Model the claims economics of FNOL automation and adjuster assist.
Built for claims, operations and transformation leaders at carriers and MGAs. Handling cost, straight-through processing, cycle time and leakage in one defensible model.
Your inputs
Benchmarks show typical enterprise ranges — override every field with your own numbers.
All lines in scope for automation
Benchmark: 100k–1M for a regional to national carrier
Adjuster time, intake, document handling and QA
Benchmark: $90–$260 depending on line of business
Low-complexity claims adjudicated without adjuster touch
Benchmark: 25–45% in personal lines
From document AI, summarisation and adjuster assist
Benchmark: 12–25%
Average days between FNOL and closure
Benchmark: 3–8 days
Total claim payouts in scope, used for leakage modelling
Benchmark: Typically 60–75% of earned premium
Points of indemnity avoided through consistent adjudication
Benchmark: 0.3–1.0 points
Document AI, core integration, model governance and change management
Benchmark: $600k–$1.8M
Handling-cost savings plus leakage avoided and cycle-time value.
Directional estimate. Assumes STP claims retain 15% of handling cost for audit and exceptions, and values each removed cycle day at $3.50 per claim.
Three-scenario view
Finance reviewers expect a range. These scenarios flex adoption and implementation cost around the model you entered.
Slower adoption, higher integration effort
Your inputs as entered
Strong sponsorship, clean data, phased scale-up
Want a quote built on these numbers?
Send us the brief and a delivery lead validates these assumptions against your data, then replies with indicative scope, timeline and commercial options.
CalculatorModel the claims economics of FNOL automation and adjuster assist. — routed to this team
How enterprise leaders use this model
- What drives ROI in claims automation?
- Three levers compound: intake automation (FNOL capture and document ingestion), straight-through processing on low-complexity claims, and adjuster assist that shortens handling time on the rest.
- How should we model claims leakage?
- Leakage is overpayment from inconsistent adjudication and missed subrogation. Even a 0.5-point reduction on total indemnity paid usually outweighs handling-cost savings, so it is modelled separately here.
- Does faster cycle time have measurable value?
- Yes — shorter cycle time reduces loss-adjustment expense, litigation propensity and inbound status contacts. This model attributes a conservative per-day carrying value to each day removed.
- What is realistic straight-through processing for personal lines?
- Carriers typically reach 25–45% STP on low-complexity auto and property claims within 18 months; commercial lines run lower because of adjuster judgment and coverage complexity.
What is the ROI of AI in insurance claims and FNOL?
Claims AI ROI combines straight-through processing on low-complexity claims, adjuster productivity on the rest, cycle-time reduction valued in indemnity and expense terms, and leakage avoided through more consistent adjudication — net of the platform, integration and model-governance investment the regulator expects to see.
Ungated — results appear instantly, no email required.
What you enter
- Annual claim volume by complexity band
- Loaded adjuster cost and touches per claim
- Straight-through processing rate achievable (%)
- Cycle-time reduction (days) and its indemnity impact
- Claims leakage rate and expected reduction
How it is calculated
- 1.Split claim volume into straight-through candidates and adjuster-handled claims.
- 2.Value removed adjuster touches at the loaded cost per touch.
- 3.Value cycle-time reduction against carrying and indemnity cost per day.
- 4.Apply the leakage reduction to the addressable claims spend.
- 5.Subtract platform, integration and governance investment.
What you get back
- Annual loss-adjustment expense saved
- Leakage avoided
- Cycle-time improvement in days
- Net benefit, payback and scenario range
Built for: Claims, operations and transformation leaders at P&C, life and specialty carriers.
Which claims workloads are safest to automate first?
Low-complexity, high-frequency claims with clean documentary evidence and no coverage ambiguity — plus FNOL intake and status enquiries, which carry volume without adjudication risk.
How does model governance affect a claims AI business case?
It is a permanent cost line, not a project line. NAIC model guidance and state DOI expectations mean documentation, monitoring and adverse-action review continue for the life of the model, and business cases that omit them understate run cost.