Banking & Financial Services · Canada

AI for banks, credit unions and lenders: credit files, covenants, KYC and compliance reviews

Credit, compliance and audit teams spend their week reading borrower packages and customer files. We build AI that extracts the figures, applies your definitions and shows its sources, so analysts review instead of rekeying.

Talk to us about AI for banking & financial services

Why now

Where AI fits in banking & financial services

Commercial lending, compliance and audit work depends on documents that arrive on their own schedule: quarterly compliance certificates, audited and interim statements, appraisals, beneficial ownership records, examination letters and audit findings. The numbers that matter sit in PDFs from hundreds of borrowers, each formatted differently. Language models are now good enough to extract those figures, apply the covenant definitions written into each credit agreement and draft the first version of a memo or alert, with every figure tied to its source page. Credit decisions, risk ratings and suspicious transaction reporting stay with credit officers and compliance staff.

Common challenges

Covenant tracking from quarterly packages. Borrowers send compliance certificates and statements in their own formats. Analysts rekey ratios into spreadsheets and compare them with terms buried in credit agreements and amendments, so early warning depends on someone finding time to read.
Exposure hidden across related entities. Guarantors, holding companies and related borrowers appear under different names and numbering across files, which makes total exposure to one connected group hard to see.
KYC refresh and beneficial ownership. Periodic reviews mean collecting articles, registers and ownership charts, checking them against what is on file and documenting the result for FINTRAC obligations and internal audit.
Screening alerts that need a written disposition. Name screening produces many potential matches from transliterations and common names, and each one has to be closed with a documented reason.

Use cases

AI automation for banking & financial services

Covenant monitoring

Extract reported figures from compliance certificates and financial statements, recalculate each covenant on the agreement's own definitions, and flag borrowers trending toward a breach.

Credit memo first drafts

Assemble borrower background, financial spreads, security and the relevant credit policy sections into a draft annual review that the account manager edits and the credit officer approves.

Connected-group exposure

Resolve guarantors, subsidiaries and directors across loan files into one view of total exposure to a related group.

KYC document review

Read incorporation documents, share registers and ownership charts, list beneficial owners, and highlight what is missing or out of date for the periodic review.

Screening alert triage

Compare a potential sanctions or watchlist hit with customer details such as date of birth, nationality, addresses and known aliases, and draft a disposition rationale the analyst accepts or rejects.

Audit and examination finding tracker

Pull findings from internal audit reports and regulator letters into one register with owners, due dates and evidence of remediation.

Example workflow

Quarterly covenant review for a commercial portfolio

01
Borrowers' compliance certificates and interim statements arrive in the shared mailbox or document system.
02
The agent extracts each reported figure (debt service coverage, leverage, working capital) and records the page it came from.
03
It reads the covenant definitions and any amendments in each credit agreement and recalculates the ratios on that basis.
04
It ranks borrowers by headroom, lists missing certificates and drafts a short note for every borrower near or past a threshold.
05
The account manager reviews the flagged files and the credit officer decides on waivers, watch-list moves or follow-up. The AI never changes a risk rating.
06
Decisions and the cited figures are saved with the credit file for the next review and for audit.

Banking: spotting borrowers close to a covenant breachAnimated, narrated story: James, a credit analyst, connects compliance certificates and loan files to Akurion and asks which borrowers are close to a covenant breach. The answer flags one breach and one tight borrower, each cited, using fictional sample data.

From our work

Local sanctions-intelligence engine

Resolves aliases, transliterations and shell entities from public watchlists into one graph, with a local open-weight model making the judgment calls.

Data and privacy

Handling sensitive data

Who regulates a Canadian financial institution depends on its charter: banks and federally regulated trust and loan companies are supervised by OSFI, whose guidance covers third-party, technology and model risk, while most credit unions answer to provincial regulators (FSRA, in Ontario's case). Customer information is also covered by PIPEDA, and KYC and AML work by FINTRAC requirements. For lenders and wealth firms we can keep the whole system inside your environment, run open-weight models for sensitive files, document how each model is used and tested so model risk and vendor reviews have what they need, and log every output. We don't give regulatory advice; your compliance team sets the rules the system follows.

Kitchener-Waterloo

Local support

Waterloo Region's financial sector includes credit unions, wealth managers, fintech start-ups and regional teams of national lenders. We work with them from Kitchener, in person for discovery and remotely for the build.

AI consulting in Kitchener-Waterloo AI for small businesses

AI for banking & financial services: frequently asked questions

Can AI spread financial statements for credit analysis?

It can do most of the extraction. Models read balance sheets, income statements and notes from PDFs, map line items to your spreading template and show the page behind each number. Unusual items, restatements and figures the model is unsure about are flagged for the analyst, who checks the spread before it feeds a ratio, a risk rating or a memo.

Can AI apply covenant definitions that differ in every credit agreement?

Yes, and that is the main reason to use a language model here. Agreements define EBITDA, funded debt and permitted add-backs in their own words, and amendments change them. The system reads the definitions for each borrower and applies them, citing the clause, instead of using one generic formula. The analyst can see exactly which definition produced each ratio.

What will our model risk team want to see before approving an AI model?

Usually a clear statement of what the model does and does not decide, the data it uses, how it was tested on your own files, its known limitations and how its outputs are monitored over time. We provide that documentation, a test set built from historical files and logs of every answer, so validation can review it like any other model in your inventory.

Can a credit union use AI without a large IT team?

Yes. Many credit unions run banking and document systems through vendors, so the AI layer connects to exports, shared drives or vendor APIs rather than replacing anything. It can be hosted in a Canadian cloud region or on a server you control, and we can handle monitoring after launch so staff use the system rather than maintain it.

Other industries

Insurance Legal Accounting Healthcare Manufacturing Real Estate

Tell us about the process you want to automate.

A 30-minute call is enough to tell whether AI is a fit, what a first project would look like and what it would take to run it in production.

Start a conversation dassjaylabs@gmail.com