What is AI in Finance? Applications, Examples, and Benefits in 2024

The Finance function is crucial for strategic planning and achieving organizational goals. However, with the explosion of data, Finance teams need to uncover valuable insights from this vast information. AI in Finance has become a game-changer, providing professionals with tools to streamline processes, detect patterns, and extract meaningful insights. This blog introduces Artificial Intelligence in Finance and previews OneStream's innovative Sensible ML solution.

What is AI in Finance?

AI in Finance is the application of artificial intelligence technologies to enhance and automate financial processes and services. This includes using machine learning algorithms, natural language processing, and data analytics to analyze large volumes of financial data, improve decision-making, and increase operational efficiency.

AI is employed in various areas, such as fraud detection, algorithmic trading, risk management, credit scoring, and customer service. By leveraging AI, financial institutions can gain deeper insights, streamline operations, and deliver more personalized and accurate financial solutions.

How is AI Used in Finance?

Efficiency Through Automation

The automation of repetitive and time-consuming tasks allows Finance professionals to focus on more strategic activities. For instance, finance AI-powered tools can automate planning, data entry and routine financial reporting – freeing up valuable time for analysis and decision-making.

Machine Learning for Predictive Analytics

AI enables Finance teams to move beyond historical reporting and embrace machine learning (ML)-backed predictive analytics. By analyzing historical data patterns, AI algorithms can more accurately forecast future trends. Those trends then help organizations make informed financial decisions. For example, AI can analyze customer behavior, purchase history and market trends to predict the ideal price point for each product or service. This personalized approach maximizes both revenue and customer satisfaction, paving the way for sustainable growth.

Learn more about machine learning in finance

Exception Handling and Anomaly Detection for Streamlined Processes

AI's ability to process vast amounts of data allows for the identification of patterns and anomalous data points. This ability facilitates exception handling for Finance processes such as planning, data quality and reporting. Thus, Finance professionals can streamline operations and focus on exceptions, rather than sifting through entire datasets. The approach ultimately saves valuable time and enhances the efficiency of financial processes.

10 Use Cases of AI Applications in Finance

AI applications in Enterprise Performance Management (EPM) and Corporate Performance Management (CPM) offer several use cases for CFOs. The following 10 use cases illustrate how AI can significantly enhance the efficiency, accuracy, and strategic value of EPM/CPM processes.

1. Predictive Analytics for Financial Forecasting

AI can enhance financial forecasting by analyzing historical data and identifying patterns to predict future financial performance more accurately. This helps CFOs make informed budgeting, cash flow management, and resource allocation decisions.

2. Automated Financial Close and Reporting

AI-driven automation can streamline the financial close process, reducing manual tasks, minimizing errors, and accelerating the preparation of financial statements. This improves efficiency and ensures timely reporting, critical for regulatory compliance and decision-making.

3. Anomaly Detection in Financial Data

AI can automatically detect anomalies or irregularities in financial transactions, helping CFOs quickly identify potential fraud, errors, or inefficiencies. This enhances financial controls and reduces the risk of financial losses.

4. Scenario Planning and Simulation

AI can run multiple "what-if" scenarios, allowing CFOs to assess the financial impact of various strategic decisions. This supports better long-term planning and risk management.

5. Enhanced Budgeting and Planning

AI can optimize the budgeting process by analyzing various data inputs, such as market trends, economic indicators, and operational data, to create more accurate and flexible budgets that adapt to changing business conditions.

6. Cost Optimization and Spend Analysis

AI tools can analyze spending patterns and suggest cost-saving opportunities, helping CFOs optimize expenditures and improve profitability. This is particularly useful for identifying non-essential spending or inefficiencies in procurement.

7. AI-Driven Compliance Monitoring

AI can continuously monitor financial transactions and processes to ensure compliance with regulatory requirements. This reduces the burden on the finance team and helps prevent costly compliance breaches.

8. Risk Management

AI can assess and quantify financial risks by analyzing large datasets and external factors like market volatility or geopolitical events. This allows CFOs to proactively manage risks and protect the organization's financial health.

9. Personalized Financial Insights

AI can provide CFOs with personalized dashboards and insights, highlighting key financial metrics and trends most relevant to their strategic goals. This enables more focused and data-driven decision-making.

10. Improved Data Quality and Integration

AI can help cleanse, normalize, and integrate data from disparate sources, ensuring that the financial data used for decision-making is accurate, up-to-date, and consistent across the organization.

6 Benefits of AI in Finance

Financial Artificial Intelligence offers a range of benefits that enhance operational efficiency and decision-making. Here are some of the advantages:

1. Automation

AI streamlines and automates routine and repetitive tasks such as transaction processing, data entry, and compliance checks. By leveraging AI algorithms and robotic process automation (RPA), financial institutions can reduce the need for manual intervention, minimize human errors, and free up valuable time for employees to focus on more strategic tasks. Automation also helps in handling large volumes of transactions and data seamlessly, improving overall operational efficiency.

2. Accuracy

AI enhances the accuracy of financial operations by reducing human errors and ensuring consistent data processing. Machine learning models and advanced algorithms analyze data with high precision, leading to more reliable financial forecasts, accurate risk assessments, and error-free transaction processing. This heightened accuracy helps in maintaining data integrity, complying with regulatory requirements, and making informed financial decisions.

3. Efficiency

AI significantly boosts operational efficiency by optimizing processes and resource allocation. For instance, AI-driven analytics can quickly process and analyze vast amounts of financial data, providing actionable insights in a fraction of the time it would take manually. Additionally, AI tools can streamline workflows, improve resource utilization, and reduce operational costs, leading to a more efficient and productive financial environment.

4. Speed

AI accelerates financial operations by processing data and performing tasks at speeds far beyond human capability. Real-time data analysis, rapid transaction processing, and instant decision-making are made possible through AI technologies. This increased speed not only enhances customer experiences by reducing wait times but also enables financial institutions to respond swiftly to market changes and emerging opportunities.

5. Availability

AI systems operate around the clock, providing continuous availability for tasks such as customer support, transaction monitoring, and data analysis. AI-driven chatbots and virtual assistants can handle customer inquiries and process transactions 24/7, ensuring that services are always accessible. This constant availability improves customer satisfaction, supports global operations across different time zones, and ensures that critical financial functions are performed without interruption.

6. Innovation

AI fosters innovation in the financial sector by enabling the development of new products, services, and business models. Through advanced data analysis, predictive modeling, and personalized recommendations, AI can uncover new opportunities and drive creative solutions. For example, AI can facilitate the creation of innovative financial products, enhance investment strategies, and enable personalized customer experiences, positioning financial institutions at the forefront of industry advancements.
What Are the Risks of Artificial Intelligence in Finance?

AI in finance offers numerous benefits but presents risks, highlighting the importance of a balanced approach to AI adoption. Here are some of the key risks to consider:

• Data Privacy and Security Risks: AI systems rely heavily on vast amounts of data, including sensitive financial information. The risk of data breaches, cyberattacks, or unauthorized access is a significant concern. CFOs must ensure robust data protection measures to safeguard financial data and comply with regulations like GDPR or CCPA.
• Bias and Fairness Issues: AI algorithms can inadvertently perpetuate biases present in the training data, leading to unfair outcomes in financial decisions such as loan approvals or credit scoring. This means CFOs must be vigilant about the ethical implications of AI models and ensure that AI-driven financial decisions are transparent, fair, and free from bias.
• Regulatory Compliance Risks: The use of AI in financial services is subject to stringent regulations. The lack of transparency in AI decision-making processes (often called the "black box" problem) can make demonstrating compliance with financial regulations difficult. CFOs must work closely with legal and compliance teams to navigate the evolving regulatory landscape and ensure that AI implementations do not expose the company to legal risks.
• Operational Risks: AI systems can fail or produce incorrect outputs due to technical errors, faulty algorithms, or unforeseen circumstances. This can disrupt financial operations, lead to incorrect financial reporting, or result in significant financial losses. CFOS must implement robust risk management strategies to mitigate operational risks associated with AI.
• Dependence on AI and Skills Gaps: Over-reliance on AI systems can reduce human oversight and the erosion of critical financial skills within the organization. CFOs must therefore balance adopting AI with developing human expertise and ensure that staff are trained to work effectively alongside AI technologies.
• Strategic Risks: AI can alter the competitive landscape of financial services. If competitors adopt AI faster or more effectively, it could lead to strategic disadvantages. Any CFO must ensure their organizations are not only adopting AI but doing so in a way that aligns with long-term strategic goals and maintains a competitive edge.
• Cost Risks: Implementing and maintaining AI systems can be expensive. There is also the risk that AI investments may not yield the expected return on investment (ROI) if the technology does not perform as anticipated or if the company fails to integrate it effectively into its processes. This means a CFO must carefully assess the cost-benefit ratio of AI initiatives.

What is the Future of AI in Finance?

The future of AI and Finance is poised to bring transformative changes, driving innovation and reshaping the industry in several significant ways:

1. Enhanced Predictive Analytics

AI will increasingly improve predictive analytics by leveraging advanced machine learning models to forecast market trends, investment opportunities, and economic conditions with greater accuracy. This will enable financial institutions to make more informed decisions and strategize effectively in a rapidly changing environment.

2. Advanced Fraud Detection and Prevention

AI will enhance fraud detection and prevention capabilities by using sophisticated algorithms to analyze transaction patterns in real-time and identify potential fraudulent activities with greater precision. This will reduce financial losses and improve security measures.

3. Personalized Financial Services

The use of AI will drive more personalized financial services, including customized investment advice, tailored financial products, and individualized customer experiences. AI will analyze vast amounts of personal and behavioral data to provide recommendations that align with individual goals and preferences.

4. Automation of Routine Tasks

AI will automate an increasing number of routine and repetitive tasks, such as data entry, compliance checks, and reporting. This will streamline operations, reduce operational costs, and free up human resources for more strategic and value-added activities.

5. Evolution of Robo-Advisors

Robo-advisors will become more sophisticated, utilizing advanced AI algorithms to offer comprehensive financial planning, investment management, and portfolio optimization. These platforms will provide high-quality advisory services at a lower cost, democratizing access to investment expertise.

6. Enhanced Risk Management

AI will improve risk management by analyzing complex data sets to identify and quantify various types of risk, including credit risk, market risk, and operational risk. This will lead to more accurate risk assessments and better risk mitigation strategies.

7. Integration with Blockchain

AI will integrate with blockchain technology to enhance transparency, security, and efficiency in financial transactions. This combination will facilitate more secure smart contracts, better compliance tracking, and more efficient transaction processing.

8. Improved Customer Engagement

AI-driven chatbots and virtual assistants will become more advanced, offering more natural and effective interactions with customers. These tools will provide instant support, handle complex queries, and enhance overall customer satisfaction.

9. AI-Driven Innovations in Trading

AI will continue to revolutionize trading by developing more sophisticated trading algorithms and strategies. High-frequency trading, sentiment analysis, and market prediction will benefit from AI's ability to process and analyze large volumes of data quickly.

10. Ethical and Regulatory Advancements

Focus on developing ethical AI frameworks and adhering to evolving regulatory standards. Efforts will be made to ensure transparency, fairness, and accountability in AI applications, addressing concerns related to bias and data privacy.

11. Enhanced Financial Forecasting

AI will refine financial forecasting methods, enabling more accurate predictions of economic trends, market movements, and company performance. This will aid in strategic planning and investment decision-making.

12. Growth in AI-Driven Financial Products

The development of new AI-driven financial products and services will continue to expand, offering innovative solutions for investment, insurance, lending, and financial management. These products will cater to diverse needs and preferences, driving market growth.

AI in Finance: Introducing Sensible ML

As the Office of Finance continues to embrace AI, adopting a practical sensible approach to ML – one that balances automation with transparency and human insight – has become increasingly important. After all, effective planning is critical for businesses to remain competitive and adapt to changing market conditions.

At OneStream, we call this Sensible ML.

OneStream's Sensible ML (see Figure 1) is a paradigm shift in leveraging AI for Finance professionals. By seamlessly unifying AI within an enterprise Finance platform, Sensible ML's purpose-built for FP&A creates forecasts and insights that were previously impossible to do.

Figure 1: Sensible ML Process Flow

Purpose-Built AI for FP&A in a Unified Platform

By integrating AI, Finance teams can seamlessly leverage AI capabilities without the need for separate tools, systems or teams. No longer are the days of having data scientists create a forecast without understanding the business value and of the Finance team receiving the output with no understanding of where the numbers originated.

Instead, with purpose-built AI for Finance, FP&A teams are creating ML-backed forecasts on their own and for the entire process – from data ingestion and quality to model building, all the way to utilization and consumption. Finance professionals can now explain their accurate forecasts with confidence and do so at scale across hundreds or thousands of forecasts.

Sensible ML also incorporates external factors such as weather or macroeconomic factors to create highly accurate forecasts and utilizes a unique and groundbreaking concept, the Model Arena.

The Model Arena

The Model Arena offers tailored precision by automatically selecting the most performant model for each forecasted line item. Contrast this approach to the one-size-fits-all approach that apply a single model for all forecasted line items, failing to account for the characteristics of each product-location combination. Comparatively, the Model Arena approach produces a much higher level of accuracy by accounting for the nuances of different forecasted products by locations.

For example, Polaris, a global leader in powersports whose products have vastly different characteristics, uses Sensible ML to forecasts for specific products and locations with distinct models across the business. Only a unique ML model tailored for their snowmobiles or off-road vehicles can create an accurate sales forecast – optimizing for downstream processes like allocation of resources or maximizing contribution margin.

Sensible ML's Model Arena automatically selects the most accurate ML model for every product-location combination within differing business units. Ultimately, then, Sensible ML arms Finance professionals with deeper insights into future financial scenarios, enabling better decision-making and strategic planning.

Empower Your Finance Team With the Purpose-Built AI Capabilities of Sensible ML

As the Finance function evolves, grasping AI fundamentals is essential for maintaining competitiveness and achieving organizational goals. OneStream's Sensible ML equips Finance professionals with specialized AI capabilities, enhancing efficiency, accuracy, and strategic planning. Adopting AI allows Finance departments to lead in innovation and significantly contribute to their organization's growth and success.

Learn More About AI in Finance

To discover how FP&A teams can master AI basics in Finance, follow our Sensible ML blog series or download our white paper here.

If you're part of an enterprise seeking to enhance your use of AI in Finance, request a OneStream demo today!

Did you know OneStream also offers a weekly live demo webinar every Friday for 1 hour on a specific topic? Check out our resources library.

FAQs About AI in Finance

H3: What is ML in Finance?

Machine learning in Finance involves using algorithms to analyze vast amounts of financial data and make predictions or decisions based on patterns and insights derived from that data. ML is applied in various areas, such as fraud detection, risk management, algorithmic trading, and personalized financial advice. By continuously learning from new data, ML models enhance their accuracy and efficiency over time, enabling financial institutions to improve decision-making, automate processes, and offer tailored services to clients.

H3: How Can Financial Institutions Implement AI Effectively?

Financial institutions can implement AI effectively by starting with a clear strategy that aligns AI initiatives with business goals. They should invest in high-quality data management and ensure robust integration with existing systems. Prioritizing compliance and ethical considerations, along with training staff to leverage AI tools, is crucial. Collaborating with AI vendors and continually monitoring and refining AI models will also help in optimizing performance and achieving desired outcomes.

How Does AI Help With Fraud Detection in Finance?

AI enhances fraud detection in finance by analyzing vast amounts of transaction data in real-time to identify unusual patterns and anomalies that may indicate fraudulent activity. Machine learning algorithms continuously learn from new data, improving their ability to detect sophisticated fraud schemes and reduce false positives. This proactive approach enables financial institutions to swiftly address potential threats, protecting both the organization and its customers from financial losses.

Can AI Be Used For Investment Strategies?

Yes, AI can be used for investment strategies by analyzing large volumes of market data to identify trends, optimize portfolio allocations, and generate predictive insights. Machine learning models can develop and test trading algorithms, assess risk factors, and provide personalized investment recommendations, helping investors make more informed decisions and enhance returns. AI's ability to process and learn from data at scale offers a significant advantage in crafting effective investment strategies.

What Role Does AI Play in Credit Scoring?

AI plays a crucial role in credit scoring by analyzing a wide range of data, including traditional and non-traditional sources, to assess an individual's creditworthiness more accurately. Machine learning algorithms evaluate patterns and behaviors that traditional models might miss, providing a more nuanced understanding of risk. This enables lenders to make more informed decisions, potentially expanding access to credit for individuals with limited credit histories while improving the accuracy of risk assessments.