Vehicle registration verification in seconds: check a vehicle registration document, Koncile reads the file and scores the risk 0 to 100.
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The vehicle registration checker used by insurers, banks and large companies
4.8 / 5
A registration card or a certificate of title usually arrives as a phone photo or a scan, rarely as a native PDF. The checker analyses the image, cross checks the fields with each other and with the rest of the file, and reads the file structure when there is one.
When a title or a registration arrives as a PDF, its structure tells its story: the software that produced it, the metadata, the way the file was written. A document reopened in a PDF editor, saved in successive versions or with elements deleted comes back with the detail of the change.
We run a proprietary image model trained on fakes. It detects a registration or a title generated in whole or in part by AI from a photo of a real one, as well as a single field rewritten: owner name, title brand, VIN.
AI detected82/100On a scan or a photo, the image itself is analysed: a name pasted over the owner field, digits cloned in the VIN, a white patch over a brand such as SALVAGE, a font that does not match the rest of the document, or a title recomposed from two.
A VIN has 17 characters, no I, O or Q, and a check digit in position 9 that the other 16 must produce. Make, model year and dates must agree with it. The owner and address are then compared with the driver's license and the proof of address in the same file.
Without a tool, these three checks stop clumsy fakes in a few minutes. A tip: ask for a clear photo of the whole document, front and back for a title, rather than a crop.
Count 17 characters with no I, O or Q, then compare the VIN with the plate on the dashboard and the label in the door jamb. Position 9 is a check digit computed from the other 16: the NHTSA VIN decoder flags one that fails, a classic sign of a retyped VIN.
The owner on the title or registration must match the driver's license, the address the proof of address. Read the brand area: salvage, rebuilt, flood or junk. A clean title on a car with a known past, or an out of state title just reissued, deserves a question.
Only the state agency that issued a title can confirm it matches its records, lien included. An NMVTIS vehicle history report, sold by approved providers, shows the latest title data and brands reported across states. Neither looks at the photo you received.
Koncile's fraud detection API gives you a score (0-100) and detailed explanations of manipulation risks, right inside your own tools.
VIN, plate, owner, brands, dates and lienholder structured.
High, medium or low level, with a one sentence explanation.
High risk from 80.
A one line summary ready for your information system.
curl -X POST https://api.koncile.ai/v1/upload_file \ -H "Authorization: Bearer $API_KEY" \ -F "files=@vehicle-title.jpg"requests.post("https://api.koncile.ai/v1/upload_file/", headers={"Authorization": f"Bearer {API_KEY}"}, files=[("files", open("vehicle-title.jpg", "rb"))]){ "status": "DONE", "General_fields": { "VIN": {"value": "4T1B11HK7KU204172", "confidence_score": 0.98}, "Title_brand": {"value": "NONE", "confidence_score": 0.93} }, "Fraud_detection": { "risk_score": 86, "summary": "VIN check digit fails: position 9 retyped", "signals": [ {"name": "VIN check digit", "severity": "high"}, {"name": "Image retouching", "severity": "medium"} ] }}Wherever a title or a registration proves that a vehicle exists and who owns it, a fake costs a policy, a claim paid or a loan with no collateral. Here is where the checker fits in.
The registration is read and checked with the driver's license and the rest of the application. An owner or a vehicle that does not match is flagged before the policy is bound.
The classic scheme: insure, with a stolen or altered title, a vehicle the policyholder never owned, then report it stolen. The document and the owner are checked before payout.
The title is the collateral. Owner, VIN, brands and lienholder are checked against the application, the driver's license and the proof of income in the same file.
Lease buyouts and refinancing put a title in front of a lender who never saw the car. An existing lien erased from the photo shows up before the funds go out.
Rental companies and peer to peer platforms receive registrations from owners listing their cars. The registration is checked before the car goes live, with the detail of every test kept.
A trade in with a washed title or an altered registration may hide a salvage brand, a lien or a theft report. The document is checked before the car joins the lot.
An altered title or registration can keep a real VIN, a perfect layout and a clean history report. The fake vehicle registration detector relies on signals invisible to the naked eye: in the pixels of the photo, in the consistency of the fields, and in the structure of the file when it is a PDF.
| Element of the document | What Koncile checks | What gives an edit away |
|---|---|---|
| Image of the document | Areas retouched, rewritten or generated by AI, analysed by our image model | A field rewritten, a name pasted over, a title generated from a photo of a real one |
| Typography of the fields | Font, size, alignment and spacing of the characters, compared with the rest of the document | A name or a date retyped in another font, shifted by a few points |
| VIN | 17 characters, no I, O or Q, check digit in position 9 (49 CFR Part 565) | A check digit that fails, a forbidden character, a VIN too short |
| Make and model year | Make compared with the manufacturer code at the start of the VIN, model year with its 10th character | A make or a model year that the VIN contradicts |
| Title brand | Brand area read: salvage, rebuilt, flood, junk | A white patch or retyped text over a brand, a brand area that does not match the layout |
| Dates and odometer | Issue date, purchase date and odometer reading: order and plausibility | An issue date before the purchase, odometer digits retyped in another font |
| Owner and address | Compared with the driver's license and the proof of address in the file | A name or an address that does not match the other documents |
| Lienholder | Lienholder field compared with the loan or lease application | A lienholder erased or replaced on a financed vehicle |
| PDF metadata and versions | Producing software, dates, saved versions, deleted elements | A title reopened in a PDF editor, elements removed after issue |
| Photo data | Device, software and dates recorded in the photo (EXIF) | A photo that went through editing software before it was sent |
A title or a registration holds a name, an address, a VIN and lien details. Founded by a former lawyer, Koncile was built with data protection at the core of its architecture: third party SOC 2 audit, GDPR compliance, secure hosting. Documents are encrypted, deleted after analysis and never used to train a model.
It is a tool that combines several technologies to spot the anomalies that give away an edit or a fraud: machine learning that analyses the photo or the scan, and analysis of the PDF file, which is written like code. The goal is to stop the few percent of fakes that slip into your insurance or lending files.
You never know for certain: you measure a risk. It becomes high when one fraud test goes over its threshold, or when several tests point the same way: an edited field on the photo, a VIN check digit that fails, an owner who does not match the driver's license. That body of evidence is what should raise the alarm.
It can help read the fields and spot an inconsistency in context. It lacks three things: models specialised in images edited or generated by AI, knowledge of the traces each editing tool leaves, and a base of reference documents that brings the mismatches out. A language model is only one part of the machine.
It depends on your volume. The price combines a base subscription and a cost per page or per document processed. The plans are detailed on the pricing page.
Each VIN character gets a value, multiplied by a weight for its position. The sum is divided by 11, and the remainder, or X for 10, must be the ninth character (49 CFR 565.15). A failing check digit often means a retyped VIN. A valid one proves consistency, not that the vehicle exists.
Title washing hides a vehicle's past, such as a salvage, flood or junk brand, by retitling it in a state with different rules or by altering the title itself. NMVTIS was built to deter it by sharing title data across states. On a photo of a title, a brand area edited or erased is what Koncile looks for.
Only the state motor vehicle agency that issued the title can confirm it matches its records, lien included. An NMVTIS report from an approved provider shows the latest title data and brands across states. Koncile does not query these registries: it checks the document you received, its image, its fields and its file.
Yes. Under federal law a motor vehicle title counts as a security, and transporting a forged or altered one across state lines with fraudulent intent carries up to 10 years in prison (18 U.S.C. §§ 2311 and 2314). States add their own title fraud offenses. If you receive a fake, keep the original file and the analysis report.
Yes. Koncile complies with SOC 2 and GDPR, and an independent firm certifies our security and data protection. Your documents are hosted securely with a provider that itself holds SOC 2 and ISO 27001 certifications.
Each document gets its own method: a proprietary ML model for photos, known metadata and forensics for native PDFs, and contextual cross checks.
Inflated balances, deleted lines, a PDF rewritten with software other than the bank's.
See the detection
Net pay changed, contributions that do not match gross pay, a layout that does not match the payroll software.
See the detection
Photo replaced, date of birth or number changed, an MRZ that no longer matches the fields.
See the detection
Edited amounts, wrong VAT, duplicates and made up suppliers.
See the detectionUpload your bank statements, pay stubs or IDs: Koncile extracts the data and flags fakes in seconds.
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