5 AI Use Cases in Finance That Are Actually Working (With a Deep Dive on A/R and Monk)

The hype cycle is over. AI isn’t the future of finance—it’s the present, especially in workflows buried in unstructured data, coordination overhead, and timing sensitivity. Nowhere is the value more immediate than in accounts receivable, where platforms like Monk automate collections, cash application, and real-time forecasting. This guide explores 5 high-leverage, real-world use cases where AI is not just adding value—it’s becoming the default.

AI Use Case #1: Accounts Receivable Automation (Monk)

Why A/R?
Because it's where the business gets paid. The cash you earn already exists—it’s trapped in workflows. That makes A/R a P0 cashflow lever.

What goes wrong without AI:

• Invoices are late or inaccurate
• Customer replies go unlogged or unread
• Payment intent is hidden in vague emails
• Disputes stall collections without SLAs
• Stripe/ACH payments aren’t applied correctly
• DSO creeps up, then becomes a permanent liability

How Monk applies AI:

Function

Manual Workflow

Monk AI Workflow

Customer communication

Gmail threads + follow-up spreadsheets

LLM reads replies, extracts PTPs, logs intent, flags risk

Collections emails

Static templates sent by A/R team

AI-personalized sequences based on behavior + invoice age

Payment matching

CSV reconciliation + guesswork

AI matches Stripe, ACH, Plaid payments to correct invoice

Forecasting

DSO-based Excel models

Behavioral forecasting based on live signals

Disputes

Ad hoc back-and-forth with sales/ops

AI-classified, routed, and tracked with SLAs

Quantified impact seen with Monk:

• Up to 90% of collections automated
• DSO reductions of 20–40% in 60–90 days
• Thousands of A/R emails auto-classified weekly
• $1M+ unlocked in working capital for mid-market companies
• Fewer finance headcount needed for same collections velocity

“Every other part of the business gets real-time visibility and automation. A/R was 10 years behind—until we turned on Monk.” – Controller, Series C SaaS

AI Use Case #2: Financial Forecasting + Scenario Planning

Most forecasts are glorified guesses:

• Static models that assume constant DSO, churn, hiring pace
• High sensitivity to manual errors
• No input from actual customer or transaction behavior

How AI improves it:

• Live ingestion from QuickBooks, Stripe, CRM, HRIS, Monk
• Rolling 13-week cashflow models that adapt to events
• Predictive drivers based on seasonality, pipeline risk, cash-in trends
• “What if” scenario planning with embedded GPT agents (e.g., “what if DSO rises 15%?”)

Why this compounds when paired with Monk:
Instead of forecasting based on assumed 45-day DSO, you forecast on real-time customer behavior. That lets finance teams shift from being reactive to predictive.

AI Use Case #3: Contract, PO, and Invoice Parsing

B2B finance lives in documents: MSAs, POs, invoices, amendments, SOWs, remittance emails. Historically:

• Parsing was brittle (regex, OCR)
• Validation required manual checks
• Context got lost in translation across systems

AI improvements:

• LLMs parse PDFs, Excel, HTML emails with embedded terms
• Normalize vendor-specific invoice formats
• Cross-validate terms: “Does invoice net-45 match contract net-30?”
• Detect missing or inconsistent data before invoices go out

Where Monk uses this:

• Reads inbound remittance attachments to match partial payments
• Validates outbound invoices against stored payment terms
• Flags mismatches (PO errors, wrong tax ID, incorrect recipient)

This eliminates “we can’t pay this” as a delay excuse—and tightens cash-in velocity.

AI Use Case #4: Expense Monitoring + Anomaly Detection

Corporate spend is increasingly decentralized (cards, SaaS, contractors), and compliance overhead is massive. Finance teams waste hours/month scanning:

• Duplicate transactions
• Out-of-policy charges
• Fraudulent vendor behavior
• Unusual expense spikes

AI-driven anomaly detection platforms now:

• Analyze historical spend by category, vendor, department
• Auto-classify based on context and metadata
• Flag unusual behavior: “Why did marketing spend 4x usual on Zoom?”
• Provide smart approvals and pre-approvals based on learning

Real-world examples:

• Ramp and Brex embedding GPT-based explainability into T&E spend
• Custom GPTs for tagging GL entries + generating audit notes
• Monthly close times reduced by 40–60% through proactive review

AI Use Case #5: Bank Reconciliation + Cash Matching

Historically a black hole of error-prone work:

• Bank feeds + ERP don’t always align
• Transaction memos are unclear or misapplied
• Partial payments, timing differences, ACH batches create confusion

What AI fixes:

• Reads bank feeds, Stripe/ACH deposits, and maps to open receivables
• Matches fuzzy text memos, partial amounts, and payment windows
• Clusters transactions for mass application (“bulk payment from customer X”)
• Self-improves: the more you reconcile, the faster it gets

Monk does this with collections reconciliation specifically:

• Auto-matches 90–95% of payments to the correct invoice
• Routes the 5–10% edge cases with full suggested matches
• Cuts month-end close time by days, especially in high-volume environments

Common Traits of High-ROI AI Use Cases in Finance

Trait

Why It Matters

High-volume, high-frequency data

Reduces manual load significantly

Semi-structured inputs

AI can normalize PDFs, emails, spreadsheets

Downstream financial impact

Affects cashflow, compliance, audit, or margin

Rule-based + exception logic

AI can handle 80–90% automatically, flag the rest

Unowned workflow

Cross-team friction (Sales ↔ Finance ↔ Ops)

Final Word

Finance was late to AI—but now it’s leading in applied ROI.

The best finance teams in 2025 aren’t building dashboards.
They’re building automation flywheels, starting with A/R.

Monk leads that movement—not by showing you more metrics, but by getting you paid, faster, with less effort and fewer errors.

If your revenue already exists, Monk ensures you collect it.
That’s the simplest, fastest ROI AI can deliver.