By the end of this you'll know how to use AI to turn a messy trucking submission into clean schedules, and which FMCSA checks you still do yourself.
A trucking submission arrives in pieces.
A driver list typed by the owner's spouse. A vehicle schedule with three trucks missing VINs and one VIN with only sixteen characters. Loss runs from two prior carriers. An MC number on a napkin, more or less.
You know the carrier will quote the clean submission first. You also know cleaning it takes most of a day.
That day is where AI pays for itself on a trucking account. It grinds through schedules without getting bored, catches the typo on row 23, and hands you a list of questions. It doesn't decide anything about the risk.
What can AI do on a trucking account?
The schedules and the summary. The checks that a carrier relies on stay with you.
| Job | What AI does | What you verify |
|---|---|---|
| Driver schedule | Standardize names, dates and license classes; compute years of experience; flag gaps | MVRs, pulled through your proper channel |
| Vehicle schedule | Standardize year, make, model, GVW, values; flag bad or missing VINs | Decoded VIN data and stated values |
| FMCSA cross-check | Compare power units and drivers on SAFER to the schedules | Authority and filing status |
| Loss runs | Combine carriers into one five-year summary | Open claims and reserves |
| Submission summary | Write the cover note: operation, radius, commodities, safety story | Every fact in it |
| Supplementals | Map your clean data onto each carrier's questions | Answers only the insured can give |
How do you clean a driver and vehicle schedule?
Redact, standardize, flag. Then check the flags.
Driver data is sensitive. License numbers and dates of birth don't belong in a consumer chatbot, and MVR data comes with its own legal limits, the federal Driver's Privacy Protection Act among them. So start by stripping it.
- Make a working copy of the schedule.
- Replace license numbers with driver numbers and dates of birth with ages.
- Use a business plan that doesn't train on your data. Our guide on whether ChatGPT is safe for client data explains why.
- Ask AI to standardize and flag, never to fill in.
- Work the flags yourself, then merge back into your own system.
Clean this trucking driver schedule. Rows are identified by DRIVER_NO.
Standardize: license class (A, B, C), license state (2-letter),
hire date (MM/DD/YYYY), age (whole number), years of CDL experience.
Flag: duplicates, drivers under 23 or with under 2 years of CDL
experience, missing hire dates, and anyone listed as excluded.
Return the clean table, then a separate list of flags. Do not invent values.
The age and experience thresholds in that prompt are placeholders. Swap in your carrier's actual driver guidelines.
How should you handle VINs?
Decode with NHTSA. Use AI to compare.
A language model can check that a VIN has 17 characters and no I, O or Q. It shouldn't be trusted to decode one from memory. Use NHTSA's free vPIC decoder for year, make, model and body type, then give AI both lists and ask for mismatches.
Compare the vehicle schedule to the decoded VIN data.
Match rows by VIN. For each vehicle, flag: model year mismatch,
make or model mismatch, a VIN that failed to decode, and any
power unit listed without a VIN.
Return only the rows with a flag, with both values side by side.
Model year mismatches are the most common catch. A 2019 that's really a 2017 changes the rate and the value. Our guide on how to get vehicle information from a VIN explains the check digit and the model-year code.
Which FMCSA data belongs in the submission?
The carrier's record, checked against what the client told you.
Look the client up on FMCSA's SAFER company snapshot by USDOT or MC number. It shows operating status, carrier operation, the number of power units and drivers reported, and any safety rating. Then compare:
- Power units on SAFER vs. trucks on the schedule. A big gap is a question.
- Drivers on SAFER vs. drivers on the schedule. Same.
- Operation type (interstate, intrastate, hazmat) vs. what the application says.
- Authority status. Active, pending, or revoked.
AI is handy for the comparison and the write-up. The lookup itself takes a minute, and you want to see the record with your own eyes.
What about filings like the MCS-90 and BMC-91?
They come from the insurer, and you confirm them. AI can't file anything.
Interstate for-hire carriers hauling general freight in vehicles of 10,001 pounds or more must carry at least $750,000 in liability, with higher minimums for certain hazardous materials. FMCSA's insurance requirements page lists the amounts by type of operation.
Proof runs through filings:
- MCS-90. The federal endorsement attached to the auto liability policy for carriers subject to the financial responsibility rules.
- BMC-91 or BMC-91X. Filed by the insurer with FMCSA as proof of liability coverage for carriers with operating authority.
- BMC-34. Cargo coverage proof for household goods carriers.
FMCSA explains who files what on its insurance filing requirements page. After binding, check that the filing actually appears on FMCSA's licensing and insurance records. The carrier's operating authority depends on it, and we thought it was filed is a terrible phone call.
This isn't legal advice. FMCSA and your carriers have the final word on requirements and filings.
Where does AI get trucking wrong?
Wherever the answer lives outside the documents.
- Radius and commodities. The schedule doesn't say the owner started hauling hazmat in June. Ask.
- Owner-operators and leased drivers. Who's an employee, who's leased on, and who's excluded changes the coverage. AI can't tell from a list.
- Garaging. The mailing address and the yard aren't always the same place.
- Loss runs. AI can total them. It can't tell you why a claim is still open.
Where should you start?
With your ugliest renewal. Run the driver and vehicle schedules through the prompts above and time it against last year.
Then build the habit: SAFER check on every new account, VIN decode on every schedule, filing confirmation after every bind.
For the paperwork around it, Salience works with agencies on applications, submissions and renewals. You forward the request, Salience prepares it, and nothing goes to a carrier until you approve it. Combining loss runs is covered in AI for loss runs, and the rest of the AI by line of business shelf covers contractors, commercial lines and more.
Give us a call
Hey, all the tech is great, and innovation is great. There's a lot of responsibility and liability across the entire space, and a lot of people are saying the same words that mean different things. Give us a call. We plug in specifically to your processes and take care of the GDS first to unlock you and bring home the bacon.
Call +1 (916) 866-7867 or book a walkthrough.
Would you rather approve this than type it?
Forward the request or the prior application. Salience prepares the form, flags what's missing, and nothing goes out until you approve it.
Frequently asked questions
How can AI help with trucking insurance?
AI is best at the schedules: standardizing driver and vehicle lists, catching duplicates and bad VINs, lining the schedule up with decoded VIN data and FMCSA records, and drafting the submission summary. A person still checks MVRs, filings and anything the carrier will rely on.
What goes into a trucking insurance submission?
Typically the ACORD application, a driver schedule, a vehicle and trailer schedule with VINs and values, radius and commodities hauled, loss runs, prior carrier details, and the USDOT and MC numbers. Each carrier adds its own supplemental.
How do I use FMCSA data for insurance?
Look the carrier up on FMCSA's SAFER system by USDOT or MC number to confirm its status, operation type, power units and drivers, and use FMCSA's licensing and insurance records to see filings on file. Compare both to what the client told you.
Can AI decode a VIN for a vehicle schedule?
AI can check VIN format and flag obvious errors, but use NHTSA's free vPIC decoder for the year, make and model, then have AI compare the decoded data to the schedule and flag mismatches.
Is it safe to put driver information into ChatGPT?
Not in a consumer account, and not with license numbers or dates of birth. Use a business plan, replace identifiers with placeholders, and keep MVR data in the systems built for it.