Artificial Intelligence (AI) in Finance and Accounting: Augmenting and Emphasizing Human Capability

This article is intended to give readers a glimpse of what an AI-driven future may look like for accounting and financial professionals. While the proliferation of AI and the race to find use cases for its effective application rages on, the authors do not believe this a time for worry. On the contrary, we believe this is an opportunity for forward-thinking professionals to use their innate human intelligence and creativity to harness the power of AI to augment their own ability. In doing so, we believe those forward thinkers have the opportunity to, among other things, reduce time spent on mundane tasks to focus on analytics and more value-added business decisions. 

Background

We thought it was fitting to enlist the help of widely available AI Chatbots that leverage large language models to create this article. According to Microsoft Copilot a large language model (LLM) is:

a type of artificial intelligence system designed to understand, generate, and interact using human language. These models are trained on vast amounts of text data and use deep learning—specifically a type of neural network architecture called a transformer—to learn patterns, grammar, facts, reasoning, and even some level of common sense.

Copilot, itself an LLM, acknowledges that LLMs, in essence, simulate intelligence and are not capable of profound understanding like human cognition. LLMs are, however, powerful tools that can perform high-volume tasks and identify complex trends/patterns in large data sets at rates exponentially faster than humans generally can. 

To demonstrate this, we took the following initial steps to develop this article:

1. We submitted the following prompt to both Copilot and Google’s Gemini (also an AI Chatbot): Please write an article with a 1,000-word maximum that describes how AI is being utilized now, as well as what it is projected to be used for in the near future by accounting and finance professionals. Please also include specific commentary about how leasing and equipment finance companies (e.g., banks, captives and independents) use AI currently.
2. We prompted Copilot to compare the responses from Copilot and Gemini. This was done by uploading the content generated by each Copilot and Gemini into Copilot and submitting the following prompt thereto: Please compare the following two articles for me and describe the similarities and differences.
3. Copilot asked if we wanted it to combine the two articles. We told it “Yes” and used the output resulting from that prompt to draft the following article.

Are you able to differentiate between which content in this article has been created by the human authors vs. the content created by those LLMs?

🔁 Automation of Routine Tasks

AI is eliminating time-consuming manual work such as:

• Invoice matching and bank reconciliation.
• Expense reporting and data entry.
• Monitor time-based contractual provisions such as lease renewals and early termination options.

🔍 Fraud Detection & Risk Management

AI systems analyze large volumes of financial data in real time to flag suspicious activities and anomalies.

• Identifies unusual leasing terms or purchase behaviors.
• Enhances risk modeling to support underwriting decisions.

📈 Predictive Analytics & Forecasting

AI and machine learning models forecast:

• Revenue trends and expense patterns.
• Cash flow fluctuations.
• Potential market and economic shifts.

Tools like FP&A (Financial Planning & Analysis) platforms simulate multiple economic scenarios to guide decision-makers with greater accuracy. AI can be leveraged to enhance the efficacy of an organization’s projections to support decision-making and strategy.

🗣️ AI-Driven Reporting and Interpretation

Using natural language processing (NLP), AI can generate narrative financial summaries, turning complex data into digestible insights for stakeholders who may not have a finance background. As an example, NLP was used to help create this article.

🔮 Future Trends: What’s on the Horizon?

🏗️ AI in Leasing & Equipment Finance: Sector-Specific Use Cases

Leasing and equipment finance companies—spanning banks, captives, and independents—may consider adopting AI to modernize credit assessment, risk management, and asset tracking.

📝 Smarter Underwriting

AI-driven platforms evaluate:

• Credit history
• Transaction data
• GPS, IoT, and behavioral analytics

🔧 Predictive Maintenance & Asset Valuation

AI can help:

• Forecast wear and maintenance needs.
• Optimize equipment lifecycle decisions.
• Predict equipment residual values to aid in lease vs. sell decision-making.

💬 Customer Engagement

Chatbots and AI virtual assistants can:

• Offer 24/7 support.
• Help with equipment maintenance related FAQs.

🏦 Banks, Captives & Independents: Examples of How Each Could Benefit

⚠️ Challenges & Considerations

While AI brings immense value, successful implementation depends on careful navigation of:

• Data privacy & governance: Especially critical in regulated sectors
• Model transparency: Decisions must be explainable to auditors and regulators
• Skills gap: Professionals must be upskilled in data interpretation, AI literacy, and strategic thinking

Authors’ Observations

We do not believe that AI will be replacing accountants or finance professionals. However, it is hard to ignore the power of AI and its potential to amplify our human capabilities as exemplified by the content produced above. Copilot and Gemini supplemented our knowledge base to provide a bigger picture of AI in finance and accounting. We believe organizations should gear their AI strategy to focus on implementing AI in a way that removes the need for humans to perform repetitive and voluminous tasks so that they can focus on analysis, applying judgement, developing strategy, decision-making, creativity, client/customer service, and last, but certainly not least, ethics. By the way, in case you were keeping track, roughly 40% of this article has been AI-generated.