The Claims VP playbook for deploying AI investigation: budget, timeline, change management

A Claims VP at a Top 50 P&C carrier sits with three numbers on the desk every quarter: the SIU caseload, the cost per investigated claim, and the loss ratio on lines where fraud is concentrated. AI claims investigation moves all three. The harder problem is scoping the buy in a way the CFO will sign, the CIO will integrate, and the SIU director will actually run.

This playbook is for the Claims VP who has decided AI investigation is in the budget cycle and now has to defend the line item. It walks the pre-RFP diagnostic, the pilot and 12-month budget ranges, the phased timeline, the stakeholder map, the change management commitments, and the four metrics that belong on the board report at month 12.

The buy sits inside the wider claims-operations stack covered in our autonomous AI claims investigation pillar, which is the upstream context for the procurement decision below.

Three signals tell a Claims VP the AI investigation buy belongs in the claims-org budget, not the IT capital plan. SIU capacity is the binding constraint on loss-ratio improvement. Cost per investigated case is the lever the CFO will model. And the antifraud-plan filing under NAIC Model Act #680, adopted in 48 states, is a claims-org responsibility, not an IT one.

The current state at most Top 50 carriers: a manual SIU investigation takes 14+ days per case, an investigator carries 200+ cases, and only about 25% of flagged claims actually get investigated. The other 75% close on adjuster judgment without an investigation file. Meanwhile, total US insurance fraud loss runs at $308 billion annually per the Coalition Against Insurance Fraud, with roughly 10% of P&C claims involving some form of fraud. SIU staffing growth, per the Insurance Information Institute, slowed to 1.4% in 2021-2022 from 2.5% the prior year. The capacity gap is widening, not closing.

A Claims VP also needs to be clear on what AI investigation is not. It is not a detection-vendor swap. FRISS, Shift Technology, and Verisk ISO ClaimSearch keep doing what they do at FNOL. The investigation layer is downstream, covered in detail in our piece on prevention, detection, and investigation as three distinct layers. The new spend sits on the investigation tier, where SIU headcount has historically been the only available lever.

A defensible budget ask has three parts: a 90-day pilot, a 12-month rollout run-rate, and the coverage-corrected loss-ratio math the CFO will actually scrutinize. Scope the pilot at 1,000 to 5,000 cases on one or two lines and have it quoted against that volume, plan the rollout run-rate to scale with flagged-claim count rather than seats or headcount, and carry the ROI in basis points of loss ratio on AI-investigated lines.

Build the CFO model on coverage-corrected unit cost, not headline per-case price. Manual SIU carries a high fully loaded cost per case and reaches about 25% of flagged claims. AI investigation reaches 100% at a unit cost low enough that the marginal cost of one more investigation stops driving the triage decision. On a carrier flagging 50,000 claims per year, the manual budget covers about 12,500 cases, and covering all 50,000 lands well under that same budget. The fraud caught in the previously uninvestigated 37,500-case tier is the loss-ratio story, not the per-case savings.

Two budget pitfalls show up in CFO reviews. First, services markup on integration: the quoted per-case rate is almost never the loaded cost, because implementation services, evidence-pack storage, and adjuster training all land in the SOW. Ask every vendor to price the fully loaded case, not the software line. Second, parallel-run costs: most carriers underfund the 60 to 90 days when SIU runs the old workflow next to the AI-assisted one for audit confidence. The full set of line items the CFO will ask about is in our breakdown of hidden integration costs for legacy claims AI. For full ROI scenarios across three carrier sizes, see our ROI case studies for AI claims investigation.

A defensible rollout has three phases and the Claims VP should commit to phase-gate decisions, not a 12-month lock. Phase 1 is a 90-day pilot on one or two lines, typically auto and workers compensation because referral volume is highest and red flags are well-defined. Phase 2 scales to homeowners, commercial property, and bodily injury once the pilot signal on cycle time and false-positive resolution is clean. Phase 3 retires manual triage on the covered lines and integrates cost-per-case into the monthly claims operating review.

The pilot exit criteria the Claims VP should write into the SOW before signature: investigation cycle time measured in minutes on 95% of pilot cases, false-positive resolution rate above 80% on rules-flagged claims (since rules-based detection runs 60-85% false positives in industry studies), and zero unresolved compliance flags from the SIU director on audit-trail completeness. If any of the three miss, the scale phase does not auto-fund.

A Claims VP who runs this procurement without a written stakeholder map will lose 60 to 90 days of cycle time to ad-hoc objection handling, mostly from CIO security review and legal review of third-party data use. That gate has its own map: the third-party risk review that sets an AI deployment date walks the sixteen artifacts and eight contract clauses reviewers ask for. Pre-empt every reviewer with the one question they actually care about. The 12-point evaluation framework in our AI fraud investigation vendor checklist is the working document for these conversations. For the legal reviewer specifically, the general counsel's guide to AI fraud investigation covers the discovery, privilege, and admissibility questions before they ask them.

Compliance is the reviewer most likely to be skipped and most likely to slow the deal in week 10. Bring them in at week one. The relevant state rule for California carriers is California 10 CCR 2698.36, which requires the SIU to investigate each credible referral of suspected fraud and to document any decision not to investigate. AI investigation makes that easier, not harder: every flagged claim gets a documented decision in the audit log, which is the exact artifact the state DOI asks for in market conduct exams.

The change management failure mode at carrier rollouts is anxiety, not pushback. SIU adjusters who handled roughly 10 investigations per month manually do not know what 800+ in the queue means for their job. The Claims VP needs three written commitments before the first case routes, all communicated to SIU staff in the same memo. The director's side of that rollout is mapped week by week in the SIU director's first 90 days with AI investigation. The manager-level version of that transition - who tells the team what, and when - is in leading a claims team through AI-augmented investigation.

1. No SIU headcount reduction in year one. The freed capacity covers the 75% of flagged claims that were previously uninvestigated, which is where the incremental fraud recovery sits.
2. The investigator role is redefined as decision-maker over AI-produced evidence packs. The work that requires judgment stays human; the work that requires data gathering, document collection, and database lookups moves to the AI layer running 15+ phases in parallel.
3. A named escalation path for any AI output the investigator disputes, with the SIU director as the final authority. Every disputed output gets logged and reviewed monthly for model-quality signal.

Named-insurer evidence helps with the SIU town hall. AXA Switzerland's deployment with Shift Technology caught a CHF 120,000 false claim involving intoxicated workers in the first week after deployment, per Samuel Klaus, Head of Fraud at AXA Switzerland, in the published Shift Technology case study. The point for SIU is that AI in production fraud workflows is now standard practice at named European carriers, not a beta experiment.

Four metrics belong on the month-12 board report, and only four. Investigation coverage as a percent of flagged claims. Cost per investigated case. Cycle time per case. Loss-ratio delta on AI-investigated lines, isolated from rate action and other claims-handling changes.

The board narrative the Claims VP brings is not a per-case cost story. It is a coverage story: the carrier is now investigating four times as many flagged claims at lower total cost, with documented decisions on every claim that satisfies state DOI referral requirements and the NAIC #680 antifraud-plan filing. The loss-ratio delta lands in months 6 to 12 as the previously-uninvestigated tier gets worked. Pricing-side actuaries can isolate the signal by line and by quarter.

The forward question for the Board is no longer whether to deploy AI investigation but whether the carrier's antifraud plan, SIU role definitions, and reinsurance reporting reflect a 100%-coverage operating model. Carriers that lock that operating model in 2026 will price 2027 books on a different loss-cost curve than carriers still triaging at 25% coverage. That is the procurement decision the Claims VP is actually making this cycle.