The use of AI in Finance

Whereas finance professionals previously relied on their static models and historical data, AI and machine learning offer new applications. These can assist them in today’s complex market environment, where traditional approaches often prove inadequate. By utilising advanced algorithms, finance professionals can analyse vast amounts of data and uncover patterns that previously went unnoticed.

AI also enables finance professionals to develop complex models that take into account a wide range of variables, from market sentiment to business performance. Through the use of machine learning, these models can improve themselves as they gather more data and recognise patterns, meaning decisions are informed by real-time insights and adaptive analyses.

The Use of AI in Finance

AI-based solutions are capable of simplifying complex processes, analysing market trends, identifying risks when granting supplier or investment credit, predicting future behaviour, assessing the likelihood of fraud, and more.

Examples include:

Anomaly detection: AI uses historical data to alert planners when planning figures fall outside normal ranges. This significantly improves understanding of the causes of data deviations, enabling timely adjustments where necessary. Each new case enables the model to learn from user feedback and continuously improve the process.

Reporting of outliers: AI can significantly reduce response times when detecting outliers in forecasts. In real time, AI can compare its own predictive forecast with a planner’s forecast, the budget or another version. It can then identify accounts with significant discrepancies. In this way, AI helps to facilitate faster analysis across planning versions, whilst also highlighting irregularities as soon as they arise.

Identifying risks: Expense processing is a major area of risk for organisations. Rather than fraud, risks often stem from manual input errors such as duplicate expenses, issues with amounts or incorrect expense categories. Machine learning can help here by examining large data sets and identifying items that appear unusual. This streamlines the process of assessing expense reports and speeds up reimbursement times.

AI and machine learning are not ends in themselves. They are tools and technologies designed to help professionals make all kinds of processes more efficient.

Machine learning – and deep learning – are all subsets of AI. AI processes data to make decisions and predictions. Machine learning algorithms ensure that AI not only processes that data, but also learns from it and becomes smarter as a result, without the need for additional programming work.

Challenges and risks of AI

Nevertheless, the integration of AI and machine learning also presents challenges. Harvard data scientist Tyler Vigan wrote in his book ‘Spurious Correlations’ that not all correlations indicate an underlying causal relationship. By this, he aims to warn that machine learning applications are vulnerable to both human and algorithmic biases and errors. An additional challenge arises when the algorithm and its output are so complex that they can hardly, if at all, be understood by humans. This is known as a ‘black-box model’. This poses risks for businesses if they are unable to determine how and why an algorithm has reached a particular conclusion or decision.

Finance professionals must be aware of the implications of using automated systems, such as bias in algorithms and the need for transparency and accountability. Furthermore, it is essential to retain human expertise and to combine AI systems with human judgement to ensure a balanced approach.