How to Use Agentic AI in Finance (2025)

A Brief Intro to Agentic AI in Finance

Agentic AI refers to autonomous systems that can reason, make decisions, and take action within defined guardrails. Unlike generative AI, which produces content (e.g., text, code), agentic AI technology orchestrates actions based on logic, context, and past performance.

Think of it as going from “ask a model a question” to “give a digital teammate a goal.”

This distinction matters. According to Thomson Reuters, agentic AI tools are defined by their ability to pursue goals and interact with systems, whereas generative AI is limited to content generation without real decision agency.

That’s why it’s particularly valuable in finance, where workflows span multiple systems, stakeholders, and compliance layers.

Whether it’s onboarding a client, processing a loan, or preparing regulatory reports, our agentic AI platform, AgentFlow, is an example of verticalized agentic AI that securely automates this type of end-to-end execution with full traceability, risk mitigation, and domain-specific intelligence.

Still, trust remains a top concern. According to a Deloitte report, Deloitte recently found that 80% of finance leaders cite trust as the main barrier to agentic AI adoption. That’s why enterprise platforms like AgentFlow prioritize in-VPC deployment, SOC 2 compliance, role-based access controls, and fully auditable execution logs.

How to Use AI in Finance

1. Client & Account Onboarding

Onboarding new clients in financial services is a high-friction, document-heavy process that often delays revenue recognition and frustrates both users and internal teams. Agentic AI transforms this by executing the entire onboarding workflow, from intake to compliance clearance, without requiring handoffs between siloed systems or departments.

Using Conversational AI, agents can interact directly with clients to request missing documents, clarify form entries, and guide users through complex submission requirements. Document AI then classifies and extracts relevant information (e.g., IDs, bank statements, tax forms) while maintaining structured outputs aligned to internal schemas.

Decision AI layers on real-time KYC (Know Your Customer), KYB (Know Your Business), and AML (Anti-Money Laundering) checks, flagging anomalies for manual review or escalating edge cases based on risk thresholds.

The result is faster onboarding with audit trails. In one of our deployments, Document AI processed over 40 unique form types in student lending, reducing time-to-decision by 60% and eliminating over 70% of manual reviews.

Read more about the results of this deployment in our customer story.

2. Compliance & Risk Management

Compliance teams in finance face a moving target: evolving regulations, rising enforcement, and more pressure for risk and fraud detection in real time. Agentic AI brings continuous monitoring, rule enforcement, and adaptive logic to help institutions stay ahead.

Decision AI and Database AI agents automatically scan transactions for suspicious patterns, cross-reference against up-to-date sanctions lists, and flag anomalies for review, eliminating the need for batch-based checks.

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For regulatory filings, Report AI standardizes report generation (e.g., IFRS 9, CECL, Basel III), ensuring every output is traceable and audit-ready. Risk simulation agents test portfolios against stress scenarios, surfacing compliance violations or capital shortfalls proactively.

By embedding explainability and audit trails into each decision, AgentFlow enables finance teams to maintain compliance with confidence while lowering the cost and complexity of traditional risk operations.

3. Financial Data Processing

Finance teams process an enormous volume of heterogeneous data, from 10-Ks and tax returns to invoices and payment records. Traditionally, this work has relied on human parsing and manual reconciliation. Agentic AI eliminates these bottlenecks by extracting, validating, and structuring data at scale.

Unstructured AI processes PDFs, scans, and web forms, breaking down text into semantically meaningful chunks for ingestion. Document AI classifies files, extracts fields with confidence scores, and ensures formatting consistency across systems. This structured data is then piped into Database AI, which validates entries, performs reconciliations, and flags discrepancies for resolution.

Every agent action is tracked in execution logs, enabling finance teams to drill into the root cause of mismatches or errors. The result is cleaner data, fewer reconciliation delays, and faster month-end and year-end cycles.

4. Portfolio & Asset Monitoring

Monitoring asset performance and portfolio risk is core to financial management, but it’s often hampered by fragmented data and manual reporting. Agentic AI creates always-on visibility across holdings, benchmarks, and regulatory limits. Database AI continuously ingests market data and internal performance metrics, while Decision AI applies custom rules to evaluate exposure, track compliance, and simulate stress scenarios.

AI agents can flag drift from portfolio mandates, detect unbalanced allocations, or notify managers when a trigger threshold is hit. Report AI then generates templated portfolio updates, client memos, or compliance disclosures, ensuring documentation is consistent and timely.

In production environments, AgentFlow has been used to consolidate asset data from structured and unstructured sources, cutting report generation time by 80% and improving financial audits and accuracy through traceable logic paths.

5. Transaction & Deal Support

Financial transactions, especially M&A, structured finance, or syndicated lending, involve enormous document loads, multiple counterparties, and complex valuation models. Agentic AI accelerates this work by extracting critical terms, running validations, and drafting deal documentation in minutes.

Document AI pulls clauses, pricing terms, and counterpart identities from NDAs, term sheets, and agreements. Database AI supplements this by searching past transactions and market data to validate assumptions. Decision AI supports diligence by identifying risks, mismatches, or missing disclosures.

These actions culminate in Report AI drafting summaries, credit memos, and internal approvals that align with institutional templates. For private equity and structured finance teams, this end-to-end orchestration cuts review time, improves consistency, and ensures that even complex deals are fully documented, traceable, and regulator-ready.

6. Reporting & Analysis

Internal and external financial reporting continues to be a source of drag, especially when teams rely on legacy systems and spreadsheets. Agentic AI speeds up reporting cycles by automating data compilation, formatting, and insight generation while embedding transparency into every step.

Database AI aggregates operational and financial data from disparate systems and normalizes it for consumption. Decision AI can then apply logic layers to classify spend, track variances, or run forecast comparisons. Finally, Report AI packages this into dynamic dashboards or downloadable reports for regulators, investors, or internal leaders.

Each report includes execution metadata, confidence scores, and field-level traceability, ensuring every insight is verifiable. This structure enables finance teams to reduce reporting cycles from weeks to days, all while maintaining compliance and improving communication quality with stakeholders.

7. Operations & Finance Administration

Back-office finance operations, like invoice handling, payment matching, and vendor onboarding, consume a disproportionate amount of time and introduce frequent errors. Agentic AI reduces these burdens by orchestrating end-to-end workflows without human intervention.

Document AI extracts line items from vendor invoices, applies schema-based validation, and categorizes expenses according to your accounting standards. Database AI matches payments to ledger entries and flags outliers or failed reconciliations. For onboarding, Conversational AI interacts with vendors to collect W-9s, bank info, and policy documents while ensuring compliance via Decision AI checks.

All actions are observable through AgentFlow’s monitoring dashboards, enabling finance ops to resolve issues quickly and maintain a continuous improvement loop.

Customers using these agents have replaced 5+ FTEs in AP/AR workflows while increasing accuracy and visibility across the finance stack.

Ready to Build? Build With AgentFlow

AgentFlow is already active across dozens of mission-critical processes in finance. Its vertical architecture, domain-specific templates, and security-by-design philosophy make it the most practical way to deploy agentic AI that actually works.

Book a demo to see how AgentFlow helps financial teams go from pilot to production in under 90 days and how you can fit it in your existing systems.