AI as arbitration layer
For every action, there is an equal and opposite reaction. Or so the theory goes. In the real world of complex, multi-party transactions, this symmetry often breaks down.
In healthcare, a payer denies a claim and the provider may simply absorb the loss instead of mounting an appeal. The friction is structural: to fight a denial, providers have to go through multiple labor-intensive manual cycles, all the while not having enough granularity on actual fault or denial reason.
I'm watching this equation flip. AI adjudication has made the cost of an expert-level ruling approach zero, turning every dispute into a candidate for instant arbitration. When judgments are cheap and continuous, the optimal strategy is to arbitrate everything.
An AI-native arbitration layer can now:
Ingest full multimodal exhaust and reducing manual orchestration of data and analysis.
Learn the governing policy lattice through ingesting a huge corpus of data including contracts, medical necessity guidelines, trust-and-safety rules, fraud heuristics.
Opinionate on how to handle said arbitration based on win probability, when every interaction or claim is passed through a system of assessment, sometimes, past rulings or result data can result in a judgment call of conceding before it gets adjudicated.
Accurately assess arbitration outcomes, and eventually underwrite potential risk. This allows companies to become the clearinghouse of claims or in a debt collection model, for example, buying the whole claim book and take on the full risk and upside.
Flag potential violations earlier upstream in a workflow. Once embedded into a workflow, AI can incept policy checks earlier in the workflow, for example flagging violations in an ecommerce marketplace before it gets to a card network chargeback.
Create deep network moats by becoming a neutral conduit. A shared communication layer that settles outcomes also hosts a pool of proprietary data once these outcomes are adjudicated.
I wonder if this moves us closer to the pareto efficient frontier across many more cases where there are two antagonist parties. When each party can see and access transparent ruling, reducing information asymmetry - and continuous rulings teach actors how to avoid future conflict, shrinking the dispute set itself.