AI is transforming the finance sector, especially in financial planning and analysis (FP&A). Using machine learning algorithms is crucial to make FP&A functions more responsive, insightful, and efficient.
Why Should FP&A Leaders Consider to Integrate AI?
FP&A leaders experience significant advantages when they embrace AI. From improved accuracy to gaining a competitive edge.
AI integration in their FP&A function brings various positive outcomes:
AI algorithms boost efficiency by swiftly handling large amounts of financial data, reducing the risk of errors, and enhancing data integrity.
It facilitates advanced analytics and offers valuable insights through methods like predictive modeling and scenario analysis.
AI tools grant real-time access to data, enabling FP&A leaders to retrieve the most current information for agile decision-making.
Top 8 AI Uses in Finance
AI/ML can enhance FP&A operations in many ways. Some typical examples of AI applications are:
1. Error and Fraud Detection
Ensuring the accuracy and reliability of financial data is crucial for FP&A professionals. Nimble machine learning (ML) algorithms play a key role in identifying abnormalities, outliers, and unusual patterns in financial data, preventing oversights during manual checks.
Advanced AI solutions offer real-time analysis during data entry. As financial data is inputted into the system, these solutions monitor and analyze it, serving as a protective barrier against errors entering the workflow. This not only safeguards the integrity of the data but also saves time and resources that would otherwise be needed for costly corrections later on.
A notable example is the Bank of America, which employs AI to maintain the integrity of transactions and combat fraud. By scrutinizing various data points such as payment history and IP addresses, the bank swiftly identifies anomalies. Allegedly, their AI-driven efforts have saved them from potential fraud losses exceeding a billion dollars.
2. Data Aggregation Automation
Ranked as the top feature, involves using AI to gather and combine information for FP&A teams. Instead of manually collecting data from various sources like systems, databases, and spreadsheets, AI does this task automatically. This streamlined process allows FP&A leaders to easily access precise and current data, saving time and maintaining data accuracy.
AI simplifies the process, allowing FP&A leaders to quickly access accurate and current data, saving time and ensuring data reliability.
3. Forecasting and Predictive Analytics
AI uses its analytical capabilities to examine past financial data, market patterns, and macroeconomic signals. It produces reliable predictions and strong predictive models, enabling it to confidently predict a company's financial performance.
Moreover, AI's in-depth analysis reveals patterns and trends that might go unnoticed by humans, offering valuable insights beyond intuition.
Equipped with precise forecasts and AI-driven insights, leaders in financial planning and analysis (FP&A) can:
Base decisions on data
Establish achievable financial objectives
Adapt resource distribution
Assess investment possibilities
4. Analyzing Scenarios and Modeling Sensitivity
AI-driven tools empower leaders in financial planning and analysis (FP&A) to analyze different scenarios and model sensitivity. By entering variables, assumptions, and market conditions, these tools swiftly generate various scenarios, offering insights into how they affect financial performance. This capability aids in assessing strategic options and understanding their financial consequences, facilitating informed decision-making.
5. Financial Performance Analysis and Reporting
AI-driven analytic tools improve the analysis and reporting of financial performance for FP&A leaders. These tools can examine large sets of financial data, pinpoint important key performance indicators (KPIs), and create interactive dashboards and reports.
FP&A leaders can use these insights to track performance, identify trends, and communicate financial results to stakeholders more effectively.
6. Risk and Expenses Management
AI-driven tools for risk management empower FP&A leaders to evaluate and address risks more efficiently. These tools examine factors such as market changes, regulations, and credit risks to pinpoint potential threats to financial performance.
FP&A leaders can then leverage these findings to:
• Create plans to minimize risks
• Test financial models under different scenarios
• Ensure the resilience of their financial strategies
Additionally, AI can enhance expense management by simplifying reporting and analysis. Machine learning models can analyze expense data, recognize patterns, and offer insights to help FP&A leaders enhance their expense management procedures.
By utilizing AI in expense management, FP&A leaders can uncover opportunities to save costs, reduce unnecessary spending, and enhance the overall financial well-being of their organization.
7. Automate Cash Collection
Machine learning models are used to predict when customers will pay their invoices. This helps collection teams take proactive steps to collect payments before they become overdue. These predictions help prioritize accounts that are at risk, making collection efforts more efficient. The forecasted cash collections from these models are used in machine learning-driven cash flow forecasting. This provides important insights for managing overall cash flow effectively.
8. Optimize Capital Allocation
AI algorithms help FP&A leaders make better decisions about where to allocate money. They consider factors like return on investment, risk, cash flow projections, and strategic goals to find the most efficient way to distribute financial resources among projects, business units, or investments. This helps FP&A leaders make informed choices that maximize profits and contribute to financial success.
Addressing Skepticism of AI and Strategies for Improvement
Although AI has made significant progress in FP&A, some companies seem hesitant to start implementing it. According to recent data from Gartner, about 61% of finance leaders and their teams are either in the early planning stages of AI implementation or have no plans for it at all. This puts them behind other departments in adopting AI.
Finance leaders, who are often skeptical, might be taking time to understand how AI tools work and how other departments are using them before integrating them into their own processes.
Marco Steecker, a senior principal in Gartner's finance practice, noted that despite AI's potential, most finance functions have limited AI implementations. He suggests that CFOs collaborate with their finance teams to compare their progress with peers and learn from early adopters on how to speed up AI use.
Gartner identifies four main reasons for the delay in AI implementation in finance: a lack of technical capabilities, low-quality data, insufficient use cases, and other more urgent priorities.
The fast-growing availability of data and easier entry into the field have opened up a big chance for AI to change FP&A. Additionally, PWC recently invested 1 billion dollars in Generative AI. Applying AI in FP&A improves business results and supports smarter decision-making. It's important to use AI alongside human expertise and knowledge in the domain.