Begin on one claims line. Catch built-up claims and coverage-check misses at intake; prove a leakage delta before any pl
Leakage is buried in the loss-adjustment line and it never s
You can pilot leakage detection at first-notice-of-loss on one line without touching the core platform.
What changes when AI orchestration runs the loop
Not 'buy more analytics' -> 'capture subrogation/reserve leakage your current model misses without burying adjusters in
You've tried analytics on leakage; the false positives eroded adjuster trust. A model trained on your own adjuster dispositions raises fidelity so the catch survives.
Not 'more STP rules' -> 'clear the submissions your rules engine bounces.'
You've got rules-based STP; exceptions still pile on underwriters. A model trained on your underwriting dispositions clears more straight-through and routes the rest with context.
Not 'more documentation' -> 'prove the model on demand to the examiner and the reinsurer.'
You've documented governance; testing and proof stay point-in-time. A model trained on your own validation history continuously tests and produces explainable, owned evidence - the floor reinsurers hold you to.
Not 'more scoring' -> 'connect the evidence your scoring can't.'
You score claims; building the network is still manual. A model trained on your SIU history connects parties and corroborating evidence across sources and shows its work.
Not 'more AMS automation' -> 'capture the bundles and referrals your generic prompts miss.'
You've tried automated prompts; they're generic. A model trained on your book's real cross-sell patterns surfaces the right gap for the right client.
Where the work changes
Five frames in this vertical's language — leak, operational, governance, analysis, growth.
Leak / value-capture: Not 'buy more analytics' -> 'capture subrogation/reserve leakage your current mo
Leakage is buried in the loss-adjustment line and it never shows up in the monthly reports.
- Claims leakage from missed subrogation, stale reserves, inconsistent coverage checks at settlement.
- Loss-adjustment expense undermonitored; unverified vendor/defense billing.
- Built-up claims (padded real losses) hard to catch without filters at first notice of loss.
- Combined ratios near/above 100 in many lines; recovering even 1% of loss payments is material.
Operational throughput: Not 'more STP rules' -> 'clear the submissions your rules engine bounces.'
Submissions pile up at intake and underwriters can't even start until someone organizes the file.
- Submission intake is the most operationally intensive stage - underwriters can't review until the file is organized and entered.
- Multi-channel submissions (portals, email, broker platforms) arrive unstructured.
- Manual triage caps how many risks an underwriting team can clear.
- Servicing/endorsements/renewals add recurring throughput load.
Governance & audit: Not 'more documentation' -> 'prove the model on demand to the examiner and the r
Regulators and reinsurers now demand provable, tested model governance and I can't produce it on demand.
- NAIC AI Model Bulletin (adopted ~24 states) requires a written AIS Program: governance, testing, documentation, vendor oversight.
- Market-conduct exams (and the 2026 NAIC AI Systems Evaluation Tool) demand document production on AI use, bias testing, and controls.
- Many insurers still don't regularly test models for bias - a live exam gap.
- Reinsurers use the bulletin as the evidentiary floor at treaty placement - weak governance blocks capacity.
Analysis / diagnosis: Not 'more scoring' -> 'connect the evidence your scoring can't.'
To see the fraud network I have to connect scattered evidence across claimants, providers, and attorneys by hand - and I miss the links.
- SIU work IS link analysis - connecting associations among claimants, medical providers, attorneys, witnesses.
- Evidence is scattered across claims systems, external data, and social/open sources.
- Fragmented communication across claims teams; SIUs work with limited resources.
- Generative AI now lets bad actors produce fraudulent evidence at scale, raising the bar on connecting corroboration.
Growth / outcome: Not 'more AMS automation' -> 'capture the bundles and referrals your generic pro
We know we should cross-sell, we just don't have the bandwidth to work the book.
- Producers chase new business; the existing book's cross-sell goes unworked.
- Most clients are never asked about additional coverage despite willingness.
- First-year policyholders churn most; renewals need proactive touches that don't happen.
- Referrals under-asked despite far higher retention.
Where current tooling falls short
Category limitation: core claims platforms manage the workflow of record but were not built to autonomously detect and work the leakage long tail; leakage detection has historically been sample-based audit after the money is paid, not at point of settlement.
What's leaking and what it costs
Frequently asked
Still have questions?
Book a 25-min callRegulators and reinsurers now demand provable, tested model governance and I can't produce it on demand. Not 'more documentation' -> 'prove the model on demand to the examiner and the reinsurer.'