Technology & Innovation

Not just ChatGPT. How to take advantage of the AI finance revolution

Discover how AI is revolutionising finance beyond just ChatGPT. Learn strategies and techniques to use artificial intelligence in financial decision making, trading, risk management, and more.

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Artificial Intelligence (AI) isn’t just the future of finance—it’s the present, actively reshaping how financial professionals work today. 

From AI security features in smartphones enabling digital payments to robo-advisors managing investments, the impact of AI is pervasive, reshaping the world of finance as we know it. 

Integrating AI into your financial operations may seem daunting, and you may feel that change is happening so fast you’re not getting a second to breathe. 

But it’s important to understand that technology software trailblazers are already deploying AI to predict and manage financial risks, progress data-driven insight, and enhance compliance and regulatory reporting efficiency. 

AI offers an unprecedented opportunity for sophisticated automation in the finance sector.  

Furthermore, AI’s ability to analyse vast datasets can empower you to make more accurate predictions and informed decisions, delivering a competitive edge. 

In this article, we will cover:

What is AI? 

Artificial Intelligence (AI) is a rapidly evolving technology transforming the business and finance landscape. 

AI is an umbrella term covering many problem-solving methods, including machine learning and natural language processing. 

At its core, AI refers to the ability of machines to learn from data and perform tasks that typically require human intelligence, such as recognising patterns, making predictions, and automating processes. 

Once a realm of speculative fiction, AI has now emerged as a ground-breaking tool for financial innovation as an increasing number of firms invest in exploring its capabilities. 

How can AI help your finance department? 

Selecting the right AI technology requires careful consideration of your specific needs and goals in accounting and financial management.  

Here are some areas to look at: 

Automation and machine learning 

Machine learning is a type of AI that allows machines to learn from data and improve their performance over time.  

Let’s go through some powerful benefits of automating routine tasks and processes. 

Improving financial workflows 

Automation allows you to complete financial tasks much faster than if you did them manually. 

Financial management software could let you automate your financial workflows, such as approvals, notifications, and the routing of documents.  

Based on predefined rules, you can automatically configure approval workflows for purchase orders or expense reports to route to the appropriate stakeholders. This automation ensures timely processing, reduces bottlenecks, and improves collaboration. 

You can also take the pain out of data entry by integrating financial management software with other systems, such as banking platforms or CRM software, to automatically capture and import financial data. This eliminates the need for manual input, reducing errors and saving time.  

Look for software that automates transaction processing in invoice creation, payment processing, and expense management, reducing manual effort and accelerating your entire financial workflow. 

Financial reconciliation 

AI-powered accounting software has revolutionised financial management by automating transaction categorisation and reconciliation, which can save hours of manual work. 

With AI, accounting software can recognise patterns in financial data, such as recurring expenses and income, and accurately categorise each transaction. 

It can also help reconcile business transactions by automatically matching bank statements with accounting records, identifying discrepancies, and highlighting errors or fraud.  

Eliminating manual errors 

Manual data entry and repetitive tasks are prone to human error, significantly affecting financial accuracy.  

Naturally, automation reduces your reliance on manual labor, minimising the risk of data entry mistakes, miscalculations, and other types of human error (think copy and paste). 

AI-driven automation ensures consistent and reliable data processing, improving financial management accuracy. 

Faster decision making with real-time insight and predictive analytics 

AI-powered algorithms can process vast amounts of data within seconds, allowing for quick analysis, rapid report generation, and fast decision-making—you can achieve real-time insights and respond swiftly to market or industry regulation changes. 

You can analyse historical data and identify patterns and trends that may be difficult for humans to detect, helping you make more informed decisions about future investments and resource allocation. 

Areas you’ll want to investigate might include: 

  • Cash flow forecasting 
  • Revenue forecasting 
  • Invoice payment predictions 
  • Expense analysis 

AI can also help you build a narrative around financial data, particularly if you create interactive visualisations that can help you easily identify trends and patterns at a glance. 

Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.  

In financial management, you can use predictive analytics to forecast market trends and identify potential risks (which we will discuss further). 

Automated reporting 

Look for software that automates the generation of financial reports and statements, eliminating the need for the manual compilation and reconciliation of data.  

With built-in reporting templates and customisable dashboards, it will be very useful to automate the generation and distribution of financial reports on a scheduled basis, particularly for key stakeholders. 

Resource optimization 

Automating routine tasks will free up valuable time for your team, allowing them to focus on more strategic and value-added activities.  

You can optimise the use of your skilled workforce by redirecting human resources from mundane tasks to critical analysis, strategic planning, and decision-making.  

This allocation of resources drives efficiency and generates greater value for your finance function. 

Scalability and consistency 

As businesses grow, the volume and complexity of financial data increase. Manual processes can become overwhelming, leading to delays, inconsistencies, and inefficiencies. 

Automation provides scalability, allowing finance teams to handle larger data sets without compromising accuracy or speed.  

Automated processes ensure data handling, analysis, and reporting consistency, regardless of the data volume or frequency. 

Cost reduction 

Automation can help you reduce operational costs associated with manual labor, such as data entry, report generation, and reconciliation. Automating these tasks can cut the need for additional personnel or outsourced services, leading to cost savings over time.  

Additionally, automation reduces the risk of financial errors or non-compliance, which can result in financial penalties or operational losses. 

Natural language processing (NLP) 

Natural language processing (NLP) is a type of AI that allows machines to understand and interpret human language.  

NLP can help software algorithms extract key information from invoices, receipts, and statements, reducing errors and saving time. By analysing transaction descriptions, NLP supports automated bookkeeping by assigning expense categories automatically and accurately.  

Look at NLP to summarise complex financial information, assist in decision-making, and help you follow accounting regulations, streamline processes, and enhance efficiency. 

Risk management 

You can use AI to manage risk, as it can accurately identify potential problems, suggest mitigation strategies, and help you make better decisions through real-time insights and predictive analysis based on large datasets. 

Here are some examples: 

Predicting future customer behaviour 

AI can analyse customer-related data, such as purchase history, online behavior, and social media interactions. This analysis can identify patterns and predict future behaviours, such as potential churn or likelihood to purchase. As a result, you can act proactively to retain customers or stimulate sales, thereby reducing the risk of lost revenue. 

Assessing credit risk 

AI can forecast the likelihood of default by examining various factors, such as a client’s payment history, debt levels, and financial stability. This could help lenders make more informed decisions about who to lend to and at what interest rates. It helps protect assets and allows fairer, more accurate assessments. 

Operational risk management 

In supply chain management, AI can predict potential disruptions based on data related to weather patterns, political climate, and even supplier reliability. You can put contingency plans in place well in advance, reducing the risk of delays or losses. 

An example of how financial software can detect fraud with AI 

AI-powered algorithms can analyse vast amounts of data and identify patterns indicative of fraudulent behaviour, such as unusual spending patterns or suspicious account activity. 

Let’s look at an example. 

Imagine your finance team has a crooked employee with access to accounts payable and accounts receivable functions. They create a fake vendor account and submit invoices for payment to this made-up company.  

The employee then approves the payment for the fake invoices, which results in funds being transferred to a personal bank account. 

Here are 3 ways AI could help uncover this fraud: 

  1. Analysing patterns and anomalies 

If you have financial management software trained to identify anomalies in transaction data, it could analyse patterns and detect deviations from established norms.  

It could, for example, identify transactions significantly higher or lower than typical payment amounts or transactions made outside of normal business hours. 

  1. NLP 

AI algorithms can analyse text data such as vendor descriptions, invoice details, and payment notes.  

You could identify irregularities in the data, such as non-existent vendors or mismatched details between vendor invoices and payment details. 

  1. Predictive Analytics 

You can use AI-powered predictive analytics to identify high-risk transactions and prioritise them for further investigation.  

How to unlock the power of generative AI 

Generative AI uses deep learning algorithms to generate new content, such as images, text, and audio.  

It works by learning patterns and features from existing data and then using this knowledge to generate new content similar in style and structure.  

Breakthroughs like OpenAI’s ChatGPT means generative AI is a staple topic in boardrooms across industries.  

The expansive capabilities ChatGPT possesses (such as simulating complex business scenarios to offering insightful suggestions for strategic planning) have transformed generative AI from a technical curiosity to a business necessity.  

Because of the wave of attention, you may be discussing with directors and executives how to make AI an integral part of your business model and growth strategy. 

ChatGPT, for instance, can revolutionise customer service, human resources, and internal communications, driving efficiency and cost reductions.  

Similarly, generative AI’s potential in forecasting, strategic planning, and data analysis has positioned it as a crucial business tool. 

David Dickson, Vice President at Sage AI, says: “To put this into perspective, imagine how generative AI can enhance your everyday life.  

“For example, when prioritising tasks on your to-do list, you can rely on ChatGPT to provide recommendations and even delegate certain tasks.  

“Engineers can explore coding tools generated by AI, while marketers can leverage AI-generated content. The opportunities to harness this technology for personal and professional use are immense.” 

However, it’s important to note that while generative AI is impressive, it’s crucial to approach it cautiously.  

David says: “Generative models can convincingly generate content, including falsehoods or hallucinations. Therefore, we should treat them as valuable tools but be aware of their limitations.  

“With the AI team at Sage, we ensure that we establish trust, simplicity, and a human touch in our solutions.” 

At the Sage Partner Summit, a remarkable proof-of-concept demonstration showed the immense capabilities of generative AI within the realm of that accountant’s favourite—Excel.  

The demo showed an AI assistant seamlessly integrated into the Excel interface, allowing users to effortlessly engage in inquiries, retrieve data, and even generate emails.  

With generative AI accessible in such an everyday tool, you could see how it might elevate your overall experience, offering a seamless fusion of language and software.  

How to take advantage of AI in your finance department

Starting an AI initiative in your finance department may seem daunting, but breaking it down into manageable steps could make the process easier.  

  1. Identify your need and set goals  

Understand the challenges in your finance department AI can help with. Identify areas where AI can provide the most value. It’s a matter of deciding what’s most important and cost-effective to you.  

Whatever you choose for your AI initiative, always define clear, measurable goals. 

  1. Research and learn 

Familiarise yourself with basic AI principles and use cases in finance. You don’t have to become an AI expert, but rather understand what AI can and can’t do and how it might benefit your department.  

You might want to take a course, hire a consultant, or attend industry conferences and seminars. 

  1. Evaluate resources  

Before starting an AI initiative, evaluate your systems and resources. 

Do you have the necessary data infrastructure for AI to work? It is important to assess the quality and completeness of your data, as AI relies on this high-quality data to provide accurate insights and predictions. 

Do your employees have the skills to use AI tools, or would they require training? If you lack the necessary resources, you might need to consider hiring new talent, training current employees, or outsourcing to a vendor.  

We’ll go into more detail about this further on. 

  1. Select the right tools and partners 

Based on your needs and resources, choose the appropriate AI tools. This could involve buying off-the-shelf software and customised AI solutions or partnering with a technology firm. 

Evaluate potential vendors or partners on their technology and understanding of your industry. Look at their track record and ability to provide ongoing support and training. 

  1. Implement and monitor 

Implement your chosen AI solution, beginning with a pilot project if possible. You can test on a small scale, fix any issues, and demonstrate value before rolling it out more widely.  

After implementation, continuously monitor the performance of the AI solution against your defined goals and adjust as necessary. 

Remember that implementing AI is not a 1-time project but a journey. It requires continuous learning, adaptation, and improvement.  

Also, understand that implementing AI isn’t just a technology project—it’s a business project that requires involvement and buy-in from people across your organisation. 

How software companies are integrating AI into finance products 

According to Partnering for Success: State of the IT Channel Ecosystem, the demand for AI and automated services is surging, signalling a significant shift in how you operate and innovate.   

Surveying 1,700 IT and tech resellers globally, almost half (47%) said that AI and automated services are a revenue stream of most interest to customers today—and anticipate this will remain the case for 18 months.  

Like many other cutting-edge tech companies, Sage integrates AI capabilities into its flagship products (like Sage Intacct). 

David Dickson says: “You can either be paralysed by fear and miss out on the opportunity, or take a leap and harness the power of AI technology.  

“By embracing AI, we open ourselves to new possibilities and previously unimaginable perspectives. 

David says AI will transform the rapid exchange and understanding of accounting and financial information.  

He says: “The classic ‘data-in, analysis, data-out’ cycle will be much easier and faster for the accounting and finance team, which will gain a clearer picture into the business’s health.” 

David oversees the AI division of Sage, with a mission of transforming the company through composable AI, focusing on automating and streamlining workflows in finance and operations.  

Using AI technologies, we want to free up time so our customers and finance professionals can harness their strategic thinking and elevate their work. The target is to automate processes as much as possible and revolutionise how you approach financial tasks.  

Through ongoing research and development, innovative AI solutions could enhance the efficiency and effectiveness of financial professionals, helping them unlock new levels of productivity and success. 

David says: “Our strategic objectives for the AI division revolve around scaling our existing AI capabilities, integrating them into more of our products and making them available to our partners.  

“We also aim to rapidly deliver transformative AI experiences, leveraging the momentum of generative AI.  

“Finally, we envision an AI-enabled workflow across the Sage network, empowering the future vision of Sage and its interconnectedness.” 

David says that his AI team is working in 4 main areas: 

  1. Document extraction 

Extracting essential information from financial documents, such as invoices, eliminates the need for manual data entry into accounting software. This streamlines processes and saves valuable time.  

  1. Transaction classification and matching 

Accurate categorisation and matching of financial transactions allow automation of transaction coding that can enhance efficiency and accuracy in financial workflows. Finance professionals can precisely track and report financial data, reducing manual effort and improving overall data integrity. 

  1. Trust 

Maintaining data integrity that ensures the reliability of AI-driven solutions is crucial. By instilling confidence in the accuracy and consistency of financial processes, financial professionals can rely on AI-derived insights and outputs with assurance, leading to better decision-making. 

  1. AI experiences  

Delivering personalized and intuitive interfaces can optimize accounting workflows and automate routine processes to make working more user-friendly. 

Sage Intacct automation in action 

In pursuit of streamlining financial processes, David says that Sage has already successfully integrated powerful AI capabilities into products like Sage Intacct.  

David says: “With options to upload financial documents manually or through email, Sage Intacct automatically processes and codes them, eliminating tedious manual data entry.  

“Additionally, our AI team has developed transaction classification and matching, coding financial transactions against nominal accounts and GL codes.” 

Currently, finance teams still waste a lot of time on manual processes. Research shows that Account Payable clerks spend 30% of their time on data entry and 20% on classification.  

“By automating both extraction and classification, we significantly reduce time, enhancing efficiency and accuracy. This scalability aligns perfectly with our business objectives.” 

Skill up your people on AI 

As the finance industry increasingly relies on AI, your team might want to prepare for the future.  

By taking advantage of training opportunities and staying up to date on the latest trends and developments in AI, you can position yourself for success and career progression in the future. 

Here is some guidance on the skills and training opportunities available and where you could see financial career progression in an AI-driven world. 

Skills needed 

To succeed in an AI-driven industry, you might want to look at developing the following skills: 

Data analysis 

As AI generates large amounts of data, learning to analyse and interpret this data will help you make more informed strategic decisions. 

AI and machine learning 

Understanding AI and machine learning, including how these technologies work, their applications in finance, and their limitations, will become an in-demand skill. 


As AI generates insights, improving how to communicate these insights to stakeholders, including senior management, investors, and customers, will drive successful relationship management. 

Training opportunities 

To develop the skills needed for an AI-driven finance team, take advantage of the following training opportunities: 

Online courses 

Many online courses are available on AI, machine learning, and data analysis, including ones offered by Coursera, edX, and Udemy. 

Professional certifications 

Professional certifications can give you the skills and credentials to succeed in an AI-driven industry. 

Industry conferences:  

Industry conferences can help you learn about the latest trends and developments in AI. 

Career progression 

As the finance industry becomes more reliant on AI, career progression opportunities will shift towards roles requiring data analysis, AI, and machine learning skills.  

Here are some potential roles you might see in your AI finance team of the future. 

Data scientist 

Data scientists are responsible for analysing and interpreting large amounts of data to generate insights and inform strategic decision-making. 

AI developer 

AI developers are responsible for designing and developing AI systems, including machine learning algorithms and natural language processing systems. 

Business analyst 

Business analysts could be responsible for analysing business operations and identifying opportunities for improvement using data analysis and AI-generated insights. 

Final thoughts on financial AI 

The ChatGPT effect is undeniable, and AI’s revolution in the finance industry is well underway. AI is revolutionising the work of financial professionals in numerous ways. 

Embracing AI can open new avenues for finance professionals to contribute strategically, using the power of AI technology to drive innovation and create value for their organisations. 

From automation and machine learning to natural language processing, AI can transform your finance department’s operations. You can automate mundane tasks, generate accurate reports, and gain valuable insights, ultimately saving time and resources. 

One particularly powerful aspect of AI is generative AI, which has the potential to unlock new levels of creativity and efficiency.  

To fully take advantage of AI in your finance department, embracing AI integration in financial products is essential. Software companies continuously develop innovative solutions that incorporate AI, empowering finance professionals with advanced tools and insights. 

However, investing in your people is crucial to harness AI’s benefits. Providing training opportunities for your employees on AI-related skills will help them adapt to the changing landscape and drive career progression.  

To stay ahead of the curve, embrace AI, integrate it into your finance processes, and invest in developing the necessary skills within your team. 

Here are some points to take away: 

  • Educate yourself and your team on AI technologies and their applications in finance. 
  • Collaborate with software companies that offer AI-integrated finance products to enhance your department’s capabilities. 
  • Embrace generative AI to automate tasks, generate insights, and improve efficiency. 
  • Invest in training opportunities to upskill your workforce in AI-related skills. 
  • Foster a culture of innovation and continuous learning to adapt to the evolving AI landscape. 

By taking these steps, you can position your finance department at the forefront of the AI revolution, driving growth, efficiency, and success in the ever-changing financial landscape.