Viewpoint: What Can Insurers Do About Extensive Attorney Involvement and High Settlement Costs?

Attorney involvement is one of the most critical determinants of ultimate claim costs. Significant costs include time from correspondence iterations, depositions, defense attorney costs, etc. These are present in determining whether the plaintiff’s attorney can negotiate a higher-than-usual settlement. Before we talk AI, let’s dig in a bit on why claimants tend to hire attorneys in the first place.

An experienced personal injury attorney can help navigate the complexities of the legal system, negotiate with insurance companies, and build a strong case, which may lead to a higher settlement or award for the claimant. Here are some reasons why claimants with legal representation may receive higher claims benefits:

Claimants are not required to hire attorneys to pursue their claims against insurance carriers or the company responsible for the injury, but they often do. Settlements for “typical” slip-and-fall accidents in California range between $15,000 and $50,000. Evidence suggests this range starts at $45,000 in Florida and goes much higher.

It’s unclear whether injured parties receive higher claim benefits when an attorney is involved. Attorneys claim this is the case and advertise relentlessly to make their point. But some carriers dispute this. Studies show that for “ordinary” injury claims, injured parties may sometimes receive lower settlements after attorney fees are deducted than what the carrier would settle the claim for if dealing directly with the claimant.

Regardless of whether a claimant will benefit from hiring an attorney, figures like this list of seven high-dollar settlements reached in injury claims are enough to entice many people to hire one.

$18 million for fall in manhole

A man walking in an alley behind a store in Brooklyn fell into an open manhole. The opening had no warning signs or barricades, and the man suffered severe head, neck and back injuries. He sued the city of New York for negligence and was awarded $18 million as compensation for his medical expenses, lost wages, and pain and suffering. He sustained spinal injuries and permanent disability from the accident.

$12.2 million for slip outside Miller Mart

A woman slipped and fell on a patch of ice outside a Miller Mart in Pennsylvania. She sustained serious back, neck and shoulder injuries, requiring multiple surgeries. The store owner was found liable for failing to remove the dangerous patch of ice from the property. The woman received $12.2 million in compensation for her medical expenses, lost wages, and pain and suffering.

$11 million slip and fall at gas station

A man slipped and fell while walking into a gas station in Texas due to a slippery substance on the floor that the store attendant had not cleaned up. He suffered severe injuries, including fractured vertebrae, which resulted in him being bedridden for several months. The court found that the gas station had been negligent in not providing a safe environment for its customers and awarded the man $11 million to cover his medical expenses, lost wages, and pain and suffering.

$11.1 million for falling off a ladder

A construction worker in California was using a ladder to reach a rooftop and subsequently fell off the ladder, suffering catastrophic injuries. The court found that the construction company had failed to exercise reasonable care in providing a safe working environment for its employees and awarded the worker $11.1 million in compensation for his medical expenses, lost wages, and pain and suffering.

$10 million for slipping on wet floor at grocery store

A woman slipped on a wet floor while shopping at a grocery store in Maryland. She suffered serious injuries, including broken bones and facial fractures, which required multiple surgeries. She sued the grocery store chain for negligence and was awarded $10 million as compensation for her medical expenses, lost wages, and pain and suffering.

$2 million for a fatal fall at Wilson College

A student at Wilson College in Pennsylvania fell from a fifth-floor balcony and died. An investigation found that the college failed to provide adequate safety measures on the balcony, such as handrails or guardrails. The court awarded the victim’s family $2 million in compensation for their loss.

$1 million for falling down stairs

A woman slipped and fell down stairs at an apartment complex in Massachusetts, suffering serious injuries, including broken bones, torn ligaments, and a severe head injury. She sued the property owner for negligence and was awarded $1 million as compensation.

Artificial intelligence (AI) can help insurance companies minimize the cost of general liability injury claims by streamlining various processes and providing valuable insights to support decision-making.

When it comes to reducing the severity of claims, techniques such as machine learning, natural language processing, anomaly detection, clustering, and time-series analysis can be used to build models that triage claims at the first notice of loss, predict which claims will be litigated, and discover fresh insights from relationships in unstructured data contained in medical bills, doctors’ reports, and the plethora of other documents associated with injury claims.

An emerging technology under the umbrella of AI is generative AI. GAI applies what it learns from creating a language model of the subject at hand, in this case, casualty claims, and guides adjusters to take specific actions supported by explanatory text statements. One way to think of GAI is that it integrates insights from the other AI techniques mentioned and communicates them to adjusters in an easy-to-understand manner.

Triaging a claim is essential in predicting how complex and severe it will likely become over time. Today, triage models built from a carrier’s data can assist in assigning the claim to the appropriate adjuster. By working with an AI software as a service (SaaS) provider, a carrier can combine their data with anonymized data from other carriers and captives to make their models more robust. The best of these models works not only with FNOL data but also updates its scores and alerts daily that new information appears on the claim file.

Similarly, models focused on predicting the probability of litigation by the injured party work with FNOL and all subsequent data. Once an attorney represents a claim, the best litigation models score plaintiff attorneys on the outcomes they have achieved on similar claims. They can even score defense counsel, in-house or out-of-house, to find which attorneys perform best from a defense perspective against the attorney a claimant has chosen to represent them.

Another type of model that leverages natural language processing of scanned and digital documents to uncover valuable items and relationships among them in unstructured data is now emerging. The best of these is revolutionary and works as a “second set of eyes” on the myriad paper and digital forms and other documents that healthcare providers, pharmacies, hospitals and clinics generate. Like triage and litigation models, the best performing models of this type act in the background and process new information when it becomes part of the claim file.

There are many misconceptions about how to get started with artificial intelligence. You cannot “throw a bunch of data” into a computer and expect sensical output. Data needs to be standardized and cleaned as much as possible. Leaders and workers from multiple disciplines need to be engaged in the initiative’s success and present to contribute their expertise. These disciplines include claims, information technology, data management, finance and operations. Lack of alignment and support will lead to substandard outcomes.

Change management needs to be handled deftly and effectively. It’s best to engage front-line workers (like claim adjusters) whose work will be impacted by artificial intelligence from the get-go to maximize the probability that they will accept and use the new approach. Finally, a strong measurement system needs to be agreed upon, created and utilized to establish, as best as possible, the impact the artificial intelligence system is having.

While developing an artificial intelligence system in-house may be desirable, AI can be outsourced to an advisory or software company in the insurance space. If the vendor is experienced and has many successful implementations, this can be a more cost-effective approach to your AI strategy. As mentioned before, there are SaaS providers of insurance AI that have established successful track records.

As many homegrown technology solutions and new processes get bogged down (“double the estimated time and triple the estimated price” is an axiom I’ve heard before), selecting a vendor may be more cost-effective. Some can even get you up and running with AI within three months. So, choose wisely!

General liability coverage is fraught with many claims that can be very costly for insurance companies to settle. Artificial intelligence is one weapon carriers and self-insureds can use to combat lengthy claims and high-dollar settlements. Several components of a comprehensive strategy need to be in place to leverage artificial intelligence. Carriers can try to develop these capabilities in-house or can rely on third-party software as a service providers who can often get them up and running faster.