The advancement and integration of artificial intelligence (AI) across various industries continues to expand, and the role of AI is becoming more global than ever across many industries. In corporate finance, AI goes beyond traditional automation. As a result, it continues to use sophisticated tools for data analysis, financial modeling and risk management.
This integration of AI is based in particular on new advances in the field of machine learning and natural language processing (NLP). These have enabled finance professionals to manage complex financial tasks, while completing them with greater precision and speed. As AI is increasingly adopted by the corporate finance industry, the AI for corporate finance market is expected to grow globally. For 2024, the net worth of the global corporate finance market is expected to reach $0.38 trillion, a figure that is expected to exceed $0.42 trillion by 2029 according to Statista. These numbers reflect how critical AI will be to facilitating this growth.
The transformation relies on AI’s ability to analyze large data sets, produce insightful predictions, and improve decision-making in various areas of AI-driven corporate finance. Finance teams are also able to streamline their operations through 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 the financial forecasts. Where traditional forecasting methods rely on historical data and linear models, AI-based forecasting tools are an upgrade. Traditional forecasting methods are labor intensive and can be limited in handling the increasing volumes of data generated by modern businesses. On the contrary, AI-based forecasting tools use machine learning algorithms to analyze both structured and unstructured data in real-time. Structured data refers to sales figures, costs, etc., while unstructured data refers to news headlines, social media trends, etc. As a result, AI-based forecasts are both more accurate and can quickly adapt to market changes.
According to Keymakr data, companies that use AI can reduce forecast errors by up to 50%. Additionally, these tools enable a more granular approach to forecasting, allowing finance teams to prepare for best- and worst-case scenarios in fluctuating markets. Many large companies such as Microsoft have already adopted AI for financial forecasting to optimize various processes.
Improved financial modeling for informed decision making
The cornerstone of corporate finance is financial modeling, which is also influenced by AI. These include generative AI models that can streamline the process of developing financial scenarios. AI models are accurate at recognizing patterns in large data sets, but the technology continues to improve in some areas. aspects such as prejudice. However, AI helps identify variables that can affect a company’s finances. This includes everything from economic indicators to operational metrics. By leveraging AI, finance teams can create more sophisticated models that can provide deeper insights.
One of the main benefits of AI in financial modeling is the reduction of human errors. This is possible because AI systems can automate many aspects of model creation, limiting the margin for error. Additionally, AI-based models are both faster and more adaptable, making it easy for analysts to explore different scenarios. The ability to instantly recalibrate and test thousands of data points makes AI a true game changer for AI-driven corporate finance. It offers dynamic modeling options, which go far beyond the scope of traditional methods.
Revolutionizing fraud detection and compliance
Across the financial landscape, regulatory oversight is intensifying. For this reason, AI-based tools have become essential for fraud detection and compliance. AI’s pattern recognition capabilities can detect anomalies in financial transactions that could be an indicator of fraud. This is a major advancement for businesses processing large volumes of transactions on a daily basis. According to figures from GRF CPAs & Advisors, it is estimated that organizations lose 5% of their revenue each year to fraud. To combat this, AI has become an important player as it can continuously monitor transactions in real time. This allows suspicious patterns to be flagged in real time that might otherwise have gone unnoticed by human listeners.
For example, AI systems can analyze factors such as frequency, amount and duration of transactions, helping to identify fraudulent activity faster than ever before. Beyond fraud detection, regulatory compliance is also ensured using AI. This is often a challenge for businesses 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 businesses avoid costly penalties for non-compliance.
The rise of generative AI in corporate finance
An emerging branch of AI is generative AI, which is making its mark by automating repetitive tasks and creating tailored financial reporting and documentation. Many companies have started using generative AI to summarize and generate reports based on complex data. As a result, analysts are freed up to focus on strategic activities rather than manual data processing. Additionally, generative AI can also produce detailed financial document summaries, helping to streamline the review process. Ultimately, finance teams can act on critical information more quickly.
The processing capabilities of generative AI are particularly valuable for enterprise financial departments intended to manage large amounts of textual data. By automating reviews and summarization of documents such as earnings reports, market analyses, and compliance documents, generative AI can reduce the time and labor costs that are otherwise integral to manual analysis.