Core finance systems are shifting from manual review and rigid rules to policy-aware agents that read documents, retrieve data, and take actions with approvals. Fraud and KYC queues are triaged faster, invoice and reconciliation work is handled touchlessly, and support teams answer policy-grounded questions at scale. New opportunities include near-real-time cash forecasting, policy-cited compliance reporting, and automated evidence packs for audits.

What’s an AI Agent and what is not

A clear mental model helps scope pilots and set controls.

Which are the most common AI agents in finance?

Below are practical clusters, how to think about them, and a simple StackAI-style use case for each. Examples draw from a curated finance library.

1) Risk and Compliance Agents

What they cover

Fraud triage, KYC and AML investigations, policy monitoring, and audit evidence generation.

Why they matter

They cut time to disposition while improving consistency. They also create a clean audit trail of context, reasoning, and approvals.

Typical actions

• Enrich alerts with device, merchant, and counterparty data.

• Extract identity fields from documents, cross-validate, and flag gaps.

• Draft SAR or STR narratives for analyst sign-off.

StackAI example

KYC Automation Agent: ingest IDs and utility bills, extract fields with OCR and LLMs, validate across sources, tag risk, and produce a structured KYC report in under two minutes, with traceable citations. Many profiles auto-verify, leaving exceptions for analysts. 

Adjacent examples you can reuse

• Refund and Expense Agent: classify receipts, validate against policy, and create a ticket with the decision and rationale, often processing 90 percent of valid claims without manual review. 

• 10-Q / 10-K Filing Extractor: pull risk, debt, and KPI sections into a brief, reducing hours of manual review while preserving source references. 

2) Finance Operations Agents

What they cover

Invoice capture, matching and reconciliation, month-end tasks, CapEx classification, and payments exceptions.

Why they matter

They raise touchless rates and shorten the close with clearer exception queues.

Typical actions

• Extract supplier, amount, and line items from invoices.

• Match GL entries to bank statements and flag variances with context.

• Classify spend as CapEx or OpEx with policy citations.

StackAI examples

• Financial Statement Reconciliation Assistant: auto-match transactions and propose journal entries, targeting 95 percent auto-reconciliation and a faster close with traceability for SOX and IFRS. 

• CapEx Classification Agent: read RFCs and internal policy, classify each line with a citation, and generate an audit-ready capitalization report. Teams report large tax benefits from consistent classification and faster reviews. 

3) Customer and Frontline Assistants

What they cover

Banking support, card disputes, fintech policy questions, and structured handoffs to back-office teams.

Why they matter

They reduce average handle time and increase first-contact resolution by grounding answers in approved policies and proposing next actions.

Typical actions

• Retrieve policy passages with citations.

• Prepare compliant replies.

• Propose safe actions like refunds or replacements with approval gates.

StackAI example

Finance FAQ Assistant: answer recurring compliance or investor questions by pulling from controlled repositories and returning cited answers for quick review and send. 

4) Analyst and Decision Support Agents

What they cover

Spreadsheet Q&A, earnings-call digestion, valuation drafts, portfolio actions, and cash forecasting.

Why they matter

They turn research and analysis that used to take hours into minutes, while keeping links back to the source data.

Typical actions

• Read spreadsheets and point to cell-level evidence.

• Summarize transcripts with speaker-level sentiment and KPIs.

• Build scenario models and propose actions.

StackAI examples

• Spreadsheet Assistant: answer business questions over CSV or XLSX with cell-level citations so reviewers can verify inputs quickly. 

• Cash-Flow Forecast and Scenario Planner: assemble a 13-week forecast and best-base-worst scenarios from uploads, improving liquidity visibility without manual spreadsheet work. 

Which features are most used for AI agents in finance?

The most successful programs emphasize control, not maximum autonomy. Below are features teams standardize early.

1) Human-in-the-loop and approvals

• Queue reviews for any action that touches money, customer communications, or regulatory filings.

• Use confidence thresholds to route items to the right role.

• Require two-person approval for sensitive moves like payouts or account changes.

2) Policy grounding with citations

• Restrict retrieval to allow-listed repositories: policy docs, product manuals, and current rate cards.

• Return excerpted passages and links in every answer that informs a decision.

• Reject responses when grounding is missing or stale, then escalate.

3) Traceability and audit logs

• Log prompts, retrieved sources, tool calls, inputs, outputs, and human decisions.

• Retain immutable traces for each case to support audits.

• Emit structured events to your SIEM or data platform for monitoring.

4) Access control and least privilege

• Define tool scopes at the operation level: read, draft, post, and reverse.

• Enforce per-environment secrets, IP allow-lists, and SSO.

• Simulate actions in a sandbox before enabling write access in production.