Auto subrogation teams send demand packages without knowing how the adverse carrier will respond: how long they will take, what they will counter-offer, and what they will ultimately accept. They also lack a reliable view of how claims of a similar profile have actually been settled. A handler’s experience with a specific adverse carrier may provide some insight, but it is limited to the specific claims they have managed.
The next gain in auto recovery will come from more than automation of recovery tasks. It will also come from a better understanding of the adverse carrier before the demand is sent. That understanding has two halves:
Each half is impactful alone. Together they enable a handler to strengthen demands and negotiations. AI agents that bring both to every file turn negotiation from instinct into strategy. Insurers that embrace this insight will be able to recover more and recover faster. The strength of evidence of the case is critical, but equally as important is an understanding of who you are up against.
This paper explains how adverse carrier behavior and comparable claims insights strengthen the recovery lifecycle, from detection to arbitration. The intelligence strengthens the filings, increasing the likelihood of a quicker settlement and decreasing the time and fees associated with arbitration.
A demand is a bet on how the adverse carrier will behave and how the claim will resolve. The recovery specialist risks effort and time to organize, create and negotiate the demand. Estimates indicate that insurers are missing $15 billion in subrogation potential each year*. Part of the challenge to maximize recovery is knowing when to escalate to arbitration and when to settle. Most subrogation teams today place bets with limited knowledge based on the facts of the claim, the handler's memory of past files, and a guess.
This creates three problems:
Two blind spots sit behind all three. One is the counterparty - how this carrier responds and counters. The other is precedent - what similar claims recovered, and how often they had to be escalated to arbitration. Both need understanding.
*Celent, Boosting Subrogation Efficiency for a Healthy Bottom Line
Comparable claims insights show a handler how claims of a similar archetype actually settled, so the demand and the settlement range rest on precedent rather than recollection, white still considering the unique facts of the specific claim.
An AI agent analyzes similar claims of the same profile and summarizes those claims with similar circumstances alongside the outcomes from those cases. A claim's archetype is its fact pattern: the type of incident, the number of parties and vehicles involved, and the evidence on file. The example below is an illustration of a group of similar claims and the resulting intelligence provided when looking across a set.
The comparison answers questions that neither the file nor the carrier profile can: a fault assessment typical for this fact pattern, which evidence was used in the claims that recovered, and how often did similar cases end up in arbitration. It is evidence about the claim's likely path, not about the counterparty.
Adverse carrier intelligence indicates to a handler how this particular counterparty tends to behave, so the first demand and every reply can be planned rather than guessed.
Before a demand is sent, an AI agent can assemble a profile of the adverse carrier, including typical response timeframes and how the carrier regularly negotiates based on specific claim attributes. An example is provided below.
The profile answers questions a single file cannot: is this carrier slow or just busy, is a 50% counter its standard opening move, and does it tend to hold firm until arbitration. It is evidence about the counterparty, not about the claim. This information gives the handler a clear, data-driven basis for how to open, when to hold, and when to settle or escalate.
The two lenses are strongest when they are considered and cross-checked against the claim's specific evidence. The agent layers in the specifics of case strength, based on the incident details and the evidence on file, then sets that against the carrier profile and the comparable claims. Together they give the handler a clear, evidence-backed basis for how to approach the claim, demand, when to hold firm, and when to settle or escalate.
Each insight is strong, but even more effective when both views are considered together. And the insights are only as credible as the data behind them. An agent builds carrier profiles and comparable claim sets from the carrier’s own historical claims and its arbitration decisions. The more complete the claim file, the more comprehensive this analysis. The data must then be anonymized and aggregated to provide analysis, while removing any bias or another carrier's identifiable data.
Data-driven insights for recovery pursuit offer a proactive approach to improve recovery rates and reduce cycle times:
Consistent approach: Every handler works from the same carrier profile and comparable outcomes, not personal memory.
Readable counter-offers: Carrier habits plus similar-claim results show whether a counter is a real position or an opening move, reducing early acceptance and over-holding.
Earlier arbitration calls: Unlikely settlements reach arbitration/litigation sooner, and filing fees are eliminated for cases that were never going to settle.
Scaled expertise: The agent acts as a training and guidance tool, easing the talent gap and staff turnover.
Handlers decide: The agent recommends, and insurers set the automation level by line of business.
The potential offered for more successful recoveries brings a compelling opportunity for data and IT teams.
One clean data foundation: Both lenses depend on structured fields and free-form text (loss descriptions, adjuster notes, third-party statements). That same foundation serves the entire organization.
Closed claims gain value: Historical data is the best starting point and can reveal recoveries on claims previously closed, so the data you already hold is put to use.
Explainability and audit: Every recommendation provides a visible rationale and audit trail to defend positions and answer regulators.
Human-in-the-loop control: Insurers set oversight levels by line of business, so IT and compliance can match automation to their own risk appetite.
As agentic AI gains momentum, it is in the foreseeable future when a demand built by one carrier's agent is reviewed by another's. When both sides have automated the process, the advantage moves to the side with better data on how the other behaves and on how comparable claims have been resolved.
Carriers that negotiate blind will meet counterparts that do not. The gap will show up in recovery rates before it shows up in anyone's strategy deck.
|
|
Shift Subrogation: Intelligence at every step of the recovery lifecycleShift Subrogation agents manage recovery pursuit across exposures, including PIP, collision, rental and MedPay, whether the referral was detected by AI or flagged by a handler. Each step feeds the next, and what the agent learns early sharpens the negotiation later. Detection. Generative AI reads free-form adjuster notes, statements and police reports to surface subrogation indicators a manual review could miss. It applies state negligence law and statutes of limitations to judge viability, in a fraction of the time a handler takes. Liability calculation. Agents evaluate points of impact, vehicle movement, statements and rules of the road the same way every time, removing the variability of subjective judgment. The result is a precise fault assessment and an explainable, defensible reasoning, updated as new evidence arrives. Comparable claims test that percentage against how similar claims are resolved. Recovery preparation. The agent triages the case, organizes the evidence and sets case strength considering the adverse carrier profile and the comparable claims shown above. Handlers know how firmly to negotiate, and what a realistic range looks like, before they begin. Demand package creation. The agent builds the demand, supported directly by the evidence - duties owed/breached, state laws and statutes, police report, insured statements or witness statements. Carrier behavior and comparable outcomes inform how the package is framed and with what evidence to lead. It is ready to send with minimal handler intervention, based on the insurer’s parameters. Negotiation. When the adverse carrier counters, the agent tests the counter against the original evidence, the carrier's known behavior and the outcomes of comparable claims, then recommends whether to accept, counter or escalate. The handler decides, with additional evidence attached where it strengthens the position. Arbitration. If negotiation fails, the case is escalated with the full demand package and evidentiary record. Better preparation with insights that strengthen negotiations mean fewer cases reach this step, and fewer filing fees are paid.
|
|
Every step of the workflow can include human review, and the insurer decides how much automation to allow, by line of business. Handlers control every decision and what is automated, including whether to follow the guidance provided by the carrier profile and comparable claims.
Learn more about Shift Subrogation.