The Cambridge Centre for Alternative Finance's 2026 Global AI in Financial Services Report found that 52% of industry respondents have already moved past pilots into active agentic AI adoption, with 23% at the more mature scaling or transforming stages.
The reason is arithmetic, not hype. The average bank or finance team runs thousands of invoices, transaction alerts, and KYC cases through its systems every month, a volume no team can review by hand at the speed money moves today.
This guide is part of Tecla's Agentic AI Use Cases series, covering agentic AI for finance and banking specifically: the AP workflows, fraud patterns, and investment research systems that have moved past the pilot stage.
What Is Agentic AI?
Agentic AI describes goal-driven systems that plan out, carry out, and adjust multi-step tasks with little day-to-day human input, a different animal from traditional automation or generative AI built to answer one prompt at a time.
Three things define it: autonomy, deciding on and carrying out actions; adaptability, adjusting the plan as new information comes in; and coordination, working across tools and systems to finish what no single tool could handle alone.
In finance, that difference shows up in accounts payable. A rules-based system flags an invoice that doesn't match a purchase order. An agentic system checks vendor history and exception patterns, then either resolves the mismatch or hands an approver a fully investigated exception instead of a raw flag.
How Agentic Systems Work in Finance
Finance has run on automation for decades too, first through mainframe batch rules, then through RPA bots that click through the same screens a person would, just faster.
RPA automates a predefined, hand-coded sequence: if this field matches that field, do this. It's fast and auditable, but it breaks the moment an invoice format changes or a transaction falls outside the bot's script, and every new pattern means an engineer rewriting the workflow.
Agentic AI operates differently: it reasons instead of just executing a script. It reads the invoice, alert, or filing, decides what context matters, and adjusts its handling as it goes, the way a skilled analyst would.
The trade-off is real and worth naming directly: RPA is more predictable and cheaper to audit; agentic systems are more adaptive and more expensive to run per case.
Most production finance deployments in 2026 are not one or the other.
They are structured workflows (matching, routing) for the repeatable 80% of cases, with agentic reasoning reserved for triage and investigation, and a human gate in front of anything that moves money, changes a credit decision, or files a regulatory report.
Perceive invoice, alert, txn
›
Retrieve ERP, core banking
›
Reason correlate, score
↓
Human gate
›
Act post, transfer, file
›
Verify
Verify feeds back into Perceive, a continuous feedback loop, for the next cycle
Agentic AI Use Cases in Finance and Banking
Finance teams don't adopt agentic AI as one system. They adopt it function by function, starting wherever the volume problem is worst.
Nine of those starting points are documented below, each at working depth. The architecture above stays abstract until it's tied to an actual trigger and an actual system.
Accounts Payable and Invoice Processing
Manual invoice processing runs $12 to $30 per invoice, against $2 to $5 for automated handling, per 2026 accounts payable benchmarking data. Estimates of the error rate vary by methodology, cited anywhere from 15-20% up to 39% of manually processed invoices, but every source agrees automated handling cuts it well below 1%.
The workflow
What it does: classifies every incoming invoice, pulls the context an AP clerk would otherwise have to hunt for across the purchase order and vendor systems, and opens a case with a recommended resolution instead of a raw queue item.
1
Invoice ingested from email, portal, or EDI feed and data extracted
2
Matched against purchase order, receipt, and vendor record
3
GL coding applied and duplicate or fraud checks run
4
Exceptions routed to the specific resolver: pricing, procurement, or compliance
5
Human gate: approver signs off on anything above the pre-set threshold
The stack: an AP automation platform (Vic.ai, Tipalti, or Bill.com) for document intelligence and matching, integrated with the ERP of record (SAP, Oracle, or NetSuite), with evals scoring touchless-processing accuracy against finance-confirmed exceptions weekly.
Why it works: the volume problem is real, and invoice matching is pattern-matching at scale, exactly what routing does well.
Production concern: exception creep. An agent tuned to approve too liberally just moves fraud risk downstream instead of removing it.
Fraud Detection and Transaction Monitoring
Global fraud losses reached an estimated $442 billion in 2025, according to the Global Anti-Scam Alliance's Global State of Scams report, cited in INTERPOL's 2026 Global Financial Fraud Threat Assessment. INTERPOL rates the overall risk as high, driven partly by agentic AI tools that can now plan and run entire fraud schemes autonomously.
The workflow
What it does: monitors transactions and account sessions in real time for fraud patterns, scoring risk and either clearing, challenging, or blocking activity within a written policy.
1
Transaction or session event scored in real time against a behavioral baseline
2
Device, location, and network signals retrieved for context
3
Signals combined into a single risk score against known fraud typologies
4
Action taken within policy: cleared, step-up challenge, or blocked
5
Case logged with reasoning attached for the fraud team's review
The stack: a fraud detection engine (Feedzai, NICE Actimize, or FICO Falcon), identity and device signal sources, a real-time scoring API embedded in the payment or transaction pipeline.
Why it works: speed matters more than nuance here, and blocking a transaction is reversible, which supports moving fast without a live human gate.
Production concern: false positive cost. Blocking a legitimate transaction erodes customer trust immediately, so the threshold gets tuned against a labeled backlog first.
AML and KYC Alert Triage
US financial institutions filed 12,870 Suspicious Activity Reports a day on average in fiscal year 2024, according to FinCEN's own Year in Review data. That's the volume this workflow exists to absorb.
The workflow
What it does: builds a complete case around a flagged customer or transaction, cross-referencing sanctions, adverse media, and prior alerts, and clears low-risk cases automatically within a written policy.
1
Transaction or customer profile flagged against AML typologies and watchlists
2
Case built automatically: prior alerts, adverse media, transaction history pulled
3
Risk scored against the institution's written AML policy
4
Low-risk cases cleared automatically within a pre-approved threshold
5
Human gate: SAR or CTR filing decision reviewed and filed by a compliance officer
The stack: an AML/transaction monitoring platform (NICE Actimize, ComplyAdvantage, or Hawk AI), sanctions and adverse media data feeds, a case management layer generating an audit-ready evidence bundle for every disposition.
Why it works: the response actions are bounded by a written AML policy, so real-time autonomy on low-risk cases is safe because a human already approved the boundaries in advance.
Production concern: alert quality depends entirely on the underlying data. Stale sanctions or adverse media feeds produce a confidently wrong case file.
Credit Risk and Loan Underwriting Triage
Upstart's own published data shows its model approves 44.28% more borrowers than a traditional credit model at 36% lower APRs, per Upstart's own reporting. Zest AI reports its models now support over 600 active deployments across its lender base, per Zest AI's own site.
The workflow
What it does: pulls income, asset, and bureau data for a loan application, verifies supporting documents, and auto-decisions within a policy band instead of leaving every file to a manual underwriter.
1
Application ingested with income, asset, and credit bureau data
2
Documents verified: pay stubs, bank statements, tax forms cross-checked
3
Alternative data incorporated where the lending policy allows it
4
Risk scored and auto-decisioning applied within the approved policy band
5
Adverse action reasons generated for any decline
6
Human gate: loan officer reviews anything outside the auto-decision band
The stack: a credit decisioning platform (Zest AI, Upstart, or Scienaptic AI), a document and income verification layer (Ocrolus), integrated with the loan origination system (MeridianLink or Temenos).
Why it works: a scoring and ranking task with a defensible reason code attached, one of the more mature places for agentic underwriting because the output has to survive a fair-lending examiner's questions anyway.
Production concern: model drift and disparate impact. A model that performed well at launch can quietly start approving or declining differently across borrower groups, which is why outcomes analysis and benchmarking stay in force regardless of how the model was built.
Investment Research and Market Intelligence
Bloomberg's April 2026 announcement of its ASKB agentic AI roadmap describes an engine built specifically to link every research takeaway back to its source data. It's a direct response to how much time analysts lose triaging filings, transcripts, and market data by hand.
The workflow
What it does: pulls filings, transcripts, and market data across sources, extracts the bull and bear case, and synthesizes a briefing with citations instead of a raw document pile.
1
Query or trigger received: earnings, filing, or scheduled thesis check
2
Filings, transcripts, and market data retrieved across sources
3
Bull and bear arguments extracted and cross-referenced
4
Findings synthesized into a briefing with inline citations
5
Analyst notes and house views layered in for context
6
Human gate: delivered to the analyst or PM for validation before use
The stack: a market data platform (Bloomberg, FactSet, or LSEG) as the primary source, an agentic research synthesis layer (Hebbia or AlphaSense) with citation tracking, retrieval over research notes and SEC filings.
Why it works: a research task with a verifiable citation trail, the shape that makes retrieval-heavy agentic systems work well, and exactly why Bloomberg built ASKB around linking every takeaway back to its source.
Production concern: signal-to-noise ratio. Synthesizing every technically relevant document just moves the judgment call from reading time to review time.
Reconciliation and Financial Close
BlackLine's April 14, 2026 announcement reports its Verity Prepare agent cutting reconciliation creation time by more than 90% for early adopters, with Verity Match reaching 80-90% match rates on complex reconciliations.
The workflow
What it does: matches sub-ledger, bank, and GL data continuously rather than only at month-end, classifying exceptions and routing them to the resolver who owns that account.
1
Sub-ledger, bank statement, and GL data ingested continuously
2
Transactions matched using learned patterns, not just fixed rules
3
Exceptions classified and routed to the right resolver
4
Human gate: variance above threshold escalated for investigation
5
Journal entries and close status logged for the controller's review
The stack: a financial close and reconciliation platform (BlackLine or Trintech), connected to the ERP of record (SAP, Oracle, NetSuite, or Workday) through bi-directional data flow.
Why it works: it correlates three data sources that usually sit in three different systems, closing exactly the kind of gap a manual month-end close leaves open until it's too late to fix quietly.
Production concern: data quality upstream. A reconciliation agent is only as good as the sub-ledger and bank feed it reads, and a broken feed produces a confidently wrong match rate.
Regulatory Reporting and Compliance Monitoring
McKinsey estimates agentic approaches can deliver productivity improvements of 200 to 2,000% compared to assisted AI tools in its research on banks and financial crime, because an agent owns the full case rather than a single step within it.
The workflow
What it does: tracks regulatory change across jurisdictions, maps impact against the institution's own obligations, and drafts filings from core banking and case management data instead of a manual compilation.
1
Regulatory change or reporting trigger detected across tracked jurisdictions
2
Impact mapped against the institution's obligations inventory
3
Report or filing drafted from core banking and case management data
4
Draft checked against the applicable regulatory template and policy
5
Human gate: compliance officer reviews and files before submission
The stack: a regulatory intelligence platform (Ascent RegTech or Norm Ai) for the obligations register, a transaction monitoring source feeding case data, a governance layer enforcing policy checks before any filing executes.
Why it works: high-volume, template-driven work where the same reasoning applies at the millionth filing as the first, exactly the profile that survives an examiner's question about consistency.
Production concern: regulatory scope itself is moving. SR 26-2 replaced SR 11-7 in April 2026 and explicitly places generative and agentic AI outside its formal scope, which means the agent doing the filing still needs a governance framework the regulation hasn't written yet.
Agent Identity and Authentication for Financial Systems
SailPoint's own Q1 fiscal 2027 disclosure found non-human identities accounted for 40% of identity growth and 14% of all identities managed on its platform, per reporting on the company's earnings. That growth curve now touches core banking, payments, and trading systems.
The workflow
What it does: verifies and governs AI agents as first-class non-human identities, since a valid credential plus authorized access no longer guarantees a safe outcome once the requester is an autonomous agent touching money movement.
1
Agent registered as a distinct identity with an owner and purpose
2
Request routed through an identity gateway before touching core banking or trading systems
3
Agent's permission for the specific action verified in real time
4
Human gate: action allowed, blocked, or escalated based on policy
5
Full action trail logged, distinguishing agent from human activity
The stack: an agent identity platform (SailPoint's Agentic Fabric, Microsoft Entra Agent ID, or Okta), layered onto the existing core banking IAM system.
Why it works: enforcement happens at the action level, not just at login, the one control traditional identity systems were never built to provide for a system that reasons and acts on its own.
Production concern: privilege drift. Agents left ungoverned tend to accumulate access to core banking and trading systems faster than anyone reviews it, exactly the pattern SailPoint's own growth numbers point to.
Model Risk Validation and Stress Testing
The Federal Reserve, OCC, and FDIC replaced SR 11-7 with SR 26-2 on April 17, 2026, the first overhaul of model risk guidance in fifteen years. It explicitly places generative and agentic AI outside its formal scope, while stating that existing risk principles still apply to the models those agents touch.
The workflow
What it does: runs backtesting and stress scenarios against a credit, capital, or trading model autonomously within a pre-approved test plan, then compiles findings with benchmarking evidence instead of a raw output log.
1
Model scope and validation plan defined under a risk-based approach
2
Backtesting run against historical outcomes and outlier analysis
3
Stress scenarios and adversarial inputs run within the approved test plan
4
Findings compiled with benchmarking against alternative methods
5
Human gate: independent validator signs off before the model returns to production
The stack: a model risk management platform (SAS Model Risk Management or a comparable validation suite), backtesting run against the institution's own historical loss and performance data.
Why it works: the testing itself is systematic work suited to speed, while the sign-off before a model returns to production stays with an independent validator, the same separation of duties SR 26-2 still requires.
Production concern: scope ambiguity. SR 26-2 excludes agentic AI models from its formal scope, so a bank still has to decide, on its own, how rigorously to apply the same validation discipline to the agent running the test.
Implementation: Guardrails and Governance
Finance teams are moving faster than governance can keep up with. McKinsey's 2026 AI Trust Maturity Survey found that only about 30% of organizations report a maturity level of three or higher in strategy, governance, and agentic AI controls specifically, even as deployments accelerate.
The Cambridge Centre for Alternative Finance report cited earlier found that only 14% of financial services firms currently see AI as transformational to their organizational strategy, a real gap between activity and integration.
That gap is closed with the same guardrail layers that apply to any agentic system, made specific to finance.
| Layer | What it does | Finance-specific example |
|---|
| System prompt | Sets the non-negotiables up front | "Never file a regulatory report or move funds outside the pre-approved list" |
| Input filters | Block or sanitize out-of-scope requests | Treat instructions embedded in invoice PDFs or wire memos as untrusted content |
| Tool-call gatekeepers | Cap what actions an agent can take | Step-up authentication allowed; account closure always needs a human |
| Output checks | Scan before the action executes | Block any action touching an account or entity outside the agent's scope |
| Human-in-the-loop | Requires approval for high-impact actions | A compliance officer approves before a SAR is filed or a large transfer clears |
One risk is specific to this stack and worth naming directly: the documents an agent reads are also an attack surface.
A malicious actor can embed instructions inside an invoice PDF or a payment memo field, the same class of prompt injection documented across other agentic use cases. Treat every uploaded document and every tool integration as part of the attack surface alongside the model itself.
The Team Behind Production Agentic AI
Agentic finance systems rarely fail for lack of tools. They fail for lack of people who have actually shipped one.
Tecla's Agentic AI services design, build, and operate these workflows directly, the same AP, fraud, and compliance systems above, running in your stack with the evals and guardrails production requires.
Or bring the expertise in-house: AI engineers who've worked on live financial systems, past the demo stage.
Tecla runs a network of senior engineers across the US and Latin America, built over more than a decade, with a top 3% acceptance rate and first candidates in 3 to 5 business days.
No company adopts all of this at once. Most teams start with whichever workflow addresses the most pressing problem.
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FAQ
What is an example of agentic AI in finance?
A concrete example is an accounts payable agent: it ingests an invoice, matches it against a purchase order and vendor history, resolves routine mismatches, and escalates only the exceptions that need a human decision, instead of routing every invoice to a manual review queue.
How is agentic AI different from RPA?
RPA automates a predefined, hand-coded sequence that breaks on anything it was not built for. Agentic AI reasons over context, adapts the workflow as it goes, and only needs a rewrite when the underlying policy changes, not every time an invoice format or a transaction pattern does something new.
Can agentic AI replace a financial analyst or underwriter?
No. Production deployments use agents to absorb triage volume and document review, while analysts and underwriters supervise outcomes, handle ambiguous cases, and provide the human sign-off any consequential action still requires.
How does agentic AI apply to investment research and credit underwriting?
The same shape applies. Agentic AI for investment research synthesizes filings, transcripts, and market data into a cited briefing instead of a raw document pile. Agentic AI for credit underwriting reuses the scoring approach used in fraud detection, ranking applications against a written policy so a human reviews the ambiguous cases instead of every case.
What are the risks of agentic AI in financial operations?
Prompt injection through the documents an agent reads, overprivileged access to core banking or ERP systems, and unclear accountability. All three are architecture problems: input filters, scoped permissions, and gates that require human sign-off address each directly, though maintaining them takes ongoing review as regulatory guidance evolves.
How do finance teams start with agentic AI?
Start with the highest-volume, lowest-risk workflow: invoice and accounts payable processing, tied directly to manual data entry. It has a reversible action and a clear cost-per-invoice baseline to measure against, before expanding into fraud, credit, and compliance.
