5 Ways Artificial Intelligence can Boost Claims Management

An aging population, reliance on AI, and new technological, environmental, financial and social risks, are top of mind issues for many claims leaders. An aging claims workforce, coupled with growing loss costs and expenses, have resulted in record high combined ratios. This presents insurers with a unique dilemma: how to ensure proper claims outcomes and lower claims spend, with an increasingly less experienced and knowledgeable talent pool.

Adopting available artificial intelligence (AI) today and preparing for future iterations, is critical for (re)insurers to address emerging transformative trends that shape our industry proactively and with the greatest impact possible. In fact, developing a comprehensive claims AI strategy, which reimagines an organization’s plan for people, process, technology and risk, is critical to achieve some of the estimated $100 billion in gross written premium, as well as associated expense savings.

Current Drivers of Claims Quality

To better understand where and how to infuse AI in the claims process, take a step back and look at the current drivers of claims quality. Aon’s Strategy and Technology Group (STG) benchmarked over 100 claims operations and found the greatest opportunity to drive claims quality improvement reside in the phases of contact, investigation and settlement.

Much of the work involved in managing these areas of the claims process requires extensive human resources, in addition to manual, often repetitive tasks that are prone to duplication and error. Embracing AI will also help close the retirement and skill gaps due to an ageing insurance workforce combined with less skilled claims handlers involved in the claims process.

Organizations that invest in claims AI solutions today can begin to soften the blow of impending retirements, while giving new claims talent the time and support to learn, grow and develop the expertise left behind by retirees.

It is important to understand the potential for AI capabilities to expand its focus from data capture, sorting, summarizing and analyzing, to an emphasis on prescribing recommended go-forward actions. It will allow (re)insurers to anticipate the challenges to come with emerging risks and plan for the best way to maintain and/or increase process efficiency and improve customer satisfaction.

How AI Helps Claims Now and in the Future

Strategic use of AI can optimize claims processes — from claim intake through claim payment — and yield efficiency and productivity benefits. Examples of these include:

Futureproofing Your Organization

With AI now ever more integral to claims processes, it’s often overwhelming to determine when, where and how to best implement it. Developing a comprehensive strategic AI plan that considers people (customer preferences and internal talent readiness), processes, technology and risk is fundamental to effective AI integration. Use these steps as a guide:

Respond to customer preferences

Customer preferences regarding human to AI engagement are shaped by factors such as claim type, claim severity/complexity and demographics. For that reason, it is imperative that insurers develop a full understanding of the types of customers they service and the types of claims they manage.

Commercial customers who deal with higher claim frequency are familiar with claims processes, and tend to have limited emotional ties to their claims. These customers could reasonably prefer a more digital and automated claims experience. Personal customers, on the other hand, when dealing with bodily injury, litigation or extensive damage, may better appreciate being serviced more directly by a claims professional to help set expectations, provide a degree of emotional support and answer questions unique to their claim. Age, geographic location and degree of education may also play a part. Considering these factors will help insurers develop an AI strategy that correlates best to their customers’ needs.

Identify talent readiness

An effective assessment of people also includes an examination of talent readiness for AI adoption. Identifying skills gaps, evaluating current and future staffing demands and adapting to the changing market are imperative. AI is not expected to remove people entirely from the equation, but rather enhance and improve processes allowing for human resource reallocation to more productive and meaningful work. While AI will allow employees to refocus on more substantive and analytical work, it also requires skills to effectively monitor and track AI functionality and use.

When determining what type and where in the claims process AI may be most impactful, assessing a claims professional’s current and long-term experience levels cannot be overlooked. With many retiring, (re)insurers must look for ways to replace or otherwise support their organizations to minimize the impact of not just losing claims handlers, but also the coaches and mentors that support their less experienced staff. AI can potentially provide those who are less experienced with comprehensive training and the opportunity to work more directly with supervisors and managers, while also spending extra time on the most meaningful phases of claims management.

Change can be challenging. Understanding the questions and concerns of your AI strategy and getting ahead of colleague pushback with transparent, meaningful and ongoing communication is key.

Map processes

In addition to a full understanding of potential people challenges, establishing a detailed workflow for all areas of claims processes is foundational to AI success. It is not enough to identify the high-level steps of managing a claim. Developing a workflow map of end-to-end processes for each line of business and all claims functions that capture time spent and resources allocated, brings to light gaps and opportunities for greater efficiency, productivity and improved quality.

AI support should focus on the following areas:

• Repetitive tasks
• Time consuming work
• Tasks involving multiple resources, systems and/or tools
• Inconsistently performed tasks that could benefit from standardization

Once gaps and opportunities are identified, the next step is determining how to incorporate AI into the process. Insurers could either partner with an AI vendor, acquire AI technology, or build the technology within their organization.

Consider the investment risk and reward

AI implementation should be balance sheet tolerant. The optimal method for AI implementation considers cost, a current state analysis and plans for AI to fully meet anticipated expectations.

Understand the risks of AI

Technological risks are those inherent to the AI solution and independent of human interaction. Because AI collects, stores and processes personal data, data privacy leaks can occur creating data confidentiality risks. AI may also be vulnerable to security risks. Algorithms are the parameters that train AI to develop insights. If an algorithm is leaked, the model can be copied, therefore compromising data. Finally, most AI solutions currently do not effectively track how it makes decisions. This lack of transparency makes it difficult to fix systems when unwanted outcomes occur. This can be problematic in the highly regulated insurance arena, especially when responding to inquiries or audits.

Usage risks are those that result from human interference. AI depends on the learned or trained data. Incorrect or biased data will produce inaccurate or distorted results. Additionally, there is potential for incorrect AI output. Users often lack awareness of what AI is, what it does and how it performs. Finally, AI could be used for a purpose outside of original intent, and thus compromised, causing adverse outcomes.

Although AI risks can be significant, implementing structured governance will help mitigate these threats. Effective governance consists of:

1. Tracking all business objectives;
2. Determining if the objectives are being met;
3. Assessing whether modifications are needed; and
4. Implementing and testing any modification.

Use Cases

As AI technology solutions evolve and grow, (re)insurers have various options to optimize the key claims quality drivers of contact, investigation, and settlement with AI, and to evolve their claims quality programs. In this section we delve into case studies detailing some of these options.

Click to download case studies

Next Steps to Boost Claims Management with AI

AI possess the power to not only transform the claims process, but also fill the skills gap due to an aging claims professional population and lack of new resources. The most successful insurers will be the ones who take the time now to create a strategic AI plan for the future. Insurers that have a full understanding of their people, process, technologies and risks associated with implementing this new technology will gain a competitive advantage over competitors. They will become more efficient, improve customer service and achieve better claims outcomes to significantly lower loss ratios and ensure future financial success.

Glossary:

What is AI?

With all the latest talk about AI, most claims professionals do not really know what it is or how it can be applied to claims functions. AI was created in the ‘60s and ‘70s and refers to mathematical models that learn patterns from data and enable faster or even automated decisions. As a machine-based system, AI can, for a given set of human-defined objectives, make predictions, recommendations and decisions that influence real and virtual environments. Although various categorizations of AI exist, AI can most simply be bucketed into two categories today: traditional and generative.

Traditional AI

Traditional AI relies on predefined rules and patterns to perform specific tasks. It has been largely restricted to an approach based on use cases, optimizing niches of existing operating models rather than fundamentally transforming them. It is designed to fulfil a specific purpose in a defined context, and strong reliance exists on labeled data for training, as well as human-crafted features. Put another way, traditional AI is often limited to the quality and quantity of the labeled data available during training. Examples include: automated insights, predictive modelling, intelligent alerting or platforms like Google, YouTube, Netflix or Amazon.

Generative AI

Generative AI operates through deep learning models and advanced algorithms, often without the need for highly structured data input. It can be a catalyst for transforming, redesigning end-to-end operating models by creating new content based on past inputs. Because generative AI is not strictly bound by fixed rules, it can create original and dynamic outputs without direct supervision. It learns from both labeled and unstructured data and can produce meaningful outputs that go beyond the training data, to the point of even summarizing large amounts of unstructured data (such as web or document content). Today, we can see this in Google Bard or ChatGPT. Future iterations of generative AI are expected to include prescriptive technology that not only predicts outcomes, but also suggests the actions to be taken based on the data it analyzes.

Proven use cases of traditional AI have already been adopted by many (re)insurers, while generative AI is just starting to take its foot hold with limited application within the claims process.

Discover More: Read more about our claims and client services for insurers, or contact our Strategy and Technology Group’s Claims team to learn how you can futureproof and boost your claims management processes.