AI-assisted financial reporting, FP&A analysis, recurring monitoring, commentary, visuals, and workflow automation

Here's the full Claude system I use for finance work.

(Everything a finance team needs to stop wasting hours on manual work)

👉 Comment "Claude" and I'll send you the full set.

I'm limiting this to the first 1,000 comments.

Businesses bring me in to run finance workshops and show their teams how to actually use AI for real finance work.

Most teams I train are using maybe 5% of what Claude can do.

They watch a few tutorials. They paste in random prompts. They get mediocre results and conclude it's overhyped.

No one ever gave them a structured way to use it for the actual job: the reporting, the analysis, the board pack.

So I packaged my best resources into one set: The Claude Finance Toolkit.

Inside, you'll find:

1. The Free Claude Course for Finance
2. Top 50 Claude Tips
3. Top 50 Claude Shortcuts
4. The Claude Finance Playbook
5. Claude for Board Packs & Presentations Guide

Practical guides, prompts, and workflows to save time, sharpen your numbers, and make better decisions.

I've watched owners burn an entire weekend on a board deck that Claude could draft in 30 minutes.

This is the shortcut I wish every finance team had on day one.

👉 Comment "Claude" and I'll send you the full set.

♻️ Repost to help a finance colleague still doing it the hard way.

I built an AI system that replaces your CFO.
Drop a comment and I'll send it to you.
...
I'm kidding but read on and I’ll tell you why.

I’m sure you've seen that post. Probably multiple times this week.

I use AI every single day in my CFO work. My prompts from two months ago look nothing like what I'm using today. The tools are changing fast and the way I use them has to change with it.

Here's what those downloadable systems actually do. They generate reports. They summarize data. They flag obvious variances.

And then they go stale.

There's also a more fundamental problem.

“Crap data in is crap data out.”

If your books are messy and your expenses are miscategorized, your AI CFO system isn't giving you the right insights. It's giving you confident-sounding slightly wrong answers.

These systems work. I use them.

But relying on a static template won't produce consistent results.

Using AI well in financial analysis is an ongoing practice. It requires clean data, good judgment, and someone who keeps evolving it as the tools and the business change.

AI makes a good analyst better. It doesn't make a downloaded template a CFO.

Trintech Flux agent automates account fluctuation analysis during the close; Trintech Variance Analysis agent accelerates budget-to-actual performance review after the close — both operating within governed financial workflows

The more I use AI in finance, the more I find myself thinking less about the technology and more about the people.

A year ago, most of the conversation was about what AI could do.

Could it write commentary?
Could it analyze data?
Could it automate parts of our workflow?

Today, I think the more interesting questions are different.

How do junior professionals develop business judgment if fewer tasks start from a blank page?

How do we make sure people still understand the "why" behind the numbers instead of just reviewing the output?

And what skills become more valuable when everyone has access to the same tools?

I don't have all the answers.

But the more I experiment with AI in my own FP&A work, the more convinced I am that the future of finance isn't really about the technology.

It's about how we adapt around it.

Using AI in accounting feels like working with Einstein — except you have to constantly teach Einstein what accounting is.

A little over 2 months into an AI startup as a Controller, I am having more fun than ever automating accounting workflows so I don’t have to do the work myself. I blew through my session limits 3 days in a row this week (consecutively mind you - 11am to 6pm, 6pm to 11am, rinse and repeat) - but I created financial reporting packs in days that would have taken an entire team weeks to prepare and review.

I no longer want to prepare any workpapers in Excel - that’s my agent’s job now. I am only going to review my agent’s work. Anything in Excel can be automated - no exceptions. In fact, this probably applies to the entire Office suite.

With that said, I am learning that the bottleneck is becoming… me. We have gone full circle - I remember back in my audit days waiting for CaseWear IDEA to finish selecting samples so I can begin testing, or waiting for Blackline to batch process reconciliations so I can start preparing reports. But now, I find myself struggling to keep up with all the different sessions and agents that are running in my Claude. Jumping between sessions to review AI output is harder than expected because of constant iterations. You add jumping in and out of meetings on top and it very quickly becomes absolute ADHD level chaos.

Because the AI works SO FAST, you need to speed up your review frequency as well, and constantly iterate to achieve the desired output. Gone are the leave-a-comment-and-wait-for-your-team review cycles - AI does 10 cycles in 1 minute.

Am I the only one hitting this attention span wall?

Claude’s new financial services release shows how powerful AI in finance is becoming. It also highlights what finance teams still need around it.
The ability to analyze data, work across documents and spreadsheets, draft outputs, and support complex finance workflows is genuinely impressive. For small businesses, solo operators, and lean finance teams, this can already be a major unlock.

But when we speak with finance teams in larger and growing companies, we hear a consistent pattern: they are excited about AI, but the real need is not just better analysis. It is getting finance work done safely, reliably, and with the right controls.

Finance teams tell us that the hard part is often connecting the right context: accounting data, approvals, budget ownership, CRM inputs, tax rules, entity-specific logics, and manager decisions. They also want AI outputs they can inspect, verify, and approve before anything consequential happens.
That distinction matters. AI can prepare, check, summarize, and recommend. But when the next step affects a payment, reimbursement, approval, accounting treatment, audit record, or ERP write-back, companies need more than a smart assistant. They need a governed finance workflow layer.

That is where the next phase of AI in finance will be won: connected data, encoded business logic, permissions, approval flows, audit trails, and human control over consequential actions.
Finance teams should start now. The best place to begin is with workflows where AI can safely prepare the work, while the company builds the operating layer needed to execute with confidence.

The companies that benefit most will be the ones that start learning now. Not by handing over consequential finance decisions on day one, but by putting AI into real workflows, understanding where it creates leverage, and building the controls needed to scale it safely.

How do you think about this? Would love to hear more thoughts

🚀 And check out how Moss can support you on that journey! 🚀

80% of finance teams blame their slow close on waiting for data — even after investing in automation.
(Source: CFO Dive, May 2026)

Meanwhile, teams using AI in their close are cutting cycle time by 40-50% and reconciliation errors by 90%.
I used to be in the 80%. Two things got me out: process redesign + AI in the right places 👇
⸻
🎯 First: Fix WHEN you work, not HOW FAST
Stop treating month-end as a 10-day sprint. Spread reconciliation across the month, bank, AP, AR, intercompany — weekly. By Day 1, you're confirming, not discovering.
The industry calls this Continuous Close. It's the biggest unlock for lean teams.
⸻
🤖 Then: Layer AI where it actually saves hours
Here are 3 things I now let AI handle — with exact examples:
Variance Analysis (2 hrs → 15 min)

I export the P&L to CSV and paste into Claude with this prompt:
"Here's actuals vs budget. Flag every line item with >±10% variance AND >$50K impact. For each, tell me: is this seasonal, one-time, or a trend shift? Draft a 2-sentence explanation."

AI returns a flagged, prioritized list with draft commentary in 30 seconds. I spend 15 minutes reviewing and editing — not 2 hours building it from scratch.
Board Commentary (2 hrs → 20 min)

After variance review, I prompt:
"Based on these 5 flagged variances, draft a 150-word management commentary. Tone: factual, forward-looking. Highlight the 3 biggest movements and their business drivers."

AI writes the first draft. I add the context only I know — market shifts, pipeline changes, operational decisions. 20 minutes total.
Anomaly Detection (manual review → exception-only)

Instead of reviewing every journal entry, I prompt:
"Here are this month's journal entries. Flag anything that looks unusual — duplicate amounts, round-number entries over $10K, entries posted outside business hours, or accounts that don't normally have activity."

AI catches what human eyes miss at 2am on Day 3. I review exceptions only.
⸻
⚡ Two process hacks that cost nothing:
🚫 Hard cut-off, Day -2: Departments submit accruals 2 days before month-end. Miss it? Next month. I put a "Late Submissions" line in the CEO report. Nobody appears twice.
📝 Same-hour commentary: Write variance notes the MOMENT P&L closes — not 48 hours later. Delayed commentary = fiction. Same-hour = insight.
⸻
Results:
📉 Close: 10 days → 4
🤖 Variance work: 2 hrs → 15 min
📝 Board commentary: 2 hrs → 20 min
🎯 Board pack: Day 4 instead of Day 12
⸻
🛠️ Tools worth exploring: → FloQast / Numeric — close task management → HighRadius — AI anomaly detection at scale → ChatGPT / Claude — variance, commentary, anomaly flagging (free)

The secret? AI doesn't fix a broken process. Fix the WHEN first. Then let AI handle the grunt work.
Which part of your close eats the most time? Drop it below 👇

#CFO #MonthEndClose #AIinFinance #FinanceLeadership #StartupCFO #ContinuousClose #FinanceAutomation #CFOInsights

I built my first AI agent.

Her name is Cam. She joined the team one month ago.

Her first assignment? Month end financial reporting.

Like most businesses, we're still heavily reliant on Excel. So I started small. Pulled the manual reporting activities from our month end checklist and taught her one report at a time.

We're almost halfway through the list.

What Cam does now:
She creates the reports. The team reviews the outcome.
10 reports that used to take approx. 3 hours during month end... Cam executes in 3 minutes. Tested multiple times. Consistent results every time.

We'll keep building her skills from here.

What I want Cam to become: Right now, she reports. But the vision is bigger with full analysis and commentary. The story behind the numbers, not just the numbers themselves.

She's not there yet.
But she has a name, a soul, characters, and her purpose. She's building her skills every week.

Share your AI agents in your finance function. Keen to know what did you tackle first?

New AI Virtual Assistant reduces manual work, enhances visibility, and powers smarter finance workflows

ATLANTA--(BUSINESS WIRE)--Apr. 28, 2026-- Corpay, Inc. (NYSE: CPAY), the corporate payments company, today announced a new wave of AI-powered capabilities within Corpay Complete, marking a meaningful step forward in how businesses manage and optimize spend. These innovations bring practical, high-impact AI into everyday financial workflows to help companies move faster, reduce manual work, and make more informed decisions.

Designed for finance and operations teams, the new capabilities apply AI where it matters most. This includes eliminating repetitive tasks, surfacing meaningful insights, and accelerating decision-making across accounts payable and expense management.

Planner Assistant generates anomaly detection and forecasting insights grounded in the customer's own planning models and the Planful Predict engine.

Planner Assistant is built on the Planful Predict engine and the customer's own financial data, which means every forecast and anomaly signal is fully explainable and can be traced back to actual financial data.

Planner is Planful's second major persona-based AI Assistant, following Analyst in October 2025. Together they form a continuous loop. Analyst explains what happened; Planner predicts what comes next.