Banking & Lending

Agentic AI that survives the exam

Banks and lenders don't need another chatbot — they need agents that can do the work and prove they did it right. We build automation for KYC, underwriting, fraud, and disputes with adverse-action logic, decision lineage, and human sign-off baked in.

  • KYC/AML & onboarding
  • Loan underwriting support
  • Fraud & dispute triage
  • Exam-ready audit trails
$0
of customer data that leaves your perimeter
100%
of decisions captured with reason codes
24/7
monitoring of alerts and onboarding queues
T+0
first-touch on fraud and dispute cases
// the reality

The constraint isn't ambition — it's the regulators

Every bank knows where the busywork is. The hard part is automating it without tripping a rule.

A lending shop runs on a stack of obligations that don't forgive a clever shortcut. KYC and CDD under the BSA. Adverse-action notices under FCRA and ECOA the moment you decline a borrower. SAR filing deadlines that don't move. Model-risk governance under SR 11-7 the moment a model touches a credit decision. UDAAP exposure on anything a customer reads. An examiner who will, eventually, ask you to show your work for any one of them.

That's exactly why generic AI tools stall in financial services. A summarizer that can't cite its reasoning is a liability the day an adverse-action notice is challenged. A model that quietly leans on ZIP code is a fair-lending finding waiting to happen. Automatic.co builds for that constraint first: agents that take real action inside your systems, but only along paths your compliance team has approved, with a record you can hand to an examiner without flinching.

// where agents earn their keep

Use cases built for a regulated balance sheet

High-volume, rule-bound, document-heavy work — the kind that burns out analysts and where consistency is itself a control.

// compliance as architecture

Guardrails the examiner can read

In a bank, 'the AI did it' is not an answer. So we don't bolt governance on at the end — we make it the structure the agent runs inside. Every action flows through an approval layer keyed to dollar amount, risk band, and action type, and every credit-relevant decision resolves to disclosable reasons, not a number.

The result is a decision lineage you can replay: which record was touched, which policy applied, what the model saw, why it concluded what it did, and who signed off. That's the artifact your model-risk, fair-lending, and audit teams have been asking for — produced automatically, for every case.

  • Adverse-action reason codes on every decline
  • Proxy screening for ECOA protected classes
  • Approval gates by amount, risk, and action type
  • Replayable, examiner-ready decision lineage
// how we deploy

From one workflow to a governed fleet

We start where the risk is contained and the ROI is obvious, then expand as trust compounds.

01

Map & scope

Pick one workflow — say, onboarding triage — and pin down the controls, systems, and exam expectations it lives under.

02

Architect controls

Design the action layer, approval gates, reason-code logic, and human checkpoints with your compliance and MRM teams in the room.

03

Deploy in your VPC

Integrate with your core, LOS, and case systems inside your perimeter, and ship to production behind the gates.

04

Validate & expand

Back-test against historical decisions, document for the model inventory, then roll the pattern to the next workflow.

A generic LLM tool vs. an Automatic.co agent

Why off-the-shelf AI keeps failing financial-services pilots.

Generic AI toolAn Automatic.co agent
Credit decisionsOpaque score, no reasonsDisclosable adverse-action factors
Fair lendingMay learn protected proxiesInputs screened, impact testable
Data locationVendor cloudYour VPC, on-prem, or air-gapped
Audit trailPrompt logs at bestReplayable per-decision lineage
AuthorityActs or advises freelyGated by amount, risk, and type

Frequently asked questions

How do agents stay compliant with fair-lending and adverse-action rules?

Credit-decision agents never act on a black-box score. Every decline or counteroffer is mapped to specific, disclosable reasons (FCRA adverse-action codes, ECOA-safe factors), and the model inputs are screened for proxies for protected classes. The full reasoning chain is captured so your fair-lending and model-risk teams can test for disparate impact.

Will an agent ever approve a loan or close an account on its own?

Not unless you decide it should. We set approval gates by dollar amount, risk band, and action type. Routine, low-risk steps (document collection, data reconciliation, SAR drafting) can run autonomously; underwriting decisions, account closures, and anything customer-facing route to a human with the agent's full rationale attached.

Where does our customer and transaction data live?

Inside your perimeter. We deploy in your VPC, on-prem, or a fully air-gapped environment so PII, transaction histories, and core-banking data never leave your control. This is the deployment model that keeps GLBA, SOX, and your examiners comfortable.

How do these agents connect to our core, LOS, and case systems?

Through a governed action layer — read/write integrations to your core banking platform, loan origination system, BSA/AML case manager, and data warehouse. Every tool call is logged with the record it touched, so you get a complete, replayable trail for each decision.

Explore adjacent industries

We build the same compliance-first agents across regulated sectors.

Bring your hardest workflow. We'll bring the controls.

One working session to scope a banking or lending workflow you'd never trust to a black box — and map the agent, the guardrails, and the audit trail that make it deployable.