The advancement and integration of artificial intelligence (AI) into various sectors continues to develop, and the role of AI is becoming more encompassing than ever in many sectors. In corporate finance, AI is expanding beyond traditional automation. As a result, it continues to use sophisticated tools for data analysis, financial modeling, and risk management.
What is bearing this AI integration is especially new breakthroughs in machine learning and natural language processing (NLP). These have enabled finance professionals to handle complex financial tasks, while at the same completing them with increased accuracy and speed. As AI is becoming more and more adopted by the corporate finance sector, the AI for corporate finance market is expected to grow worldwide. For 2024, the global corporate finance market net worth is projected to reach $0.38 trillion, a number expected to pass $0.42 trillion by 2029 according to Statista. These numbers reflect how essential AI will be in facilitating this growth.
The transformation is driven by the ability of AI to analyze vast datasets, produce insightful predictions, and enhance decision-making across various areas of AI corporate finance. Finance teams are also able to streamline operations with the integration of generative AI and predictive analytics, which also means it is possible to detect fraud and create more robust financial models.
Financial forecasting enters a new era with AI
One of the most essential applications of AI in corporate finance is in financial forecasting. Where traditional forecasting methods rely on historical data and linear models, AI-powered forecasting tools are an upgrade. Traditional forecasting methods are labor-intensive and can be limited in handling the increasing volumes of data being generated by modern businesses. On the contrary, AI-powered forecasting tools are using machine learning algorithms to analyze both structured data and unstructured data in real time. Structured data refers to sales figures, costs, etc., whereas unstructured data refers to news headlines, social media trends, etc. As a result, AI-powered forecasts are both more accurate and can adapt quickly to market changes.
According to data from Keymakr, companies that use AI can reduce forecasting errors by up to 50%. Additionally, these tools allow for a more granular approach to forecasting, which makes it possible for finance teams to prepare for best- and worst-case scenarios in fluctuating markets. Many major companies such as Microsoft have already embraced AI for financial forecasting to optimize various processes.
Enhanced financial modeling for informed decision-making
A cornerstone of corporate finance is financial modeling, which is also being influenced by AI. It is especially generative AI models which can streamline the process of developing financial scenarios. AI-models are precise in recognizing patterns in large datasets, however the technology is still improving in certain aspects such as biases. However, AI makes it possible to identify variables which can affect a business’s finances. This includes anything from economic indicators to operational metrics. By leveraging AI, finance teams can build more sophisticated models, which can provide more in-depth insights.
One key benefit of AI in financial modeling is the reduction of human error. This can be achieved as AI systems can automate many aspects of model creation, limiting the margin of error. Furthermore, AI-driven models are both faster and more adaptable, which allows analysts to explore different scenarios with ease. The ability to instantly recalibrate and test thousands of data points makes AI a game-changer for AI corporate finance. It provides dynamic modeling options, which goes far beyond the reach of traditional methods.
Revolutionizing fraud detection and compliance
In the financial landscape, regulatory scrutiny is increasing. Due to this, AI-powered tools have become essential in fraud detection and compliance. The pattern recognition capabilities of AI make it possible to detect anomalies in financial transactions that could be an indicator for fraud. This is a major advancement for corporations dealing with high volumes of transactions daily. According to numbers from GRF CPAs & Advisors, it is estimated that organizations lose 5% of their revenues each year to fraud. To combat this, AI has become an important player, as it can continuously monitor transactions in real time. This makes it possible to flag suspicious patterns in real-time, which otherwise could have gone unnoticed by huma auditors.
AI systems can for example analyze factors including transaction frequency, amount, and time, which makes it possible to identify fraudulent activities faster than ever. Beyond fraud detection, regulatory compliance is also being achieved by AI. This is oftentimes a struggle for companies operating across multiple regions, but by leveraging machine learning algorithms to analyze regulatory updates, it is possible to align them with financial transactions. In other words, AI can help companies avoid costly penalties for non-compliance.
The rise of generative AI in corporate finance
An emerging branch within AI is generative AI, which is establishing itself by automating repetitive tasks and creating tailored reports and financial documentation. Many corporations have started to utilize generative AI to summarize and generate reports based on complex data. As a result, analysts are freed up so they instead can focus on strategic activities rather than manual data processing. On top of this, generative AI can also produce summaries of extensive financial documents, which makes it possible to streamline the review process. Ultimately, finance teams can act more quickly on critical information.
The processing capabilities of generative AI is especially valuable for corporate finance departments who are destined to manage large amounts of textual data. By automating the reviews and summary of documents like earnings reports, market analyses, and compliance documents, generative AI can reduce the time and labor costs that is otherwise an integrated part of manual analysis.
