5 AI Use Cases in Finance That Are Working

September 17, 2025

min read

Research

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 five 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

Why A/R matters: Because it's where the business gets paid. The cash you've earned already exists—it's just 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:

Monk replaces manual collections workflows with intelligent automation. Instead of sifting through Gmail threads and follow-up spreadsheets, LLMs read customer replies, extract payment promises, log intent, and flag risk. AI-personalized email sequences adapt based on customer behavior and invoice age, replacing static templates sent by A/R teams. Payment matching shifts from CSV reconciliation and guesswork to AI-powered matching that connects Stripe, ACH, and Plaid payments to the correct invoices. Forecasting moves from DSO-based Excel models to behavioral predictions based on live customer signals. Disputes get AI-classified, routed, and tracked with SLAs instead of lost in ad hoc back-and-forth with sales and ops.

Quantified impact:

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 the 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, and hiring pace, with high sensitivity to manual errors and no input from actual customer or transaction behavior.

How AI improves it:

Modern AI-powered forecasting platforms ingest live data from QuickBooks, Stripe, CRM, HRIS, and Monk to build rolling 13-week cashflow models that adapt to events in real time. Predictive drivers are based on seasonality, pipeline risk, and cash-in trends rather than historical averages. Embedded GPT agents enable "what if" scenario planning with natural language queries like "what if DSO rises 15%?"

Why this compounds when paired with Monk:

Instead of forecasting based on an assumed 45-day DSO, you forecast on real-time customer behavior. That shift lets finance teams move from reactive to predictive.

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

B2B finance lives in documents: MSAs, POs, invoices, amendments, SOWs, and remittance emails. Historically, parsing was brittle with regex and OCR, validation required manual checks, and context got lost in translation across systems.

AI improvements:

LLMs now parse PDFs, Excel files, and HTML emails with embedded terms, normalize vendor-specific invoice formats, cross-validate terms (like checking if invoice net-45 matches contract net-30), and detect missing or inconsistent data before invoices go out.

Where Monk uses this:

Monk reads inbound remittance attachments to match partial payments, validates outbound invoices against stored payment terms, and flags mismatches like PO errors, wrong tax IDs, or incorrect recipients. 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 across cards, SaaS subscriptions, and contractors, creating massive compliance overhead. Finance teams waste hours each month scanning for duplicate transactions, out-of-policy charges, fraudulent vendor behavior, and unusual expense spikes.

AI-driven anomaly detection platforms now:

These systems analyze historical spend by category, vendor, and department, auto-classify based on context and metadata, flag unusual behavior (like "Why did marketing spend 4x usual on Zoom?"), and provide smart approvals and pre-approvals based on learning.

Real-world examples:

Ramp and Brex are embedding GPT-based explainability into T&E spend. Custom GPTs tag GL entries and generate audit notes. Monthly close times have been reduced by 40–60% through proactive review.

AI Use Case #5: Bank Reconciliation + Cash Matching

Bank reconciliation has historically been a black hole of error-prone work. Bank feeds and ERPs don't always align, transaction memos are unclear or misapplied, and partial payments, timing differences, and ACH batches create confusion.

What AI fixes:

Modern systems read bank feeds, Stripe/ACH deposits, and map them to open receivables. They match fuzzy text memos, partial amounts, and payment windows, cluster transactions for mass application (like "bulk payment from customer X"), and self-improve—the more you reconcile, the faster it gets.

Monk's approach to collections reconciliation:

Monk auto-matches 90–95% of payments to the correct invoice, routes the 5–10% edge cases with full suggested matches, and cuts month-end close time by days, especially in high-volume environments.

Common Traits of High-ROI AI Use Cases in Finance

The most successful AI implementations in finance share these characteristics:

High-volume, high-frequency data reduces manual load significantly. Semi-structured inputs mean AI can normalize PDFs, emails, and spreadsheets. Downstream financial impact affects cashflow, compliance, audit, or margin. Rule-based + exception logic allows AI to handle 80–90% automatically and flag the rest. Unowned workflows with cross-team friction between Sales, Finance, and Ops benefit most from automation.