What are the practical / real / pragmatic use cases of AI/GenAI/Machine learning in your finance department? e.g., in areas like transactional accounting, Controllership, financial reporting, financial planning & Analysis, AR/AP, Treasury, tax, etc.
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Founder in Miscellaneous5 months ago
I'd think about AI in these ways:1. Cognition
2. Creation
3. Knowledge
4. Data & Automation
1. Cognition
Because Generative AI is trying to simulate a human brain, it can be useful when augmenting our own brains (not replacing them).
- Can’t decide whether to hire that FP&A analyst?
- Struggling to think of ways to de-risk a big investment?
- Mental block trying to simplify that credit control process?
There’s a lot to be said for using AI as a sounding board to reduce some of that mental fatigue.
Think about the tasks you do that take a lot of brain power, and seek AI for advice.
2. Creation
Put simply, ‘Generative’ AI means it can create stuff. Yes, it’s limited by it’s training data, but either way, there’s a reason so many are using Gen AI to produce content, imagery, and now videos.
It’s not limited to ‘creatives’ though. Finance have plenty of opportunities to get creative.
- Need a polite way of telling that supplier your not paying them?
- Roadblock producing commentary on your management accounts?
- Can’t think of impactful themes for that next high-stakes presentation?
With AI, you never need to start from scratch again.
Find creative tasks where AI might help you take you work to the next level
Try this prompt:
“Here’s an example of my last management report commentary. And here’s this month’s data. Can you help me create a new report using the latest data?”
IMPORTANT - Only give data using a secure platform (ChatGPT Team & Enterprise editions, Claude, Microsoft Copilot etc)
3. Knowledge
Knowledge is power, and there’s nothing more powerful than having your own instantly available personal instructor.
Gen AI is trained on a HUGE knowledge base, so use it!
- Moving into a role in a new industry?
- Need a refresher on how to use Dynamic Arrays in Excel?
- Wanting to keep up to date with the latest compliance standards?
With AI you can advance your skills in every subject area.
As part of your day to day, look everywhere for opportunities to sharpen your skills.
Use Copilot (formerly Bing Chat) and Perplexity if you’re looking to power up your web search.
3. Data & Automation
Once you've got into a rhythm working with AI as part of your day to day, you can then take it to the next level by using it to automate tasks, and analyse data.
- Ask AI for VBA code to automate data manipulation in spreadsheets
- Ask AI for Python code you can run in Google Colab to produce visualisations
- Ask AI using the Copilot for Finance plug-in in Excel to help with financial reconciliations
From a data point of view, AI doesn't yet have the context window to analyse large amounts of data.
But you can ask AI to help you use tools and generate code to take your data analysis to the next level.
Hopefully this helps.
Director, Engineering Finance2 months ago
I discussed these top 3 use cases of AI in Finance that we have already implemented in a recent AI podcast. 1. Risk Management and Fraud Detection: AI algorithms have helped us detect anomalies in financial transactions, such as fraudulent activities or unusual patterns/risks and therefore have prevented fraud to happen by enhancing security.
2. Transparency and Compliance: AI have helped us maintain compliance with regulations by automating processes.
3. Automated Operations and Cost Reduction: AI streamlines routine tasks, such as document processing, automation of PO generation, Invoice generation, real time reporting notifications (above a certain threshold), calculation of tax projections etc. by automating several processes.
VP of IT in Retail2 months ago
Controllership - Anomaly Detection
Machine learning algorithms can learn patterns from historical data and detect anomalies in financial transactions, which can be indicators of fraud or errors.
Financial Planning & Analysis - Predictive Analysis
Machine learning can be used to predict future revenues, costs, and other financial metrics based on historical data, economic indicators, and other relevant factors.
Accounts Receivable/Accounts Payable (AR/AP) - Invoice Processing (i.e. OpenText?)
AI can automate the processing of invoices in accounts payable and accounts receivable, including matching invoices to purchase orders, coding invoices, and even making payments.
Treasury - Cash Flow Forecasting
Machine learning can enhance cash flow forecasting by analyzing patterns in historical data and predicting future cash inflows and outflows.
1- Using built in AI within the systems we have. So we have done a proof of concept using the inbuilt AI forecasting within our planning & forecasting tool that we are now looking to pilot.
2- On the GenAI side, at this stage, it’s about productivity and efficiency benefits for our finance teams. So real examples we are using with our internally developed tool are writing meeting minutes, document translation, email/communication drafting, ideation. We are starting to explore with our GenAI tool connecting in data sources so we can start to look at things like writing financial commentaries, and looking at potential for document summarisation/analysis for things like M&A data rooms.