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Out of all the obstacles posed to the insurance industry in the past few years, the largest hurdle the industry has yet to solve for is the fallout from the fast-changing workforce.
According to a recent Jacobson Group/Aon study, over half of insurance companies planned to expand their staff throughout 2022. The two reasons cited? Planned business expansion and current understaffing challenges. While most of these understaffing issues are due to long-term, experienced employees chasing newer opportunities or feeling the impact of the Great Resignation, there are unexpected knowledge gaps across organizations that are not being filled fast enough to keep up with current customer demand.
Making this workforce predicament worse is the growing gap in the insurance industry’s technology stack. Many insurers are still using incongruous systems that are not interoperable with one another, which makes for unnecessary workflow gaps and redundancies. This, combined with the post-pandemic boom in remote workforce models, means that insurers are facing a major operational dilemma.
As an industry, we need to evaluate all the current roadblocks and lean on digital transformation to help fill the gaps in talent and technology on the road to innovation. Once we achieve this, we’ll be able to operate more efficiently and service customers in the face of ongoing changes in the workforce and economic uncertainty.
What are the current operational challenges and why are they important?
The traditional claims process is based on several different types of software capabilities, which are forced to work together while only addressing individual pieces of the claims lifecycle at a time. Combined with manual processes and refinement, these disparate software systems create a cumbersome process. Additionally, while each of these capabilities within a single core system meet specific needs, this piecemealed approach to the claims lifecycle can often be the reason for unintended misinformation, data gaps, and errors.
Even worse, the overall decisions process is often overlooked as being the reason for a lack of interoperability that creates new silos in information-sharing, which has organizational-wide impacts. These silos create a trickle-down effect on how claims are handled, which adds frustration to the policyholder along with the internal employees responsible for their overall satisfaction.
Let’s use the example of a common two-car accident. A claim comes in for a two-car accident with minor damages and no reported injuries with an alert attached indicating potential patterns of fraud. The claims-processing team relies on a single system to upload data about the case and analyze it to detect any patterns of fraud or potential risk by manually adding other loss information such as the repair estimate.
Simultaneously, the team is also double-checking any work done by the AI. A third system then compares the claims team’s findings with outsourced information, such as corroborating documents like police or witness statements. Ultimately, it might be found not to be fraud and that the flag was an error. This entire process typically spans over two weeks of work, which means lost time invested towards research, app toggling, and manual analysis. Even worse, the already unsatisfied customer has been left on hold with a damaged car and no response.
If the insurance industry wants to keep up with innovation, we cannot continue to rely on the same processes, as the playing field is constantly evolving. The future of insurance innovation must include automated digital touchpoints and expanded ecosystems that are compatible with other systems.
The role of digital transformation and automation in decisioning
The practice of leveraging technology to automate business processes is not novel for the insurance industry. The reality, however, is that although insurers have already been using traditional automation methods, the tools aren’t serving them in truly impactful ways for their business and policyholders. Digital transformation plays a key role in the claims lifecycle process, and this can take the shape of automated rules-based engines or business process management (BPM) software. While an improvement, the rules-based engines are cumbersome to update and manage creating a lag in responding to market conditions.
The foundational tech stack for any insurer serves as the core transactional system, providing functions such as policy administration, claims management, billing, and agency management. These core systems rely largely on automated engines—but automation alone isn’t enough.
There must also be an intelligent decision engine to sit above the transactional systems, which ingests data from core systems and the growing ecosystem of third-party applications and data sources.
How intelligent decisioning can help
There are two key benefits of an intelligent decision engine from a rules-based engine or BPM. The first is that it can evaluate a larger volume and variety of data – which includes unstructured data such as file notes, images, emails, or PDFs.
Even the most basic claims can contain complexities that force automated systems to assign tasks to an adjuster, utilizing employee time to verify minor details. Incorporating an intelligent decisioning engine directly into the claims process engages this complexity to compare the details against data found throughout the larger core systems. As a result, this frees up the valuable, limited time of an adjuster and reduces the overall length of the claims process, resulting in a “touchless” claim. Unfortunately, it is found that less than 10 percent of claims are leveraging intelligent decisioning for processing in this way.
Secondly, intelligent decision engines use Artificial Intelligence (AI) and Machine Learning (ML) models to provide core systems with another layer of accuracy and operational efficiency. Rather than relying on a set of predetermined (and often outdated) rules, it learns from previous claims data and continuously improves as it takes in new data. Intelligent decisioning systems also analyze previous claims to connect the dots around potential trends. For example, a claims professional can leverage intelligent decisioning to spot a particular policyholder’s claim history that may be suspicious, or expedite the process based on previous decisions for similar types of losses. This ability to provide greater levels of accuracy and efficiency streamlines the entire claims lifecycle, from first notice of loss to resolution, which reduces losses, ancillary loss costs (i.e., loss of use or storage) and improves overall outcomes and experiences for the business and policyholder.
Intelligent decisioning technology brings value to the workplace
Let’s re-examine the previously mentioned two-car accident example, this time using an intelligent decisioning engine that works as a top layer in an insurer’s core system.
Suppose that the same claim is submitted with corroborating information and a system ensures the information is correctly submitted, sending out an alert to a specific claim handler. The alert reads, “potential patterns of fraud detected, police report is missing timeline.” Utilizing the full context of the fraud pattern alert, the claim handler can leverage the AI within the decisioning engine to double check any missing details, corroborate the police report with public records, and all other claim information at once. Then once the claim handler confirms the missing timeline on the police report, it is uploaded into the system and the AI automatically inputs all the new and missing information into the claims file for a final review by the claim handler.
Within days, as opposed to weeks, the claim handler can confidently identify that there was no evidence of fraud and ensure that the policyholder’s claim is advanced before the week is over. With customer turnover costing $470 billion in premiums annually, resolving the policyholder’s claim quickly can make or break their loyalty and impact your bottom line.
Conclusion
Ultimately, truly effective claims decision-making cannot be done in silos—your tech stack should be collaborating to enable claims processes to be more impactful and efficient, while being even more streamlined for your employees to use. This can be achieved by implementing technology systems that are curated to add compatible layers of insight and automation to each step in the claims decisioning process.
The goal of bringing digital transformation technology, such as ML and AI, into the insurance industry is to transform the way we operate—not to make it more complicated or difficult.
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