How generative AI trained in finance and accounting gives you a strategic edge 

How can you get generative AI to deliver the deep, actionable insights you need to unlock real strategic value when managing your finances? 

Built upon finance-specialised large language models (LLMs), AI-powered productivity assistants like Sage Copilot are designed to address accounting, compliance, and strategic growth complexities. 

While traditional AI focuses on automating tasks, finance LLMs go further—transforming data into accessible, actionable insights that encourage confident, strategic decision-making. 

What is an LLM—the heart of generative AI? 

LLMs are at the heart of generative AI. These learning “brains” are trained on massive datasets to understand and respond in human-like language. LLMs can be trained to specialise in and recognise industry patterns, norms, and outliers. 

While traditional LLMs cover broad topics, finance-specific LLMs are designed to understand the complex language of finance, accounting, regulatory standards, and compliance needs, offering precise, actionable insights. 

This article will explore how your finance team might benefit from generative AI today and how finance-tailored LLMs can genuinely change how you work. 

Contents

The role of AI in finance: Traditional versus generative 

Artificial intelligence (AI) often serves as a catch-all umbrella term, so it’s helpful to understand how traditional AI and generative AI play unique roles in finance.  

• Traditional AI has long been a cornerstone, focusing on automating repetitive tasks, analysing historical data, and boosting data-driven decision-making. While it crunches data faster than ever, it can fail to deliver the contextual, accessible insights that finance professionals need to make strategic decisions. 

• Generative AI goes beyond these capabilities. It uses advanced models to create new content, predict scenarios, and deliver dynamic, real-time insights from what it learns about the data, its use, and patterns in the wider environment—bringing a transformative, strategic edge to your fingertips. 

Traditional AI in finance: Automating and streamlining processes 

Traditional AI automates repetitive tasks, reduces risk, and enhances efficiency by focusing on data-driven decisions.  

• Applications include fraud detection, risk assessment, and regulatory compliance monitoring, freeing your finance team to concentrate on higher-value work. 

Generative AI in finance: Driving strategic innovation and growth 

Generative AI provides a further strategic edge by creating real-time, adaptive insights with the ability to carry out scenario planning. 

• Applications include dynamic forecasting, scenario simulations, and personalised customer engagement, assisting your finance team in making proactive, informed decisions at pace. 

Why AI can speed up your financial workflows 

It’s together that traditional and generative AI can provide a comprehensive approach to finance: 

• Enhanced decision-making: generative AI forecasts combined with historical analysis from traditional AI give you a fuller picture. 

• Operational continuity and innovation: traditional AI maintains core processes, while generative AI encourages new growth avenues and adaptability. 

• Greater confidence in strategy: with combined AI-driven insights, you can make more confident, informed decisions that align with immediate and long-term goals. 

AI can be powerful in monthly financial close processes 

Traditional AI handles task automation and error flagging. At the same time, generative AI acts as a strategic advisor, generating forecasts, suggesting budget adjustments, and simulating “what-if” scenarios to address your company’s future.  

This operational efficiency and strategic foresight can help you drive smarter, faster growth.  

How generative AI is already boosting efficiency  

Your finance team may already use non-specific generative AI tools like ChatGPT and Microsoft Copilot to streamline complex tasks, create summaries, automate repetitive processes, and retrieve information, improving daily workflows. 

Angus Gregory, CEO of Biomni, highlights how generative AI can enhance financial analysis by quickly processing massive datasets to identify patterns and predict outcomes.  

He explains that AI can assess financial markets, generate digestible document summaries with the proper prompts and information, and even support brainstorming on growth and cost-saving strategies. 

Daniele Grassi, CEO of Axyon AI, notes generative AI’s capacity to identify patterns invisible to human analysis. “AI has a superior ability to identify non-linear patterns in data, such as fundamentals, technical indicators, and macroeconomics.”  

Paul Ronan, CTO of FE fundinfo, emphasises its role in simplifying compliance-heavy processes. “AI can understand people’s needs and direct them to the right resources,” he shared, noting its value in managing complex financial and compliance information.  

“AI acts as a super assistant, simplifying lengthy documents by highlighting key information, which is particularly useful in compliance.” 

Limitations of general-use generative AI in finance 

General-purpose generative AI tools, while powerful, have fundamental limitations when applied in high-stakes finance: 

1. Security and data privacy 

General AI models may compromise sensitive financial data without enterprise-grade protections, posing security and compliance risks. 

2. Reliability and accuracy 

Due to their generalised nature, general models can lack the precision required for critical financial decisions, potentially leading to errors. 

3. Contextual understanding 

Standard generative AI may not satisfactorily interpret finance-specific terminology, regulatory requirements, and nuanced processes without industry-specific knowledge. This can result in incomplete or incorrect responses, particularly in complex financial scenarios. 

Because they aren’t appropriately trained with your confidential financial data, general-use AI models may misunderstand the context and produce inaccurate results, especially in areas needing detailed regulatory and economic awareness relevant to your business. 

Financial professionals need confidence in the technology 

With any new technology, there is a period of adjustment, testing, and learning. With general AI tools, this takes much longer. Choosing a system specific to your industry will reduce this process’s pain. 

Paul Ronan says: “ChatGPT can hallucinate and make mistakes, which is concerning, especially in the financial sector where incorrect advice could have serious consequences. 

“As we gain confidence in AI, it could provide quick pivots on views and support advice for various fields like finance, tax, and compliance. With a controlled narrative and clearer communication, AI can be a powerful tool for assisting professionals.”  

“To have confidence in AI’s domain expertise in areas such as finance, we must move towards sparse expertise models that are easier and cheaper to train.  

“These models will be specialised, allowing for mass adoption and increased confidence in the quality of their responses.  

“The focus should be on creating expert systems specifically trained for industries like finance, pharmaceuticals, or others, rather than general models that are ‘experts’ in every field.” 

Why finance-specific LLMs matter 

So, when it comes to finance, general-purpose AI models often fail to address its specific needs. They may need more precision, regulatory understanding, and context for critical decision-making.  

Finance-specific LLMs can shine in this area, as they are tailored exclusively for accounting and finance professionals.  

5 benefits of finance domain-specific LLMs 

Here are five key advantages of domain-specific LLMs for finance professionals: 

1. Contextual Accuracy 

Unlike general-purpose AI tools, a finance domain-specific LLM can be trained to understand financial terminology, standards, and processes. This ensures outputs align with industry expectations, reducing the risk of errors or irrelevant insights often produced by generic models. 

2. Enhanced Privacy and Compliance 

Handling sensitive financial data demands rigorous privacy standards. Unlike general models that may not prioritise these needs, a finance domain-specific model should be designed with enterprise-grade security protocols to keep data secure and adhere to compliance requirements. 

3. Efficiency and Scalability 

Generic AI tools often become expensive at scale due to pay-per-use pricing. Purpose-built models for finance integrate seamlessly into workflows, offering predictable cost structures and scalable solutions tailored to your business. 

4. Reliable Insights for Critical Tasks 

A finance-specific LLM can deliver actionable insights for tasks like forecasting cash flow, conducting real-time regulatory checks, and automating complex workflows. These models eliminate the need for manual intervention while improving decision-making accuracy. 

5. Regulatory Expertise and Customisation 

An LLM can ensure compliance with evolving laws and standards if built on finance-focused datasets. They are tailored to financial workflows, making them uniquely equipped to provide advice that professionals can trust, unlike generic models that may need more nuance to navigate regulatory complexities. 

Comparison: Finance-specific vs. general AI models 

Feature	General AI Models	Finance-specific LLMs
Contextual Accuracy	Limited understanding of finance	Deep knowledge of financial terminology and processes
Regulatory Compliance	Generic security measures	Built-in compliance with industry standards
Data Privacy	Potential third-party data usage	Rigorous, self-hosted privacy protocols
Cost Efficiency	Pay-per-use, unpredictable pricing	Scalable, predictable costs
Reliability	Variable responses	Stable, consistent outputs tailored to finance

Real-world applications: Driving efficiency and strategic focus 

With finance-specific LLMs, you could experience significant time savings and strategic benefits. Imagine asking the AI questions like: 

• “What’s our forecasted cash flow for next quarter?” 

• “Which product lines are driving the most profit this year?” 

Instead of navigating spreadsheets or analysing lengthy reports, you want AI to provide immediate, accurate responses. Additionally, it’ll be beneficial for: 

• Generating detailed financial reports 

• Conducting regulatory-compliant risk assessments 

• Supporting data-driven decision-making with trusted insights 

These capabilities free finance teams from administrative tasks, enabling them to focus on driving growth and innovation. 

Transforming finance with accessible insights 

Finance-specific LLM could deliver the reliability, precision, and security that finance professionals need to excel.  

These tailored solutions would go beyond providing an AI tool—they can act as a trusted partner who understands your unique challenges and aligns with your goals.  

Sage Copilot, built on finance-domain expertise, exemplifies this approach.  

It combines advanced financial language models with a focus on compliance, accurate forecasting, and streamlined workflows, empowering you to prioritise strategic decision-making while simplifying complex processes. 

As Srijith Rajamohan, Staff AI Research Scientist at Sage, says, “Trust is paramount to us. In the world of accounting, our customers trust us with their critical financial information and expect us to provide correct and precise information.  

“Therefore, we operate under the guiding principle that a product that provides no response is better than a product that provides an incorrect response.” 

Sage Copilot can be your strategic partner  

Built on a Sage finance-specific LLM, Sage Copilot is designed to: 

• Simplify complexity: Streamline accounting and financial processes with precision. 

• Speed Up workflows: Deliver actionable insights instantly, reducing time spent on manual tasks. 

• Empower decision-making: Surface insights grounded in financial expertise so that you can make confident and informed choices. 

Sage Copilot can integrate seamlessly into your workflow, delivering accessible, actionable insights that enable your teams to prioritise growth and innovation. 

• Powered by AWS: supported by AWS infrastructure, Sage’s domain-specific LLM processes and analyses vast datasets efficiently, simplifying complex accounting tasks. 

• Deep financial expertise: unlike general-purpose AI, Sage Copilot is trained to understand the nuances of accounting and finance, delivering precise and reliable insights that professionals can trust. 

• Customer-focused Design: Sage partners play a pivotal role in refining Copilot, ensuring it meets real-world business needs.  

Dan Miller, EVP of Financials and ERP at Sage, emphasises this collaborative approach: 
“Our partners help customers select solutions and make business process changes that make customers more effective. They are key recommenders and will help businesses unlock the full potential of Sage Copilot.” 

Customer feedback is crucial 

Sage Intacct customers, typically growing businesses, face the dual challenge of navigating complex financial landscapes while maximising efficiency. 

Sage Copilot is being refined in real-world scenarios through direct engagement with them to address their needs. 

Dan Miller explains that customer engagement is critical to effectively shaping Sage Copilot and Intacct to support professional needs.  

He says: “Since day one, we’ve been gathering customer insights in real-time. This has helped us to adjust and improve Sage Copilot as we go, ensuring that we are creating a tool that delivers real value to our customers,”  
 
Dan was clear that it’s still early days for Sage Copilot in Sage Intacct. “This is just the beginning,” he explained.  

“We’re learning a lot through this process, and the feedback we’re gathering from customers will shape how we improve and evolve Sage Copilot. It’s critical to have this real-time feedback as we refine the tool to deliver the most value.” 

Final thoughts 

Finance-specific LLMs can reshape how finance teams work, offering the precision, reliability, and security professionals need.  

These tools go beyond traditional AI by aligning with your industry-specific challenges, empowering you to focus on strategic goals. 

By building on finance-specific large language models, tools like Sage Copilot can address some of the most complex challenges in accounting and compliance while opening the door to more confident, data-driven strategies. 

However, only with real-world feedback and industry expertise can AI move beyond generalised capabilities to meet the specific needs of finance professionals.  

For those curious about the intersection of AI and finance, explore Sage Ai more or learn about Sage Copilot’s specific capabilities. 

Editor’s note: This article was originally published in May 2023 and has been updated for relevance.