Build everyday back-office agents on one live model of your books
AI agents that run the entire regulatory reporting pipeline for banks, from raw ledger data to the filed report.
TL;DR: Definite automates regulatory reporting for banks end to end, from raw ledger data to filed report, turning weeks of manual work into hours. usedefinite.com
Hey! We're Gurshabd, Mazin and Farhan from Definite.
https://www.youtube.com/watch?v=ji1ae8y_V4E
The Problem
Proving to regulators that their books follow the rules is a constant, non-negotiable obligation for banks. Each report is thousands of numbers, personally signed off by an executive. Getting it wrong can mean a fine up to a million dollars over a single undetected error. Producing that proof is slow, and doesn't even guarantee it's correct.
Here's where the current system breaks down:
- Wasted Hours: A controller pulls data from the core system into Excel and maps it line by line by hand. By regulators' own estimate, one quarterly filing takes weeks of employee time, time pulled from higher-value finance work every quarter.
- False Confidence: The software banks use to file checks formatting, not truth. A loan mapped to the wrong schedule line sails through every check. Nothing catches it until an examiner does, and by then it's not a mistake, it's a violation.
The Solution
Report agents: We connect to your books and our agents build the reports you owe.
Verification engine: Every number is recomputed from source records and checked against a versioned rulebook, so each line carries its rule and source data.
Self-correcting agents: When a check fails, the agent gets the reason and fixes it in seconds. Real exceptions come to your team with a drafted explanation, not a blank error. This loop is why our agents are the most accurate in finance.
Receipts: Every check produces a tamper-evident receipt you can hand an examiner or auditor.
Your team reviews and signs instead of building.
How we got here
Gurshabd worked at BitGo, a regulated crypto custodian, where attestations proving customer assets matched the ledger were built by hand every period. Farhan worked at Meta where he delved extensively into machine learning and building AI agents. Mazin saw Optiver recheck daily regulatory numbers by hand, then built LLM agents at Boosted.ai and learned finance people won't act on AI output unless every number comes with its source.
The lesson from all three: in finance, producing a number is cheap, proving it is the job. On two public reporting benchmarks, DeepSeek V3 with our verification engine outperformed GPT-5 on reconciliation accuracy (FinBalance) at roughly 50× lower cost, and beat the entire published field on XBRL calculation validation (FinAuditing).
The Ask
- Work at a bank, credit union, fintech or insurer on regulatory filings? Let's talk: https://calendly.com/usedefinite/intro, or text me: +1 519-619-5467
- We’d also love an intro to a VP of Finance or Engineering at your partner bank (personal or business)
We'll be in the comments. gurshabd@usedefinite.com
