How Finance Teams Use AI To Automate Tasks?
AI has moved quickly from buzzword to everyday tool, and finance teams are starting to feel that shift in very real ways. Over the past two years, its adoption has accelerated significantly. According to a recent McKinsey & Company survey of 102 CFOs, 44% reported using generative AI across more than five use cases in 2025, a sharp rise from just 7% the year before. At the same time, 65% of organizations plan to increase their investment in AI, compared to roughly a quarter just two years ago.
Yet despite this momentum, results remain uneven. Nearly two-thirds of companies say they have not successfully scaled AI across their operations, often due to disconnected pilots and poor integration into day-to-day workflows. Still, some finance teams are moving beyond experimentation, using AI to automate routine tasks, speed up reporting, and unlock more forward-looking insights, hence freeing up time for work that actually drives decisions.
What Is Driving AI Adoption in Finance
Finance teams are no longer just reporting numbers. Now, more than ever before, they are being asked to shape decisions. Across organizations, the role of the CFO is shifting from looking backward to guiding what comes next. That means answering questions around pricing, margins, and investments, not just validating historical data. The challenge is that many finance teams still spend a significant portion of their time on manual checks and repetitive processes, leaving little room for strategic thinking.
This is where AI is gaining traction. According to recent industry insights, around 72% of finance organizations are already using AI in some form, signaling that adoption is no longer experimental, but becoming a standard practice.
Several forces are pushing this shift forward:
- Rising Expectations from Leadership: Finance is now expected to provide forward-looking insights that directly influence business strategy, not just report past performance.
- Limited Team Capacity: With growing workloads and lean teams, AI helps automate routine tasks, freeing up time for higher-value analysis.
- Need for Smarter Workflows: Simply adding AI on top of existing processes does not work, which means the teams are rethinking how financial data flows across the organization.
- Competitive Pressure: Companies using AI effectively are gaining faster insights and better decision-making capabilities, creating a clear gap between leaders and laggards.
At its core, AI adoption in finance is not about technology, rather about redefining how finance teams create value.
The Use of AI in Finance (OECD Survey Overview)
Based on survey of 49 responding jurisdictions
| Use Case | Approx. Adoption Level (%) |
| Customer relations | ~50% |
| Process automation | ~48% |
| Fraud detection / prevention | ~38% |
| Risk management | ~27% |
| Data and text analysis | ~27% |
| Anti-money laundering / Counter-terrorism | ~22% |
| Portfolio management | ~20% |
| Cybersecurity | ~15% |
| Customer authentication | ~10% |
| Onboarding | ~8% |
| Supervisory technology | ~8% |
| New product / service creation | ~5% |
Source: OECD (2024) – Artificial Intelligence in Finance
How AI Is Driving Real Efficiency in Finance Operations
The growing adoption of AI in finance is not happening in isolation. It is being driven by clear, practical benefits that directly support how teams operate. As finance functions move toward automation, AI is helping reduce manual workload, improve accuracy, and create space for more strategic work. This shift is central to how finance teams are putting AI to work today, not as a replacement, but as a way to streamline everyday processes.
One of the most visible benefits is the ability to automate repetitive and time-consuming tasks. Finance teams no longer need to spend hours on routine workflows that add little strategic value:
- Automation of Back-Office Tasks: AI is being used to handle invoice processing, data entry, report generation, and compliance checks, significantly reducing manual effort and turnaround time.
- Improved Accuracy and Error Reduction: By analyzing large datasets consistently, AI minimizes human errors and helps detect anomalies or inconsistencies that require attention.
Beyond efficiency, AI is also strengthening how finance teams manage risk and make decisions. Instead of relying solely on historical data, teams can now work with real-time insights:
- Enhanced Risk Management: AI systems can identify patterns related to fraud, credit risk, and market changes, enabling quicker and more proactive responses.
- Faster, Data-Driven Decisions: With predictive analytics and real-time reporting, finance teams can provide forward-looking insights rather than just past performance summaries.
Another important advantage is scalability and cost efficiency, especially for growing organizations:
- Operational Scalability: AI systems can handle increasing transaction volumes without compromising speed or accuracy, making them ideal for scaling businesses.
- Cost Optimization: Automating labor-intensive processes reduces operational costs while allowing teams to focus on higher-value activities.
Overall, these benefits highlight a clear trend: finance teams are using AI not just to improve processes, but to automate the operational noise. This allows them to shift their focus toward strategy, insights, and decision-making, the areas where finance can create the most impact.
How Has AI Been Used in Finance?
AI adoption in finance is no longer theoretical. It is visible across everyday operations, from fraud detection to reporting. What makes this shift important is not just the variety of use cases, but how directly they support automation and efficiency. Finance teams are increasingly using AI to reduce manual workload while improving the quality and speed of decision-making.
Risk Management and Fraud Detection
One of the most established applications of AI in finance is in risk and fraud detection. Companies like PayPal use machine learning models to monitor transactions in real time, automatically flagging suspicious activity.
Similarly, firms like Kensho Technologies leverage large datasets and advanced algorithms to identify patterns and generate insights that support better financial decisions. With access to extensive data through partnerships like S&P Global, these systems can detect risks faster and more accurately than traditional methods.
Credit Scoring and Investment Decisions
AI is also transforming how financial institutions assess risk and allocate capital. In lending and insurance, AI models analyze both traditional and alternative data sources to evaluate creditworthiness and pricing more effectively.
In wealth management, platforms like Wealthfront use AI to create personalized portfolios based on an individual’s risk profile, goals, and preferences. Robo-advisors extend this further by automating portfolio management, allowing users to receive tailored investment strategies without constant human intervention.
Automation in Accounting and Financial Operations
Perhaps the most direct impact of AI can be seen in accounting and finance operations, where automation is reducing manual work at scale.
- Invoice Processing and Accounts Payable: Tools like Tipalti automate the entire invoice lifecycle, from receipt to payment, while also detecting anomalies and fraud risks.
- Financial Reporting and Analysis: AI systems can identify trends, highlight anomalies, and generate reports in real time, helping teams move faster and make better-informed decisions.
- Compliance and Anti-Money Laundering (AML): AI models continuously monitor transactions, flagging suspicious behavior and supporting regulatory compliance efforts.
Budgeting and Workflow Optimization
AI is also being used to improve budgeting and internal workflows. By analyzing spending patterns and financial data, AI can highlight areas for cost savings or investment opportunities.
At the same time, automation is streamlining processes such as purchase order management and approvals, ensuring that financial operations run smoothly without unnecessary delays.
Overall, these applications point to a clear trend: finance teams are not just adopting AI for innovation, but at the same time, they are using it to automate core tasks, reduce friction, and operate more efficiently.
Conclusion
Artificial intelligence is no longer a future-facing concept in finance. It is already reshaping how teams operate on a daily basis. From automating routine tasks to improving accuracy and enabling faster decision-making, AI is helping finance functions move beyond manual processes and into more strategic roles. What once required hours of effort, such as data validation, reporting, compliance checks, can now be completed with far greater speed and consistency.
This shift is not just about efficiency. It is changing the value finance teams bring to the business. By reducing time spent on repetitive tasks, AI allows professionals to focus on forecasting, analysis, and decision support, the domains that directly influence growth and performance. As a result, finance is evolving from a back-office function into a forward-looking partner that actively shapes business outcomes.
The impact is already measurable. According to industry data, organizations using AI in finance can reduce processing costs by up to 82%, while also significantly accelerating reporting cycles and improving accuracy. These gains highlight how automation is not only saving time but also unlocking real financial value.
However, successful adoption requires a thoughtful approach. Finance teams need clear use cases, reliable data, and redesigned workflows to fully benefit from AI. It is not about replacing human judgment, but about supporting it with better tools and insights.
If you are looking to streamline your finance operations and implement AI-driven solutions effectively, Arthur Lawrence can help you design, deploy, and scale the right strategies tailored to your business needs.