AI in banking for the KYC, AML and loan file work your operations team does by hand
We build AI agents that prepare onboarding checks, alert cases and credit files inside the systems your bank already runs. An analyst approves every decision, and customer data stays in your own cloud.
Jason, our COO, runs the call and scopes the first workflow with you.
- Alert receivedNine cash deposits just under $10,000 in 12 daysDone
- KYC file pulledCustomer profile, risk rating, stated businessDone
- Activity summarized90 days of transactions against the stated businessDone
- History checkedEarlier alerts and case notes on this customerDone
- Analyst decisionDraft disposition and evidence sent for reviewWaiting
- Written to case managementNarrative draft, evidence links and run log entryAfter approval
What the agent can seeOnly the fields this task needs. Everything else stays in your systems.
- Customer name
- J. R••••
- Risk rating
- Visible
- Transactions, 90 days
- Visible
- KYC documents on file
- Visible
- Social Security number
- Masked
- Other customers' accounts
- Not retrieved
Illustrative run. Fields, rules and thresholds are set per workflow during scoping. The agent never closes an alert or files a report.
Clients include
AI use cases in banking operations
Start where the volume is high, the rules are written down, and someone on your team already reviews the work before it counts. These six workflows meet all three.
KYC and onboarding document checks
The agent reads IDs, formation documents and beneficial ownership forms, checks each one against your onboarding checklist, and hands your analyst a file with every gap marked.
Same document pipeline as our invoice automationAML alert triage
For each alert, the agent pulls the customer file, summarizes the activity against the stated business and drafts a disposition with its evidence. Your analyst decides, and the agent never closes an alert.
How approval gates workLoan and credit file preparation
The agent collects tax returns, financial statements and rent rolls, extracts the figures your spreading template needs, and lists what the borrower still owes the file. Your underwriters make the credit decision.
Prove it on your own files firstComplaints and customer questions
The agent answers routine questions about fees, hours and document requests, sorts incoming complaints by product and issue, and hands anything about a dispute or a specific account to a person with the conversation attached.
How a second agent checks every replyRegulatory reporting and MI prep
The agent gathers source extracts, reconciles them against the last period and flags every movement past the threshold you set. The database computes the numbers, and the model writes only the commentary.
Work out the payback for one reporting cyclePolicy and procedure answers for staff
Branch and operations staff ask a question and get the answer from your current procedures with the source page cited, so a new hire stops waiting on the one colleague who knows the manual.
See the context layerThe compliance workload behind every account
total cost of financial crime compliance for financial institutions in the US and Canada
Source: LexisNexis Risk Solutions, True Cost of Financial Crime Compliance Study, US and Canada edition, conducted by Forrester Consulting (2024).FinCEN and LexisNexis Risk Solutions counted where the hours go. Each figure maps to the agent that takes on part of the work.
4.6 million suspicious activity reports filed in FY2023
Depository institutions filed about 2.5 million of them. The triage agent assembles the evidence behind each alert, so your analysts spend their time on the decision.
99% of institutions saw compliance costs rise
79% reported higher technology costs for compliance and KYC software. Onboarding agents work through the KYC tools you already pay for, using their APIs.
70% are prioritizing lower compliance costs this year
The AI Audit ranks your candidate workflows by value, effort and risk, so the first build goes where the hours are.
Start in the queue, before the decision
An agent that prepares an alert or a credit file gets checked by the analyst who already reviews that work. Decisions to file, close or lend stay with your people.
Sources: FinCEN, Year in Review for FY 2023; LexisNexis Risk Solutions, True Cost of Financial Crime Compliance Study, US and Canada (2024).
AI governance in banking, built into the workflow
Your compliance and model risk teams will ask what the agent saw, what it did and who approved it. We build those answers into the system before it goes live.
- SourcesCore banking, case management, document storeRead through the interfaces they already expose
- Context layerScopes and masksOnly the fields each task needs, identifiers masked
- AgentSelf-hosted n8nRuns the workflow and its rules in your cloud
- ApprovalAnalyst sign-offEvery disposition, before write-back
run 20417 · AML alert triage · 4 sources read · SSN masked · disposition drafted · approved by analyst · written to case management
A model provider receives scoped, masked fields only, under your own agreement with that provider, or is replaced by a model you host.
- An agent filing, closing or approving on its own
- Customer data used to train models
- Figures the model calculated itself
A named person on every decision
Dispositions, credit packages and complaint responses wait for a named approver before anything is written back. Each workflow has an owner and an escalation path before it goes live.
See AI governanceA second agent checks each draft
Before a draft reaches an analyst or a customer, a second agent checks it against your rules and sends it back with the violation named, so your analyst reviews only drafts that passed.
How the second-agent check worksBuilt and logged in your environment
We deploy on self-hosted n8n in your cloud, behind your own auth, with permissions inherited from the systems the agent reads. Every run is logged with the records it read, what it changed and who approved it.
Built on self-hosted n8nHow a banking AI build runs
The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. See how we work
Jason, COOAI Audit, if you need one
Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.
- Jason, COO
Scope
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
- The fields each agent may read
- The decisions that always go to a person
- The timeline, before you sign
Maciej, CTOBuild
Built against your real data, in your environment, as production software from the first week.
- Staging first, on test or masked records
- Your analysts use it on real cases before go-live
- Self-hosted n8n in your cloud
- Jason, COO
Integrate
Into production behind your auth, your governance and your monitoring, with your team trained on it.
- Separate development, pre-production and production environments
- Runs behind your own auth
- Your people trained on the system
- Maciej, CTO
Run and improve
Watched, fixed and extended in weekly sprints by the engineers who built it.
- Error alerts to the engineers who built it
- Weekly sprints that you direct
- The next workflow, scoped with you
What your operations team gets from a build with us
We build the agent around your policies and run it in your environment.
| What you get | |
|---|---|
| You receive | Working agents inside your core, case and document systems |
| Where customer data lives | In your cloud, on self-hosted infrastructure |
| Fit to your rules | Built around your onboarding, alert and credit policies |
| Decisions | Made by your people, approved and logged |
| Timeline | Scoped per workflow and agreed before you sign |
| After launch | The engineers who built it monitor and extend it |
What banks ask about AI agents
Ask us something elseHow is AI used in banking operations?
Mostly in the queues behind the customer: KYC and onboarding checks, AML alert triage, loan file preparation, complaint sorting and reporting prep. In each one, an agent reads the documents and systems a person would, assembles the case and drafts the next step, and a person makes the call. Fully automated steps are limited to work with written rules, such as asking a customer for a missing document.
How do you secure AI agents that touch customer data?
Four controls do most of the work. A context layer gives each agent only the fields its task needs and masks identifiers. The workflow runs on self-hosted n8n in your cloud, behind your own auth, with permissions inherited from your systems. Any external model call carries scoped fields only, under your own agreement with that provider, or goes to a model you host. Every run is logged in your environment with what the agent read, what it changed and who approved it, so your compliance team has a record to review.
Can an AI agent decide an AML alert or a loan?
No. In the systems we build, the agent prepares the alert case or the credit file and drafts a recommendation with its evidence. Your analyst or underwriter decides, and the agent cannot close an alert, file a report or approve credit.
Can you connect to our core banking system?
We connect through whatever your systems expose: a vendor API, a database view you give us read access to, scheduled exports, or the documents that arrive by email and upload. For an older core, scheduled exports into a staging database often work best, so the agent reads the staging copy and never queries the core directly. The AI Audit confirms what each of your systems allows before anything is quoted.
How long does it take to implement AI in banking?
A single workflow on systems with a clean interface usually takes weeks. One that spans several systems, or needs a context layer built first, takes longer. We scope each workflow and give you the timeline before you commit. If you want proof first, an AI pilot runs 2 to 6 weeks on your own data and costs $35,000 to $75,000, with the fee credited toward the build.
What is agentic AI in banking?
An agent takes several steps toward a goal and chooses which system to read next, where a chatbot answers one prompt. In banking operations that means an agent that opens the alert, pulls the KYC file, reads the transactions and drafts the case, then stops at the approval gate. The steps it may take, the fields it may read and the point where a person takes over are written down before we build it.
Where should a bank start with AI?
With the operations queue that has the most volume and the clearest rules, usually onboarding checks or alert triage. If you already know which one, book a discovery call and we will scope it. If you have several candidates, the AI Audit ranks them by value, effort and risk over 4 to 8 weeks and ends in a 90-day roadmap. It costs $10,000 to $100,000 depending on scope, and the fee is credited toward your first build.
Who runs the system after launch?
The engineers who built it, in weekly sprints that you direct, with error alerts going straight to them. If another team built workflows you already run, we take them over after a technical assessment of their quality. See AI managed services.
Other regulated teams that prepare files for a person to approve
Insurers, accounting firms, deal teams and law firms run the same pattern as your KYC and alert queues: an agent assembles the file, and a named person decides.
AI for insurance agents
Quote intake, renewals, certificates and claims intake for agencies, brokers and MGAs, with a licensed person approving coverage.
See AI for insurance agentsAI in accounting
Bank reconciliations, close files and tax workpapers prepared inside your firm's systems, with an accountant signing off.
See AI in accountingPut your first workflow on the board
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
