What is VIN fraud and how does Inspektlabs help tackle it?

Accurate vehicle identification is the foundation of every insurance claim and repair decision. When the identity of a vehicle is unclear or manipulated, even the most well-designed inspection or claims process starts to fail. 

As automated vehicle inspections and digital claims workflows become standard across insurance and repair networks, verifying that the right vehicle is being inspected has become just as important as detecting damage itself. 

One of the most common ways this breakdown occurs is through VIN manipulation. While VIN-related fraud isn’t new, it has become significantly easier to execute in remote and photo-based inspection environments. This blog explains how VIN manipulation occurs, why it poses serious risks for insurers and repair companies, and how remote vehicle inspection systems help detect and prevent these issues at scale. 

Understanding VIN manipulation in vehicle inspections

A Vehicle Identification Number (VIN) is a unique, 17-character code assigned to every vehicle. It encodes critical information such as the manufacturer, brand, model, body type, engine configuration, and model year. 

In traditional, in-person inspections, VIN verification was largely manual. However, in automated vehicle inspection and photo-based vehicle inspection workflows, VIN validation must be handled digitally, and at scale. 

VIN manipulation occurs when the VIN captured during an inspection does not correctly represent the vehicle being inspected. This creates gaps in vehicle identity verification and opens the door to incorrect damage assessments, fraudulent claims, and mismatched repair approvals. 

Common ways VIN manipulation occurs

#1 - Vehicle swapping during remote inspections

In this scenario, damage is recorded on one vehicle, while the VIN belongs to another vehicle. 

This typically happens when: 

• A damaged vehicle is inspected
• The VIN is captured from a different vehicle
• The inspection appears complete, but it represents two different vehicles. 

In remote vehicle inspection workflows, this can be difficult to detect without motion analysis and inspection flow validation. 

#2 - Non-existent or Invalid VINs

Some inspections capture VINs that: 

• Do not conform to VIN standards 
• Fail checksum validation 
• Do not map to any known manufacturer or model year

These invalid VINs often pass unnoticed during manual review but create serious issues downstream in claims automation systems. 

Altered or punched VINs

In more sophisticated cases, VINs are physically altered or re-punched on the vehicle chassis or body. 

These cases are particularly challenging because: 

• The VIN appears visually present
• Supporting documents may look legitimate
• Only deeper verification reveals inconsistencies

This is where remote vehicle inspection platforms like Inspektlabs provide an advantage over manual checks. 

Why VIN manipulation is a serious risk for insurers and repair networks 

Impact on insurance companies

For insurers, VIN mismatches directly affect: 

• Claim accuracy
• Vehicle valuation
• Loss ratio
• Fraud detection effectiveness

When VIN data is unreliable, even advanced Insurance Claims AI systems struggle to deliver accurate outcomes. Incorrect Vehicle identity leads to incorrect claim decisions. 

Impact on Repair Networks

For repair companies and networks, VIN-related issues result in: 

• Incorrect repair approvals
• Part mismatches
• Billing disputes with insurers
• Increased audit exposure

Over time, these issues slow down operations and strain insurer-repairer relationships. 

How Automated Vehicle Inspection platforms like Inspektlabs address VIN fraud

Modern AI-powered vehicle inspection platforms like Inspektlabs approach VIN validation as a part of a broader vehicle identity verification process, rather than a standalone check.

#1 - Motion analysis to detect vehicle swapping 

During video-based inspections, AI systems analyse movement continuity. 

Abrupt camera movements, sudden scene changes, or inconsistent inspection paths can indicate that:

• The inspector has moved between vehicles
• VIN capture and damage capture occurred on different vehicles

This capability is especially important for remote and self-guided vehicle inspections. 

#2 - Physical step tracking around the vehicle

During video-based inspections, Inspektlabs tracks how many steps the inspector walks around the vehicle while documenting it. 

This helps ensure that: 

• The inspector has physically moved around a single vehicle
• VIN capture and damage capture occur within the same inspection workflow
• Unusual walking patterns or incomplete coverage are identified

If the system detects suspicious movement, such as: 

• Abrupt changes in walking direction 
• Unnaturally short or fragmented movement paths
• Inconsistencies between movement and visual contexts

The inspection is flagged for further review. 

By analyzing physical movement, not just uploaded media, the system helps detect potential vehicle swapping or manipulated documentation during remote inspections. 

#3 - VIN decoding and logical validation

VINs are not random strings. Each section of a VIN represents specific vehicle attributes. 

Automated vehicle inspection systems are trained to: 

• Decode the VIN
• Extract brand, make, model, and model year
• Validate whether the VIN structure is logically correct

Invalid or inconsistent VINs are flagged early in the inspection process. 

#4 - Visual verification against the recorded vehicle

One of the most effective safeguards is cross-verifying VIN data with the vehicle’s visual appearance. 

Photo-based vehicle inspection systems analyse: 

• Vehicle shape
• Body type
• Brand-specific design cues

If the decoded VIN does not match the visually recorded vehicle, the inspection is flagged for review. 

How Inspektlabs helps strengthen Vehicle Identity Verification

Inspektlabs integrates VIN validation into its end-to-end Automated vehicle inspection and damage detection platform. 

By combining

• Step counting
• Motion analysis
• VIN decoding
• Visual make, model, and brand verification 

Inspektlabs helps insurers and repair networks: 

• Reduce identity mismatches
• Improve claims accuracy
• Strengthen fraud detection
• Scale automated vehicle inspection systems without increasing risk

Conclusion

As insurance and repair operations continue to digitize, vehicle identity verification can no longer be an afterthought. 

VIN manipulation doesn’t just enable fraud. It undermines the accuracy of inspections, the reliability of claims automation, and the trust between insurers and repair partners. 

By embedding VIN validation into automated vehicle inspection workflows, insurers and repair networks can detect inconsistencies early, reduce financial leakage, and confidently scale remote inspections without compromising control.