Undoubtedly, any modern finance and accounting teams that have explored ways to improve efficiency and save costs have considered implementing artificial intelligence (AI) into their workflows.
Over recent years, AI adoption has soared within the finance space. According to one report, the value of the global AI in finance market is expected to grow at 34.3% annually from 2025 until 2032.
Whether a business is quick to embrace AI or takes a more methodical approach to its adoption, it’s clear that this technology has the potential to completely overhaul how we approach productivity and efficiency in the finance space.
What is AI in finance?
Artificial intelligence (AI) in finance refers to the practice of using advanced technology like machine learning (ML) and natural language processing (NLP) to augment and streamline traditional financial workflows.
While AI is not a direct replacement for the human experience, there are areas of finance that are ripe for AI transformation, including processes that are repetitive or time-consuming to deliver at scale, such as:
- Invoice processing
- Fraud detection
- Customer support
- Forecasting
- Personalized service delivery
As we’ll cover in further detail below, the main draw of using AI in finance is that it can process large amounts of information much more quickly and with greater accuracy than humans can, leading to efficiency gains and potential cost savings.
Why is AI in finance important?
AI transformation is relevant to both financial services companies and the finance teams of businesses in other industries. The constant expectation to improve financial metrics and profitability has placed increased interest in technologies like AI that promise to deliver better efficiency and potential cost savings.
AI is a broad category of technology, and no two companies will implement it in exactly the same way. It’s not a one-size-fits-all solution, and there is a wide range of tools and platforms available, depending on the desired use. As such, finance leaders can leverage AI as needed to augment their existing workflows and workforce.
Key benefits of AI in finance automation
When implementing AI, finance teams can automate critical yet repetitive processes, improve overall efficiency, and dedicate human resources to more complex tasks.
Here’s a closer look at the benefits of using AI:
Increased efficiency and reduced costs
Possibly the largest benefits of using AI in finance are the potential efficiency gains and cost savings it can help deliver.
AI is especially useful at automating repetitive or resource-intensive tasks, which a human would have otherwise handled.
With labor costs rising, teams can leverage AI to take care of high-volume, menial tasks, while allocating higher-cost human resources to more strategic work that AI systems cannot replicate.
Enhanced accuracy
Another area where AI technology stands out is accuracy. When financial processes are completed by hand, like invoice processing, analysis, or forecasting, there is always room for human error, even among skilled workers.
Through automation, AI systems ensure that workflows are handled with precision every time. There’s no risk of data entry mistakes or miscalculations. And, unlike human workers, the performance and accuracy of AI tools won’t suffer due to fatigue or emotion.
Improved decision-making
AI in accounting and finance can also be used to improve financial decision-making. The use of this technology helps to remove instances of human and emotional bias that influence outcomes while leveraging more data points in the analysis.
More specifically, machine learning platforms excel at pattern recognition and predicting future outcomes. As such, these tools can ingest much more data than a human could and use these findings to help with financial decision-making, like risk or credit assessment, trend analysis, forecasting, and more.
Proactive risk management
This technology can redefine and strengthen risk management practices, helping teams make more informed credit decisions and stronger fraud detection.
As mentioned above, AI tools can bolster decision-making processes by assessing vast amounts of data. They are better at detecting subtle pattern deviations and potential outliers before they evolve into greater threats.
Streamlined regulatory compliance
Certain businesses in the finance space are subject to reporting and monitoring requirements to comply with know your customer (KYC) and anti-money laundering (AML) regulations.
While teams could request, review, and manage the necessary information by hand, it’s much more efficient to use systems that leverage AI and automation for a hands-off approach.
Not only does this save employees time for more valuable tasks, but it also makes it easier for teams to quickly adapt their operations to comply with changing regulatory requirements.
Innovation
While many uses of AI in finance are to streamline or improve existing processes, this technology can also be a key resource for teams to innovate their operations and offerings in completely new ways.
For instance, they might use an AI assistant as a brainstorming partner, helping them conduct deep research into areas they’re unfamiliar with. This may help them identify areas of opportunity or provide the necessary teachings to enter a new market or pursue a new product line for revenue growth.
Common applications of AI in finance
Each finance team will implement AI in unique ways that play to their own line of business, strengths, and areas for improvement. Here are some examples of how teams can use AI:
Accounts payable/receivable
Using AI in accounts payable (AP) or receivable (AR) workflows is becoming increasingly popular. As mentioned throughout, AI is useful at automating repetitive tasks and workflows, which are common in AP/AR.
Traditionally, these functions were largely concerned with issuing invoices to clients and processing incoming invoices from vendors and suppliers. With the help of AI, a human no longer needs to pore over invoice details to match them against the purchase order or supporting documentation.
Instead, dedicated AI systems in AR or AP can streamline this process in a few moments, helping teams categorize transactions, scan invoices and other documents, and route the proper approvals.
Forecasting and planning
Teams may also use AI for better financial planning and budgeting. ML tools can assess historical data, compare it against real-time performance, and generate future forecasts accordingly.
In the past, these activities would need to be done by hand or through static models that may rely on stagnant data or assumptions.
However, AI algorithms are much more dynamic, adjusting with real-time data while considering historical performance and structured and unstructured information for more precise forecasts.
Fraud detection
In practice, artificial intelligence in finance may be used for fraud detection. This might look like a system that monitors bank transactions in real-time to provide alerts or account freezes when fraud is detected.
This compares to banking customers manually reviewing their bank statements at the end of the period, after a fraudulent transaction has already occurred. This can make it more difficult to investigate and rectify the situation than if the transaction had been flagged and prevented in the first place.
Algorithmic trading
Institutional investors and traders can use AI to implement algorithmic trading based on market trends, news sentiment, and other indicators.
These systems can submit trades in fractions of a second, 24/7, which is much quicker than humans can when doing so manually. This disparity in execution speed can mean the difference between investors taking advantage of market opportunities or missing out completely.
Customer service
Financial services companies can also leverage AI technology to streamline internal support operations and improve the customer experience with quicker response times to support queries.
Rather than waiting for a human team member to respond or interacting with a clunky chatbot with just a few trained responses, AI-powered chatbots can deliver a more humanized chat support experience on demand.
In fact, according to a Zendesk survey, 69% of customer experience leaders believe AI will help them humanize digital interactions with their customers.
Challenges of AI in finance
Implementing artificial intelligence in finance operations won’t happen overnight. Despite the clear potential for efficiency and cost-saving improvements, teams should expect to confront certain challenges during implementation, such as:
- Data privacy and security concerns: Business leaders must thoroughly assess the AI tools and platforms they adopt to ensure sensitive financial data will remain secure.
- Regulatory compliance: Teams must continue to adhere to regulatory compliance while implementing AI tools, including data privacy laws.
- Ethical considerations: Enabling AI tools to make critical decisions may present certain ethical challenges, which require some level of human oversight to ensure fair handling and decision-making.
- Integration with existing systems and processes: Depending on the specific tool and intended use, a supported integration may not be available, which can limit potential efficiency gains.
The future of AI in finance
The use of AI in finance has quickly grown over recent years, but this technology is far from reaching maturity.
AI tools and platforms will continue to evolve and grow more tailored for use by financial teams, facilitating more straightforward implementation and adoption into existing workflows.
In the meantime, finance leaders should assess what their team does best, where they’re lacking expertise or resources, and how they can leverage AI to fill in knowledge or resource gaps to remain competitive and improve operational efficiency.
Leverage AI in finance operations with BILL
BILL is already known for helping finance teams automate AP, AR, and expense workflows, and our AI capabilities can drive further growth and innovation through intelligent automation.
BILL’s AI empowers accountants and finance leaders to better manage financial operations through automated auto-categorization of expenses, duplicate invoice detection, invoice data extraction, and more.
Sign up for a free trial of BILL today to get started!
