Multi-Modal Document & Visual Intelligence for End-to-End Insurance Claims Processing

Protocol Metadata	Specification
Protocol Identifier	FALCONIC-PROT-CLM-18 // REV_3.7
Domain & Sector	Property & Casualty (P&C) & Health Insurance, Multi-Modal Claims Adjudication & Fraud Detection
Core Technology Stack	Python, Vision-Language Transformer, EXIF/PRNU Image Forensics, Whisper-Large-v3 Audio Diarization, DuckDB Policy Engine, Guidewire ClaimCenter API
Primary Benchmark Metric	96.9% Multi-Modal Adjudication Precision; 73.8% Straight-Through Auto-Adjudication Rate
Execution Latency	14.2s median end-to-end processing per multi-modal claim packet (PDFs + 12 Photos + Call Audio)
Unit Economics (Cost-Per-Run)	$0.11 per complete multi-modal claim evaluation
Verified Business Impact	Claims cycle time compressed from 14.2 days to 2.5 days (-82.4%); $3.8M fraud/duplicate payout prevention
Reference Anchor	FalconicLab Document Intelligence // FalconicLab Case Studies

1. Problem Statement: Disconnected Modalities and AI-Generated Image Fraud in Insurance Claims

Modern P&C auto, property, and medical insurance claims arrive as messy multi-modal bundles: a 6-page ACORD loss notice PDF, 15 smartphone photos of vehicle or roof damage, a body-shop repair estimate PDF (CCC ONE / Mitchell), a police report scan, and a 12-minute recorded FNOL (First Notice of Loss) phone call. In our baseline audit of a national carrier processing 95,000 claims monthly, human adjusters spent 4.2 hours per claim cross-checking whether the damage described on the phone call matched the police report diagram, the photos, and the repair line items—while missing 18.4% of recycled, doctored, or generative-AI-inpainted damage photos.

FalconicLab architected the Multi-Modal Claims Intelligence Engine (clm-18 v3.7) to fuse audio, document IDP, computer vision damage segmentation, and cryptographic image forensics into a single unified adjudication graph.

2. High-Level Technical Architecture: Cross-Modal Consistency Graph & Image Forensics Gate

• Node 01 — Cryptographic Image Forensics & Perceptual Deduplication (Forensic-Vision-Node, 1.8s): Inspects every submitted damage photo before vision analysis:
  • Computes pHash / DINOv2 perceptual embeddings against a 40M-image historical claims index to catch claimants re-submitting prior accident photos or stock web images (even if cropped, flipped, or color-shifted).
  • Analyzes PRNU (Photo Response Non-Uniformity) sensor noise residuals, ELA (Error Level Analysis), and C2PA metadata to detect Photoshop cloning or Stable Diffusion / Flux generative inpainting of artificial dents and water damage (99.2% synthetic tampering detection).
• Node 02 — Part-Level Damage Segmentation & Repair Estimate Matcher (Damage-Vision-Node, 4.6s): Maps segmented vehicle/property damage polygons (front_right_fender: severe_crush, headlamp_assembly: cracked) against the structured line items extracted from the body-shop PDF estimate (exp-03 IDP core). Automatically flags line-item padding (e.g., body shop billing for a rear bumper replacement when both photos and police report confirm a front-right collision only).
• Node 03 — Audio FNOL & Police Report Cross-Modal Consistency Judge (Cross-Modal-Judge, 5.4s): Transcribes the claimant’s recorded FNOL statement with speaker diarization and checks Cross-Modal Spatio-Temporal Invariants across all four modalities (Audio Statement vs. Police Report PDF vs. Photo EXIF GPS/Timestamp vs. Weather API historical radar at the loss coordinates).
• Node 04 — Policy Coverage & Deductible Adjudication Compiler (Guidewire-Compiler-Node, 2.4s): Cross-references the verified loss cause against the policyholder’s exact policy declarations, endorsements, and exclusions, routing clean claims (< $5,000 with 100% cross-modal consistency) for instant payout and escalating anomalous claims to SIU (Special Investigations Unit).

Cross-Modal Verification Check	Legacy Manual Adjuster	Document-Only AI Wrapper	FalconicLab Multi-Modal Engine (clm-18 v3.7)	Operational Impact
Straight-Through Auto-Adjudication (Low/Mid Complexity)	12.0% (rule-based glass only)	31.0%	73.8%	-82.4% claims resolution cycle time
Recycled / Generative-AI Doctored Photo Detection	14.0% (visual guesswork)	0.0% (blind to images)	99.2% (PRNU + DINOv2 Forensics)	$3.8M/yr fraudulent payouts blocked
Estimate Line-Item vs. Photo Geometry Reconciliation	79.5%	61.0%	97.4%	Eliminates unrelated repair line-item padding
Processing Cost Per Claim Packet	$165.00 (adjuster labor)	$0.68	$0.11	-99.9% marginal triage cost

3. Failure Logs & Production Lessons Learned

• Failure Log FL-18A — WhatsApp / SMS Image Re-Compression Triggering False Manipulation Alerts (v2.1): When policyholders texted damage photos to their agent via WhatsApp or MMS, aggressive JPEG re-compression stripped EXIF headers and introduced 8x8 DCT block artifacts that early ELA forensic models misclassified as Photoshop tampering (9.4% false-positive SIU flag rate). Remediation: Trained the forensic classifier on a Messaging-Channel Augmentation Pipeline (simulating WhatsApp, iMessage, and SMS compression quantization tables) and required dual confirmation from Diffusion Noise Frequency Spectrum Analysis + cross-modal narrative inconsistency before raising an SIU fraud flag, cutting false positives to 0.3%.

Mathematical & Forensic Architecture of Multi-Modal Claims Adjudication (Forensic-Vision-Node & Cross-Modal-Judge)

Why do single-modality insurance AI systems fail to stop claims leakage while frustrating honest policyholders with 14-day settlement delays? Because insurance claims are inherently multi-modal constraint satisfaction problems: a claim is valid and payable without human intervention if and only if the Audio Statement, the Police/Incident Report PDF, the Submitted Damage Photographs, the Repair Estimate Line Items, the Historical Weather/Telematics Data, and the Policy Coverage Contract form a mutually consistent spatio-temporal graph.

In clm-18 v3.7, FalconicLab engineered two core technical innovations that enabled 73.8% straight-through auto-adjudication while catching $3.8M/year in image and line-item fraud:

1. Two-Stage Generative-AI & Recycled Image Forensics (Forensic-Vision-Node, 1.8s):
   • Stage 1A — Transformation-Invariant Perceptual Graph (DINOv2 + pHash over 40M Historical Photos): Fraudsters frequently rotate, crop, mirror-flip, or adjust brightness on damage photos from a 2023 claim (or scraped from Salvage auction websites like Copart/IAAI) and submit them under a new policy. We index 40M historical and public salvage photos using 768-D DINOv2 self-supervised vision embeddings invariant to ±30° rotation, horizontal flipping, and aspect cropping—flagging recycled photos across different policies in 14ms with 99.7% precision.
   • Stage 1B — Diffusion-Inpainting Frequency & PRNU Sensor Residual Analysis: With commercial generative fill tools (Photoshop Generative Fill, Flux, Stable Diffusion Inpainting), fraudsters take a photo of an undamaged car door or house roof and inpaint realistic hail dents, cracked windshields, or water stains. Forensic-Vision-Node computes the High-Pass Wavelet Noise Residual and 2D Azimuthal Power Spectral Density (PSD) across sliding 128 x 128 image patches. Even after WhatsApp/SMS JPEG re-compression, diffusion-inpainted regions exhibit a localized interruption in the camera CMOS sensor’s physical Photo Response Non-Uniformity (PRNU) pattern and characteristic upsampling checkerboard spectra—detecting localized synthetic tampering at 99.2% precision (0.3% FPR).
2. 3D/2D Part-Polygon to Repair-Line-Item Bipartite Reconciliation (Damage-Vision-Node):
   • A fine-tuned instance segmentation model (Mask2Former-Auto-Property) maps every damaged vehicle panel or roof slope into canonical industry part codes (CCC ONE / Mitchell / Xactimate taxonomy) and severity grades (Paintless_Dent_Repair, Sheet_Metal_Straighten_2hr, Full_OEM_Panel_Replace).
   • The node then constructs a bipartite graph between Visually Observed Damaged Parts and Billed Estimate PDF Line Items (exp-03), flagging any billed replacement part or labor operation lacking visual polygon evidence or adjacent structural crush propagation paths.

Granular Cost-Per-Run & Claims Triage Economics ($0.11 Per Complete Multi-Modal Claim Packet)

• Forensic-Vision-Node (DINOv2 40M Search + PRNU/Frequency Tampering Analysis across 12 avg photos): 1.8s = $0.018
• Damage-Vision-Node (Part Segmentation + Estimate Line-Item Bipartite Reconciliation): 4.6s = $0.036
• Cross-Modal-Judge (Whisper-Large-v3 Audio Diarization + Police PDF + Weather Radar Consistency): 5.4s = $0.042
• Guidewire-Compiler-Node (Policy Endorsement/Deductible Check & ClaimCenter Payload): 2.4s = $0.014

4. Extended Failure Logs: Multi-Modal Claims Edge Cases & Breakdowns (clm-18 v3.7)

Failure Log FL-18B — Hidden Internal Structural Damage Flagged as Body-Shop Estimate Padding (v2.6)

• Incident Description: When a vehicle suffered a moderate rear-bumper collision, initial exterior smartphone photos submitted by the driver showed only a cracked plastic rear bumper cover. When the body shop tore down the bumper and submitted a supplement billing for a crushed inner aluminum impact bar and rear body panel (+$1,450), v2.6 flagged the inner impact bar as "Unverified Line-Item Padding" because it was invisible in the initial exterior photos.
• Root Cause Analysis: Exterior consumer photos only capture outer skin panels; mechanical energy absorption laws dictate that outer bumper deformation above a threshold depth propagates into hidden inner reinforcement beams.
• Architectural Remediation: Integrated a Conditional Hidden-Damage Propagation Graph (trained on 1.8M historical teardown supplements) into Damage-Vision-Node AND added a Photo_Stage classifier (INITIAL_EXTERIOR_FNOL vs. SHOP_TEARDOWN_SUPPLEMENT). When impact severity on an outer skin panel exceeds Crush_Depth_Class >= 2, the graph automatically authorizes conditionally linked inner reinforcement/absorber part codes provided the shop attaches a teardown photo showing the exposed inner structure. False padding flags on legitimate hidden damage dropped from 11.2% to 0.8%.

Failure Log FL-18C — Apple HEIC Live-Photo & HDR Tone-Mapping Triggering PRNU Sensor Mismatch (v3.1)

• Incident Description: iPhones shooting in Smart HDR 5 / HEIC fuse multiple exposure brackets computationally. Early PRNU camera-fingerprint models treated multi-frame computational HDR fusion in high-contrast shadow/highlight boundaries (e.g., bright sunlight reflecting off a dented chrome bumper) as a spliced foreign image region.
• Root Cause Analysis: Classical single-exposure PRNU formulas do not account for Apple/Samsung multi-frame computational photography tone-mapping boundaries.
• Architectural Remediation: Calibrated Forensic-Vision-Node with a Device-Model Computational HDR Profile Library keyed by Apple/Samsung MakerNote tags and replaced raw pixel PRNU thresholding with a Cross-Patch Diffusion-Latent Artifact Transformer trained specifically to separate computational HDR exposure blending from generative AI inpainting and clone-stamp splicing.

Failure Log FL-18D — Pre-Existing Prior-Loss Damage Re-Billed Under New Storm/Collision Event (v3.4)

• Incident Description: Policyholders occasionally filed a hail or parking-lot collision claim where 3 of the 7 dented panels had already been paid out on a prior cash-settled claim 14 months earlier (where the owner pocketed the check without repairing the car).
• Root Cause Analysis: Inspecting only the active claim’s photos without comparing against the vehicle’s VIN-Linked Prior-Claim Polygon History allows double-dipping on unrepaired prior damage.
• Architectural Remediation: Added a VIN-Scoped 3D Canonical Vehicle Mesh Diff in Damage-Vision-Node that projects prior-claim damage polygons onto a canonical 3D vehicle UV map; any dent cluster matching a previously paid unrepaired polygon (spatial IoU > 0.65) is automatically deducted as PRE_EXISTING_UNREPAIRED_DAMAGE unless a verified paid repair receipt was logged in the interim.

Failure Log FL-18E — Regional Labor-Rate & OEM vs. Aftermarket Part Endorsement Discrepancies (v3.6)

• Incident Description: Body shops in high-cost metro ZIP codes billed $115/hr structural frame labor and OEM replacement parts on policies carrying an Aftermarket / LKQ (Like Kind and Quality) Parts Endorsement.
• Root Cause Analysis: Line-item reconciliation checked part geometry but did not cross-reference regional prevailing labor-rate tables or policy-specific OEM part riders.
• Architectural Remediation: Connected Guidewire-Compiler-Node to live ZIP-Code Prevailing Labor-Rate Matrices and policy endorsement codes (OEM_RIDER_ACTIVE == true/false), automatically re-pricing non-compliant aftermarket/OEM part substitutions with line-by-line vendor part availability links.

5. Final Results & Production Engineering Lessons Learned

• Claims Resolution Compressed from 14.2 Days to 2.5 Days (-82.4%): Clean low-to-mid complexity claims (73.8% of volume) are verified across audio, PDFs, photos, and policy rules in 14.2 seconds, issuing instant payment links to policyholders.
• $3.8M Annual Fraud & Leakage Prevented: Caught 1,420 recycled/AI-doctored image submissions and $2.1M in unrelated repair estimate line-item padding in the first 12 months.

6. Protocol Execution Specification: Multi-Modal Adjudication Graph, Forensic Schema & Claims Telemetry (clm-18 v3.7)

6.1 Multi-Modal Claims Adjudication State Machine (FALCONIC-CLM18-GRAPH)

State ID	Adjudication Stage	Active Engine	Median Latency	Cross-Modal Invariant	Escalation / SIU Action
C01_IMG_FORENSIC	40M DINOv2 Dedup + Diffusion/PRNU Scan	Forensic-Vision-Node	1.8s	Zero recycled pHash/DINOv2 match; tampering <0.05	Route to SIU_FRAUD_QUEUE on match
C02_PART_SEGMENT	3D/2D Part Damage & Severity Polygon Map	Damage-Vision-Node	2.8s	Map polygons to CCC/Mitchell/Xactimate part IDs	Request guided re-photo if blur/glare
C03_EST_RECONCILE	Estimate PDF (exp-03) vs. Polygon Match	Damage-Vision-Node	1.8s	Every billed line item linked to visible or propagation part	Flag unlinked lines for adjuster review
C04_AUDIO_MODAL	Whisper Diarization + Police + Weather Check	Cross-Modal-Judge	5.4s	Audio loss narrative == Police diagram == NOAA radar/GPS	Escalate on spatio-temporal conflict
C05_POLICY_GATE	Coverage, Exclusion & Deductible Solver	Guidewire-Compiler-Node	2.4s	Policy active at loss timestamp; net payout <= $5,000	Auto-issue instant payout via ClaimCenter

6.2 Structured Multi-Modal Claims Adjudication & Forensic Payload (clm-18 v3.7)

{
  "protocol_version": "FALCONIC-CLM-18-MULTIMODAL-V3.7",
  "claim_number": "CLM-2026-AUTO-884920",
  "total_processing_latency_seconds": 13.9,
  "image_forensics_audit": {
    "photos_evaluated": 11,
    "historical_40m_dinov2_max_similarity": 0.142,
    "recycled_photo_detected": false,
    "diffusion_inpainting_max_patch_score": 0.012,
    "exif_gps_weather_corroboration": "MATCH_NOAA_HAIL_SWATH_DALLAS_TX_2026_05_14_1730_UTC"
  },
  "part_to_estimate_reconciliation": {
    "repair_estimate_total_usd": 3480.00,
    "verified_visual_and_propagation_usd": 3120.00,
    "flagged_uncorroborated_line_items": [
      {
        "line_number": 18,
        "part_code": "OEM_REAR_SPOILER_ASSY",
        "billed_amount_usd": 360.00,
        "flag_reason": "Zero hail or impact polygons detected on rear decklid/spoiler across Photos #4, #5, #6"
      }
    ]
  },
  "cross_modal_consistency_score": 0.988,
  "guidewire_adjudication_decision": "AUTO_APPROVE_VERIFIED_3120_USD_MINUS_500_DEDUCTIBLE"
}

6.3 Production Telemetry Breakdown Across 95,000 Monthly Insurance Claims

Insurance Claim Line & Complexity	Monthly Volume (n=95,000)	Auto-Adjudication STP %	Adjudication Precision	Fraud / Padding Catch Rate	Cycle Time (Post vs Pre)	Mean Cost / Claim
Auto Collision — Low/Moderate (<$5,000, No Injury)	41,800 (44.0%)	86.4%	98.1%	99.1%	1.1 days (vs 12.4d)	$0.10
Comprehensive Hail, Wind & Glass Claims	22,800 (24.0%)	89.2%	98.8%	99.4% (NOAA radar bound)	0.6 days (vs 9.8d)	$0.09
Homeowners / Property Water & Roof Damage	16,150 (17.0%)	54.0%	95.4%	98.6%	3.8 days (vs 18.5d)	$0.14
Auto Multi-Vehicle / Complex Structural (>$5,000)	10,450 (11.0%)	31.2% (Pre-briefed HITL)	94.8%	98.9%	5.2 days (vs 21.0d)	$0.15
Bodily Injury / Litigation / SIU Escalations	3,800 (4.0%)	0.0% (100% SIU/Adjuster)	96.2% (Triage)	99.5%	7.4 days (vs 28.0d)	$0.12
Total / Weighted Carrier Claims Aggregate	95,000 (100%)	73.8% Touchless STP	96.9%	99.2% Image Forensics	2.5 days (-82.4%)	$0.11

6.4 Subrogation Recovery & Telematics / EDR Crash-Pulse Corroboration

Beyond first-party payout adjudication, clm-18 automates Third-Party Subrogation Recovery and Vehicle Telematics Corroboration:

• Automated Fault & Subrogation Demand Generation: When Cross-Modal-Judge parses the police crash diagram (Boxes 19–21 Contributing Factors), traffic-camera metadata, and driver audio statements, it computes comparative negligence percentages under the loss state’s statutory regime (Pure Comparative vs. Modified 50%/51% Bar). Whenever an adverse carrier is liable, Guidewire-Compiler-Node automatically compiles an Arbitration-Ready Subrogation Demand Package (complete with annotated damage polygons, OEM repair invoice proofs, and police report citations) and transmits it via AF (Arbitration Forums) E-Subro Hub—accelerating subrogation recovery by 34 days and increasing recovered funds by +$4.1M/year.
• OEM Telematics & Bosch CDR Crash-Pulse Physics Check: For connected vehicles submitting accelerometer $\Delta v$ crash pulses, Cross-Modal-Judge verifies that the physical momentum transfer ($\Delta v$ in mph and principal direction of force PDOF in degrees) is physically consistent with the segmented crush depth and any claimed occupant whiplash injury.

6.5 Pre-Deployment Engineering Checklist for Multi-Modal Insurance Claims AI

• Screen Every Photo Against a Transformation-Invariant DINOv2 Index & Diffusion Noise Detector: Catch recycled salvage-auction photos and generative-AI-inpainted dents before running damage estimation.
• Train Forensic Models with Messaging-App (WhatsApp/MMS) Compression Augmentation: Ensure JPEG re-compression artifacts and iPhone computational HDR blending are never misclassified as image tampering.
• Model Conditional Hidden-Damage Propagation: Use historical teardown graphs so legitimate inner structural reinforcement supplements are distinguished from unrelated outer-panel billing padding.
• Cross-Check Audio FNOL, Police Diagrams, EXIF GPS, and Historical Weather Radar: Enforce four-way spatio-temporal consistency before authorizing touchless payouts.

7. Production Infrastructure, Hardware Topology & Chaos Engineering Benchmarks (clm-18 v3.7)

7.1 High-Availability Cloud, Edge & Enclave Deployment Topology

To adjudicate 95,000+ multi-modal Property & Casualty (P&C) and health insurance claim packets per month (1.14 million claims/year, each containing 10–25 high-resolution smartphone photos, body-shop/contractor PDF estimates, police crash reports, and 10-minute recorded FNOL audio calls) in 14.2 seconds at $0.11 per claim, FalconicLab deployed a four-plane multi-modal architecture integrated directly with Guidewire ClaimCenter:

• 40-Million-Image Perceptual & Forensic Screening Plane (Forensic-Vision-Node):
  • Qdrant DINOv2 Perceptual Deduplication Cluster: Holds 40M+ historical claim photos, industry salvage-auction images (Copart / IAAI), and public web stock crash images indexed as 768-D transformation-invariant DINOv2 embeddings. Every newly uploaded claim photo is checked in 14ms across rotations (±180°), crops, aspect shifts, and color filters to block recycled prior-loss photos.
  • GPU Wavelet-PRNU & Diffusion-Inpainting Forensic Workers: Runs on 4x NVIDIA L40S GPUs. Evaluates high-pass wavelet sensor noise residuals (PRNU), 2D azimuthal power spectral density (PSD), and C2PA provenance signatures—calibrated with our WhatsApp/MMS/iMessage Re-Compression & Apple Smart-HDR 5 Profile Library—to isolate generative-AI inpainting (Flux, Stable Diffusion, Photoshop Generative Fill) at 99.2% precision (0.3% FPR).
• 3D/2D Part Segmentation & Estimate Bipartite Graph Solver (Damage-Vision-Node):
  • Runs Mask2Former-Auto-Property to segment damaged panels/roof slopes and project them onto a VIN-Scoped 3D Canonical Vehicle/Property UV Mesh (comparing against any unrepaired prior-claim polygons for that exact VIN/address).
  • Matches visual damage polygons and conditional inner-structure crush propagation rules against the line items extracted from CCC ONE / Mitchell / Xactimate repair estimate PDFs (exp-03).
• Streaming Audio Diarization & Spatio-Temporal Judge (Cross-Modal-Judge): Runs TensorRT Whisper-Large-v3 with pyannote speaker diarization (1.9s per 10-minute call), cross-checking the claimant’s spoken collision/storm narrative against police report diagrams (Boxes 19–21), EXIF GPS coordinates, and historical NOAA NEXRAD Level-II Doppler Radar hail/wind swaths.

7.2 Chaos Engineering & Adversarial Fault-Injection Drills

Before certifying clm-18 v3.7 as STATUS: NOMINAL in the FalconicLab Live Experiments & Systems Registry, our reliability and security engineering teams subjected the production deployment to three destructive fault-injection and adversarial chaos drills:

1. Chaos Drill CD-18A — Generative-AI “Fake Hail & Bumper Crush” Inpainting Red-Team Drill: SIU (Special Investigations Unit) investigators took 400 clean photos of undamaged vehicles and residential roofs and used 4 state-of-the-art commercial diffusion editors (Adobe Firefly / Photoshop Generative Fill, Flux.1-Fill-dev, SDXL-Inpaint, and Midjourney Retexture) to paint realistic hail dents, cracked windshields, and bumper scrapes—then passed half of the images through WhatsApp re-compression. Forensic-Vision-Node detected 397 out of 400 (99.25%) synthetic manipulations via cross-patch diffusion frequency artifacts and PRNU disruption while triggering only 1 false positive (0.25%) on 400 real WhatsApp-compressed claimant photos.
2. Chaos Drill CD-18B — Salvage-Auction Photo Cropping & Horizontal Flip Evasion Test: Took 500 historical total-loss photos from 2023–2025 claims, cropped 25% off the edges, flipped them horizontally, applied a warm Instagram-style color LUT, stripped all EXIF tags, and submitted them under new policy numbers. Standard MD5/SHA-256 and basic perceptual hashes (aHash) caught only 18%, whereas our DINOv2 Transformation-Invariant Perceptual Index caught 499 out of 500 (99.8%) in 14ms.
3. Chaos Drill CD-18C — Catastrophic Hailstorm 15x Surge (18,000 FNOL Claims in 24 Hours): Simulated a severe Texas/Colorado hail catastrophe event dumping 18,000 claims and 216,000 photos in 24 hours. The Kubernetes GPU pool auto-scaled to 32 L4/L40S nodes, auto-adjudicating 89.2% of verified NOAA-swath hail & glass claims in 11.8 seconds average and issuing same-day policyholder payouts without hiring temporary catastrophe adjusters.

7.3 Executive Total Cost of Ownership (TCO) & 12-Month Financial ROI Ledger

Across 1.14 million annual claims (95,000/month), clm-18 v3.7 generated over $21.4M in combined operational, fraud-prevention, and subrogation value:

• Adjuster Labor & Cycle-Time Savings:
  • Touchless auto-adjudication rose from 12.0% (simple glass-only rules) to 73.8% across low-to-moderate auto and property claims, compressing average claim settlement cycle time from 14.2 days to 2.5 days (-82.4%) and saving $13.5M/year in manual desk-adjuster triage and external independent adjuster (IA) fees.
• Direct Fraud & Estimate Leakage Blocked:
  • $3.8M/year saved by blocking 1,420 recycled/AI-inpainted photo claims and stripping uncorroborated body-shop repair estimate line-item padding.
• Accelerated Subrogation Recovery:
  • +$4.1M/year in incremental third-party subrogation recoveries via automated police-diagram fault calculation and instant Arbitration Forums (E-Subro Hub) demand packet generation.
• Total Platform Compute & Inference Cost: $0.11 per claim ($125,400/year across 1.14M claims)—yielding a 170x net return on AI compute spend.