with these digital initiatives, they are positioned to create loyalty that lasts a lifetime by redesigning their processes through the lens of the customer and harnessing the power of machine learning (ML). ML is a branch of artificial intelligence (AI). AI utilizes the computer to think like a human to solve problems (e.g., chat-bots) while ML is a computer that collects and distills data/information to help make decisions (e.g., Spotify music suggestions).

As more people prefer online experiences and are accustomed to internet marketing practices that “know” their online moves, it’s inevitable that customers expect their insurers to provide similar experiences during purchasing, enrolling, and filing claims. But how can insurers provide this digital transaction insight? The transformative opportunity is to pinpoint the exact moment of need in the insurance buying and claims filing experience.

Pinpointing the Pain

Integrating ML into the claims process from the customer viewpoint requires examining each step to understand any unnecessary time and effort. As they analyze each step, insurers should ask, “Is this what I want my customers or claims adjusters to do? Is this how I want them to feel? How much time and energy is this taking? Why am I requiring this of the customer or claims handler? Have I asked for this information before? Is there anything I can do to make it easier?” Asking these questions will surface new opportunities for improving the claims experience. The insurer can then feed that information into their ML tool.

Although the role of claims adjusters and claims managers sometimes goes unnoticed, they can make or break the customer experience. Equipped with the right information and technology, they can create an opportunity for customer loyalty in a matter of minutes or destroy a long-term relationship when lacking information or authorization.

ML technologies make it possible to improve the customer experience by reducing the time and effort required for claims handler processes like adjudication. Many insurers have found ways to use ML to capitalize on predictive analytics to identify claim patterns, triage claims faster, and manage workloads more intelligently. For example, ML can be used to determine the highest approval and lowest risk to enable quick payment determination.

Let the Machines Do the Work

ML and natural language processing can analyze massive amounts of information from multiple sources in near real-time, making it possible to expedite claims with automated workflows, intelligent claims handler assignments, and personalized responses to customers’ questions. When used well, ML can manage high-frequency inquiries in a way that feels natural to a customer. It’s also available 24/7.  With the right feedback, ML can provide deeper insights on customer issues and concerns that can differentiate claims service delivery and fuel satisfaction.

Insurance companies have become data companies; they possess an overwhelming amount of data. A commitment to harnessing that data in a meaningful way will unlock potent revelations. ML results are only as good as the data used to train the models. Insurers with a strong commitment to data quality, data governance, and having one source of truth for data management are more likely to succeed with ML implementation. The ability to easily connect and integrate data across internal and external sources significantly enhances the breadth and depth of its capabilities. In addition to quality data, insurers must have modern technology that can leverage this data gathering and intelligence; otherwise they will limit the value of their AI and ML investment.

Freeing People to Help People

The last major consideration for ML implementation success is having the right people with the right skills to set up and refine the models. Organizations often fail to invest in this area, particularly in the feedback loop that helps perfect the ML model. ML algorithms have to be explainable — to the business stakeholders, the customer, and regulators. And once the model is understood, leveraging the analysis and information available through ML gives claims managers and adjusters more time and bandwidth to respond to the customers they serve. Removing barriers and reducing the friction to adjudicating claims increases the value these professionals bring to their organizations.

The benefits of implementing ML in claims are clear. It can help customers navigate complexity in the digital world and provide the experience customers expect in their day-to-day lives. ML makes the roles of claims professionals more rewarding by eliminating tedious and time-consuming routine tasks. By leveraging its findings to inform decisions, ML can help insurers achieve many strategic objectives, particularly in the claims arena.

You may also be interested in