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Will AI Replace Finance Jobs? The Future of Finance Professionals

A group of businesspeople discussing artificial intelligence while looking at a digital tablet

New uses for artificial intelligence (AI) emerge daily. From robo-advisors that provide algorithm-driven financial planning to chatbots and digital assistants, AI is changing finance operations at warp speed. The job skills finance professionals need to stay competitive in the marketplace are morphing just as quickly.  

According to a 2023 KPMG survey (70%) of companies expect to roll out AI more broadly over the next 2 years and two-thirds (62%) plan to increase investment over the next year. Ultimately, AI is just a tool. It’s up to you to learn how to use it to your advantage.  

In this blog post, you’ll find out how AI is predicted to change the work lives of financial professionals over the next few years.   

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Traditionally, finance has been transactional and process oriented. Activities that involve repetitive daily tasks have been a natural place to start a digital transformation. With workflow automation, formerly time-intensive activities such as budget approvals, managing employee and customer data, and invoice processing occur efficiently with minimal human intervention.   

According to Shannon Cole, senior director analyst, research in the Gartner Finance practice, “Automation will reshape transaction processing functions such as procure to pay (P2P) and order to cash (O2C). Armies of staff in these processes will be replaced by small teams of specialists focused on process excellence, data governance and application management.”  

The human element in finance’s digital transformation 

Two colleagues walking in office hallway discussing business issues

Finance involves more than calculations, aggregating data and straightforward quantitative analysis. Decoding the meaning of human behavior, managing risk and making strategic decisions are just as important. Financial professionals interpret market trends, assess the impact of world events, and grapple with complex state, federal and international regulations. This requires critical thinking and intuition which AI cannot duplicate. 

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Business thrives on human contact 

Many aspects of finance are relationship based, and trust is an integral part of the equation. AI systems lack emotional intelligence. Finance professionals who have strong interpersonal and communication skills play a crucial role in maintaining a human connection with clients and colleagues.  

Structured data vs. unstructured data 

Network of interconnected, colorful boxes forming the shape of a human brain on a rich purple background. Illustrates the complexity and beauty of neural connections and symbolizes the multifaceted nature of the human brain.

Finance roles will continue to be influenced by the rise of AI and its ability to process massive amounts of structured data in real-time. Structured data is information that fits into data tables and includes data types such as numbers, short text, and dates. Examples of structured data include web form results, SQL databases, Excel files. and employee data.    

However, AI doesn’t do as well with unstructured data because it is not organized in a predefined way – and the finance industry produces a lot of it. Examples of unstructured data include reports, invoices, emails, PDFs, and social media posts. Currently, AI algorithms can’t extract meaning from this data as well as a human being can.   

Monitoring ethical AIThe word algorithm superimposed over the source code of a website about artificial intelligence

Many AI tools use machine learning, a subset of AI, that trains computer systems to accept and process information automatically. It enables AI to learn to recognize patterns and trends and make decisions with little human involvement. AI systems will mirror human biases if they are present in the data supplied to them. Careful review of AI output can verify that the results are accurate and being used ethically. 

Using AI is used to make lending decisions is one example of how ethical issues arise. Systems trained on actual mortgage data influences AI-based decision-making in a way that can put low-income and minority groups at a disadvantage. The difference in the approval rate is not just due to bias but also because minority and low-income groups tend to have less data in their credit histories. To compensate for this, mortgage lenders are working with indicators that usually aren't considered in a typical credit score, such as a borrower’s income level, job opportunities in their field, and earning potential. 

Other considerations, such as transparency, regulatory compliance and privacy, must also be addressed to ensure responsible AI adoption.

When adopting AI your attitude matters 

Businesswoman looking at a tablet in a futuristic office

Corporate finance expert Nicolas Boucher, who spoke at a recent DocuWare webinar, How finance teams boost efficiency with AI and automation, defines three common reactions to the expanding role of AI. People who ignore AI are in the first category. Boucher compares them to those who were reluctant to learn Microsoft Excel and chose to rely on their calculators instead. From his point of view, someone who wants to do their job as they’ve always done it is certainly at risk of being replaced.   

A second type of person uses AI passively. They may copy and paste a question into ChatGPT and use the information it provides as written. They don’t take ownership and instead incorporate AI output into their own work without reviewing it. There’s a significant risk of making a mistake with this half-hearted approach. These finance professionals aren’t adding value by bringing their own point of view to the table.  

Boucher explains that those in the third category who learn how to harness AI’s potential will be sought after. They are aware that everything you input into a computer to use machine learning, or the wording of generative AI prompt should be based on knowledge of the context and your business goals. Then the output must be reviewed carefully. 

Learn new skills to prepare for the AI's next wave 

Multi-collared cyborg head avatar with lines connecting it to the words AI prompt

The rapid increase in AI tools' capabilities has resulted in new job roles. Employers are looking for creative thinkers, great communication skills and a commitment to continuous learning. You don't need to become a programmer or data scientist, but a basic understanding of coding concepts and how databases work is a major advantage. This will soon be as important as advanced Excel skills are today. Getting familiar with frequently used programming languages like Python or R will help you stay ahead of the curve.  

Learning to build highly targeted prompts that help solve business problems is also an essential skill. It requires coming up with questions and instructions that guide AI so that it creates accurate reports, forecasts and data analyses. You may also be called up to decide which AI models and tools will be the best to deal with the issue at hand. Be ready to explain the benefits and limitations that are associated with your choice. 


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A successful digital transformation makes use of the differing strengths of humans and AI. It requires the skills of experienced financial specialists to manage, interpret, and assess the quality of data and reporting it generates. There's plenty of room to leverage your experience, knowledge and insight in tandem with AI advances.