Auto claims cycle time is 14 days because adjusters spend hours manually processing police reports and repair estimates
High-frequency, low-severity auto claims should settle in 5 days. But adjusters manually download police reports from law enforcement portals, re-key repair estimates from body shops, chase policyholders for photos, and coordinate rental car authorizations. Every manual step adds days. Regure automates FNOL intake from phone/app/web/telematics, processes police reports automatically, and integrates with Mitchell/CCC/Audatex for seamless estimate management.
Auto claims are high-frequency, low-severity events requiring speed and fraud detection
Auto claims are the highest-volume claim type for most carriers. A mid-size carrier processes 10,000-50,000 auto claims annually. Speed matters: policyholders expect same-day acknowledgment and settlement within a week. Manual processing creates delays that destroy customer satisfaction.
Multi-Channel FNOL Intake Complexity
Auto claims arrive through every channel: policyholders call the 1-800 number while sitting in their damaged vehicle, file claims through mobile apps with accident photos, submit web portal claims with police report attachments, or trigger automated claims via telematics devices that detect collisions.
Each intake channel generates data in different formats. Phone calls create call center transcripts that must be manually entered into claims systems. Mobile apps upload photos that lack context (which damage is from this accident vs. pre-existing?). Web portals collect inconsistent data depending on what policyholders choose to enter. Telematics data provides impact force and location but no narrative context.
Regure unifies multi-channel FNOL intake: phone transcriptions auto-extract claim details (loss date, location, vehicles involved, injuries reported), mobile photos auto-tag with accident scene context, web portal submissions validate required fields, and telematics data cross-references with policyholder narratives to detect inconsistencies. All channels create standardized claim records in 3 minutes average.
Police Report Processing Bottleneck
Auto claims require police reports for liability determination and subrogation. But police reports arrive as PDFs from hundreds of different law enforcement agencies, each with different formats: some are typed, some handwritten, some use agency-specific codes, some span 20+ pages with witness statements and diagrams.
Adjusters manually read police reports to extract key data: at-fault party, contributing factors (speed, alcohol, distraction), witness information, and officer narrative. This manual extraction takes 15-30 minutes per report and creates errors when critical details are missed.
Regure automates police report processing: OCR extracts text from scanned/handwritten reports, NLP identifies key fields (at-fault determination, violations cited, BAC test results), and structured data auto-populates claim records. Adjusters review extracted data instead of reading 20-page reports line-by-line. Processing time drops from 25 minutes to 3 minutes per report.
Repair Estimate Management Chaos
Auto claims require repair estimates from body shops. Most carriers use Mitchell, CCC, or Audatex for estimating. But estimates still arrive as PDFs via email, require manual review for supplement approvals, and create coordination bottlenecks between adjusters and shops.
A typical auto claim involves: initial estimate from preferred shop, adjuster review and line-item approval, supplement estimate when additional damage discovered during teardown, second adjuster review, and final invoice reconciliation. Each step requires document exchange and manual data entry.
Regure integrates with Mitchell, CCC, and Audatex via APIs: estimates auto-import into claim files, line items flag for review based on pricing thresholds, supplements auto-route to the original reviewing adjuster, and final invoices compare against approved estimates to detect overcharges. Cycle time reduces from 14 days to 5 days average.
Fraud Detection on Staged Accidents
Auto insurance fraud costs carriers $8B annually according to the Coalition Against Insurance Fraud. Staged accidents (two vehicles deliberately collide to file false claims) are the most common fraud scheme. Indicators include: accidents at known fraud locations, multiple claimants from same address, consistent injury patterns across unrelated accidents, and providers with high fraud rates.
Manual fraud detection is reactive — SIU reviews flagged claims after investigation begins, wasting resources. Regure detects fraud indicators at FNOL: accident locations with 3+ prior claims, claimants with suspicious claim histories, injury patterns matching known fraud schemes (10 passengers in a 5-seat vehicle all claiming soft-tissue injuries).
Flagged claims route to SIU automatically before field investigation begins, preventing wasted adjuster time and stopping payments to fraudulent claimants. Fraud detection rate increases from 2% (industry average) to 8% with automated screening.
Auto-specific documents and workflow patterns that Regure automates
Auto claims involve unique document types and rapid-cycle workflows that require specialized automation. Regure's auto claims platform handles these specific requirements.
Police Reports
Law enforcement reports with at-fault determinations, violation citations, witness statements, and accident diagrams. Regure extracts key data using OCR and NLP: at-fault party, contributing factors, BAC results, and witness contact info. Structured data auto-populates liability assessment fields.
Repair Estimates & Supplements
Initial estimates, supplement estimates for hidden damage, and final invoices from body shops. Regure integrates with Mitchell, CCC, Audatex to auto-import estimates, flag line items exceeding pricing thresholds, and reconcile final invoices against approved amounts.
Telematics Data
Collision detection data from telematics devices: impact force, vehicle speed at collision, GPS location, timestamp. Regure cross-references telematics data with policyholder narratives to detect inconsistencies (claimed low-speed parking lot accident but telematics shows 45 mph impact).
Rental Car Authorization
Rental car companies require authorization letters with coverage dates, daily limits, and vehicle class restrictions. Regure auto-generates rental authorizations based on policy coverage, tracks rental duration against approved days, and alerts when rentals approach limits.
Subrogation Documentation
When carrier pays claim and pursues recovery from at-fault party's insurer: demand letters, liability analysis, negotiation correspondence. Regure identifies subrogation opportunities at FNOL (other party at fault), auto-generates demand letters with claim details, and tracks recovery status.
Bodily Injury Records
Medical records, bills, wage loss statements, legal correspondence for BI claims. Regure classifies medical documents by provider and treatment date, extracts billing amounts, correlates treatment to accident date (pre-existing vs. accident-related), and flags potential fraud (excessive treatment).
Six capabilities that make Regure the fastest auto claims platform
Auto claims require speed, accuracy, and fraud detection at high volume. Regure delivers all three through specialized automation built for auto claims workflows.
1. Unified FNOL Intake Across All Channels
Auto claims arrive via phone (call center), mobile app (policyholder self-service), web portal, telematics (automated collision detection), broker notification, and even social media DMs. Each channel creates fragmented data that must be unified into a single claim record.
Regure normalizes multi-channel FNOL: phone call transcriptions extract structured claim data (who/what/when/where), mobile photos auto-tag with accident context, telematics data validates against policyholder narratives, and all sources merge into a single claim record with conflict detection.
Conflict detection identifies discrepancies: policyholder says "minor parking lot accident" but telematics shows 40 mph impact and airbag deployment. These conflicts auto-flag for adjuster review, preventing fraud and ensuring accurate assessment.
2. Intelligent Police Report Processing
Police reports are critical for liability determination but arrive in 500+ different formats from law enforcement agencies nationwide. Some use standardized forms, others are narrative-heavy, some handwritten, some digital.
Regure's police report AI is trained on 400,000+ reports from agencies across all 50 states. The system extracts: at-fault determination (Party A/Party B/both/neither), violations cited (speed, failure to yield, DUI), witness contact information, officer narrative summary, and accident diagram interpretation.
For handwritten reports, OCR converts to text with 96% accuracy. For narrative reports without clear at-fault statements, NLP analyzes officer descriptions to infer fault indicators. Extracted data auto-populates liability worksheets, saving adjusters 20+ minutes per claim.
Integration with law enforcement portals: Regure auto-downloads police reports from LexisNexis, PropertyInfo, and state-specific portals when report numbers provided at FNOL. No more manual portal logins and downloads.
3. Estimating Platform Integration
Most carriers use Mitchell, CCC, or Audatex for repair estimating. But estimates still require manual review, approval, and data entry into claims systems. Regure integrates bi-directionally with all three platforms.
When a body shop creates an estimate in Mitchell, it auto-imports into Regure with line-item detail: parts pricing, labor hours, paint materials, sublet repairs. Line items exceeding thresholds (labor rate $20+ above market average, parts marked up 30%+) auto-flag for adjuster review.
Supplement estimates (additional damage found during repair) auto-route to the original reviewing adjuster with change summary: "3 new line items added: frame damage $2,400, suspension repair $850, wheel replacement $420. Total supplement: $3,670." Adjuster approves or declines supplements in-app without re-reviewing entire estimate.
Final invoice reconciliation: when body shop submits final invoice, Regure compares against approved estimate and flags variances. Approved line items pay automatically. Variances over $200 require adjuster approval. This automation reduces payment errors and prevents overpayment.
4. Staged Accident Fraud Detection
Staged accidents cost auto insurers $8B annually. Common schemes: two vehicles deliberately collide at intersections, occupants file injury claims, and staged accidents cluster at known fraud locations ("accident mills" near personal injury law firms).
Regure detects fraud patterns at FNOL intake: accident location has 5+ prior claims within 6 months (geo-fencing fraud hotspots), multiple claimants share same address but claim to be unrelated, injury patterns match known schemes (all passengers claim identical soft-tissue injuries), and providers have high fraud indicators (chiropractor with 200+ claims last year).
Flagged claims auto-route to SIU with supporting evidence before field investigation begins. This early detection prevents wasting resources on fraudulent claims and stops payments before money leaves the door. Fraud recovery increases from $2M to $7M annually for a mid-size auto carrier after implementing automated fraud screening.
5. Rental Car Workflow Automation
When policyholder vehicles are undriveable, carriers authorize rental cars. This creates workflow complexity: generate authorization letter, send to rental company, track rental duration against approved days, alert when rental approaches limit, and coordinate return when vehicle repaired.
Regure automates rental workflows: authorization letters auto-generate with policy coverage limits (30 days max, $40/day max, mid-size vehicle class), send to rental companies via API or email, track rental days against limits, and auto-alert policyholder at 25 days ("your rental coverage expires in 5 days, please coordinate vehicle pickup with body shop").
Rental overages (policyholder keeps rental beyond approved days) create disputes. Automated alerts reduce overage disputes by 68% by keeping policyholders informed throughout the rental period.
6. Subrogation Opportunity Identification
When carriers pay claims where another party is at fault, they pursue subrogation recovery from the at-fault party's insurer. But subrogation opportunities are often missed because adjusters don't identify them at FNOL or don't have bandwidth to pursue small-dollar recoveries.
Regure identifies subrogation opportunities automatically: police reports indicating other party at fault, policyholders reporting being struck by another vehicle, telematics showing impact from rear (presumed other-party fault). Identified opportunities auto-flag with recovery potential estimate.
For recoveries over $5,000, Regure auto-generates demand letters with supporting documentation (police report, repair estimate, payment proof) and sends to at-fault insurer. For recoveries under $5,000, opportunities batch-route to subrogation vendors. This automation increases recovery rates from 45% of opportunities to 78%. See customer experience improvements.
What auto claims teams ask about Regure
Does Regure integrate with Mitchell, CCC, and Audatex?
Yes. Regure integrates bi-directionally with Mitchell, CCC One, and Audatex via APIs. Repair estimates created in these platforms auto-import into Regure with full line-item detail. Claim data from Regure (VIN, loss date, vehicle info) exports to estimating platforms to reduce duplicate data entry for appraisers.
Supplement estimates auto-route to the original reviewing adjuster with change summaries. Final invoices reconcile against approved estimates automatically. Line-item variances exceeding thresholds flag for review before payment.
How does telematics data integration work?
Regure integrates with major telematics providers (Cambridge Mobile Telematics, Arity, Octo, Zendrive) to receive collision detection data: impact force, vehicle speed at collision, GPS coordinates, timestamp, and airbag deployment status. This data auto-correlates with FNOL submissions to validate policyholder narratives.
Discrepancies flag automatically: policyholder reports low-speed parking lot accident but telematics shows 45 mph impact and airbag deployment. These conflicts route to adjusters for investigation, preventing fraud and ensuring accurate liability assessment.
Can Regure detect staged accident fraud?
Yes. Regure flags fraud indicators at FNOL intake: geo-fenced fraud hotspots (locations with 5+ prior claims in 6 months), multiple claimants from same address claiming to be unrelated, injury patterns matching known fraud schemes, and providers with high fraud rates.
Flagged claims auto-route to SIU with supporting evidence before field investigation begins. This early detection prevents wasting resources on fraudulent claims. Carriers using Regure fraud detection report 4x increase in fraud identification rates compared to manual review.
How accurate is police report extraction?
Regure's police report AI is trained on 400,000+ reports from agencies across all 50 states, achieving 94% accuracy on key field extraction: at-fault determination, violations cited, witness information, and narrative summary. For handwritten reports, OCR accuracy is 96%.
Extracted data auto-populates liability worksheets for adjuster review. Adjusters verify extracted data (3 minutes) instead of reading 20-page reports line-by-line (25 minutes). Errors in extraction are rare and caught during adjuster review.
What happens to FNOL data from phone calls?
When policyholders call to report claims, call center agents enter data into Regure during the call or calls are transcribed using speech-to-text. Transcriptions auto-extract structured claim data: loss date, loss location, vehicles involved, injuries reported, and narrative summary.
Call recordings attach to claim files for quality assurance. For disputed liability cases, original call recordings provide evidence of what policyholder initially reported vs. later statements.
How long does implementation take for auto claims teams?
Standard implementation is 10-14 days for auto-only operations. This includes: integrating with Mitchell/CCC/Audatex, configuring FNOL intake workflows, setting up fraud detection rules, training adjusters and call center staff, and migrating active claims (optional). Implementation includes integration with your policy admin system for coverage verification and telematics providers for collision data. See carrier implementation process.
See how Regure handles high-volume auto claims
Book a 20-minute demo with your actual auto claim workflows. We'll show you FNOL automation, police report processing, estimating platform integration, and fraud detection — with your data.