Risk, fraud, and claims
Claims Triage & Fraud Referral
Instant pay where it is safe, expert attention where it matters, and a special-investigations referral where the evidence points, measured in cycle time, leakage, and referral hit rate.
The problem
Uniform manual queues make simple claims wait behind complex ones. Investigators receive weak referrals or miss organized rings, and automated denial is a regulatory and reputational risk this solution never takes.
Who owns it
Claims operations, with the chief operating officer. Claims management owns the straight-through payment policy, the special investigations unit co-designs referral reasons, and actuarial monitors leakage.
What it learns from
- Claims history
- Policy and coverage
- First-notice documents and photos
- Special-investigations dispositions
- Payment and repair events
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Predicts severity and fraud risk
Models score severity and fraud propensity from claim, policy, and history features. Documents and photos become structured fields with accuracy checks on each field, and link analysis connects claimants, providers, and repair shops.
- 02
Decides the path through a policy
Each claim is assigned instant settlement, fast track, standard, or complex handling. Straight-through payment runs only inside conservative allowlists by line and amount, referral thresholds match investigator capacity, injury and liability lines always get a human adjuster, and catastrophe modes are configuration.
- 03
Acts in the claims platform
Paths, pre-populated files, referrals with reasons, reserve suggestions, and vendor dispatch land in the claims platform and in special-investigations case management. Anything that points toward denial always lands with a person.
- 04
Measures with leakage audits and a phased rollout
Random samples of automatically paid claims are re-adjudicated to measure leakage, and random referrals keep the fraud model honest. Cycle time, referral hit rate, and satisfaction are compared by path and line of business in a phased rollout.
What it moves
The business levers
Cycle time and handling cost
Simple claims paid quickly, with fewer adjuster touches on the fast track.
Leakage
Overpayment found by standing audits rather than discovered late.
Investigator capacity
Referrals ranked so investigators spend time where the evidence points.
Claim experience
The claim is the moment a policyholder remembers, and the fast path shortens it.
Proof
How the return is proven
The return is loss-adjustment expense saved plus leakage avoided plus referral value, minus overpayment and the burden of false referrals, measured by the leakage audits and hit-rate readouts and stated against the return model agreed up front.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Claim, referral, and investigation views are scoped by role through your single sign-on.
Approval gates
Allowlist expansions need claims management and compliance sign-off, with the leakage evidence attached.
Audit trail
Every path decision is logged, and every extracted field is traceable to its source.
NAIC and unfair-claims rules
Governance follows the NAIC model bulletin on insurers' use of AI, with an inventory, testing evidence, and adverse-impact review. The solution never automates denial, every customer keeps an appeal path, and health information is handled under the privacy rules that apply to it.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.