From Reactive, Fragmented Claims Management to Empowered, Consistent Claims Resolution
By Yariv Lissauer, CEO at Canotera
Insurance is fundamentally a business of prediction. Carriers forecast frequency, severity and aggregate losses to price risk and allocate capital. Yet once a complex claim arrives, some of the industry's most consequential financial decisions remain surprisingly reactive and fragmented.
Claims management has traditionally been an iterative process: adjusters assess evolving information, defense counsel provides evaluations, reserves are revised as new facts emerge, and senior claims professionals become involved as severity becomes clearer. Filling these tasks manually makes the process fragmented and reactive, with different professionals reaching materially different conclusions from the same information. Furthermore, it causes significant risks to become apparent only after they have already materialized.
In today’s casualty environment, the cost of recognizing those risks too late is rising sharply.
Social inflation is pushing liability claim costs beyond ordinary economic inflation, fueled by changing jury attitudes, increasingly sophisticated plaintiff strategies, litigation funding and nuclear verdicts. The NAIC identifies nuclear verdicts as a major contributor to social inflation, with particularly significant exposure in auto, product liability and medical liability.1
This changes the fundamental question for carriers. It is no longer simply “How efficiently can we process this claim?” but increasingly “How early can we understand where this claim is heading, and act while there is still time to influence the outcome?”
Prediction changes the claims equation
Generative AI has dramatically improved our ability to read and structure enormous volumes of unstructured information: medical records, adjuster notes, pleadings, correspondence, expert reports and demands.
But reading a claim is not properly evaluating it.
Effective predictive analytics requires combining structured claim information with mathematical models trained against large populations of resolved claims with known trajectories and outcomes. Rather than asking an LLM to guess a settlement number, the objective is to generate calibrated forecasts: outcome ranges, escalation probabilities, and expected trajectories.
And the value is not limited to claims already in litigation. Predictive intelligence can begin pre-litigation and evolve continuously throughout the claim lifecycle as new information becomes available.
The more complex the claim, the greater the potential economic value of knowing something important earlier.
The risk is already visible in carrier portfolios
Travelers provides a compelling example of how predictive analytics can change intervention timing. Its claims organization uses predictive models to assess the likelihood that a claimant will retain an attorney, drawing on data covering more than 200,000 plaintiff attorneys and 50,000 firms. The purpose is not simply prediction: identifying the risk earlier allows claims professionals to intervene earlier and potentially avoid unnecessary litigation.2
That principle can extend much further.
Imagine a carrier has 20,000 open casualty claims. Predictive analytics identifies 300 with unusual escalation characteristics, 75 where current reserves materially diverge from modeled outcomes, and 25 where the economics favor immediate senior review or early resolution.
The technology has not replaced the adjuster. It has materially improved evaluation accuracy and velocity and also indicated to the organization where adjusters' attention is most economically valuable.
Consistency may be as important as accuracy claims. Organizations also face a less visible problem: fragmentation.
Two experienced adjusters or attorneys can reach materially different evaluations of the same claim. Geography, individual experience, outside counsel and organizational silos can all affect the assessment.
Predictive analytics provides a common reference point.
The question becomes not merely: “What do I think this claim is worth?” but: “What does the evidence indicate, what have comparable claims actually done, and why does my professional judgment differ?”
That fosters greater consistency in reserving, settlement strategy, escalation and allocation of legal resources, without removing human judgment.
From legal spend to intelligent allocation
The same principle applies to adjusting and defense costs.
Carriers spend enormous amounts reviewing files, obtaining repeated counsel evaluations, conducting discovery and advancing cases through litigation. Not every dollar of that expenditure has equal value.
If analytics can identify earlier which claims genuinely warrant intensive investigation, senior adjuster involvement, specialist counsel or trial preparation, and which are economically better suited for earlier resolution, carriers can allocate adjusting
expense, panel counsel spend and management attention according to predicted risk rather than process convention. The future claims organization therefore isn't autonomous. It is empowered.
Predictive intelligence can operate in the background, integrated directly into the carrier's existing claims environment, while continuously updating as the file develops rather than requiring adjusters to adopt another disconnected system.
Against social inflation and increasingly unpredictable litigation outcomes, carriers cannot eliminate uncertainty. But they can become much better at seeing it earlier.
And that may ultimately be the most important promise of predictive claims analytics: not predicting the future perfectly, but giving claims professionals the foresight and muscles to impact it.
