Beyond prediction: Rise of explainable AI in financial decision making

FOR decades, financial analysts have relied on a core set of tools and models to assess risk, predict market trends, and guide investment strategies.

- By Rozan Jameel and Dr. Susil Kumara Silva

While these traditional methods have provided a valuable foundation, they often fall short in today's increasingly complex and data-rich environment. The limitations of these traditional approaches are becoming ever more apparent.

The limits of traditional financial analysis

Traditional financial analysis often oversimplifies complex financial realities. Models struggle to capture the nuances and interdependencies within a company's data. Moreover, they often struggle to process the sheer volume of data available today, potentially overlooking critical signals and patterns. And, as some have pointed out, financial statements can sometimes be deliberately obfuscated (Piazza et al., 2022), making accurate analysis even more challenging. The primary focus of these traditional models has been on prediction - forecasting future performance based on historical data - without providing much insight into why those predictions are made.

The promise of Al

The rise of artificial intelligence offers a powerful new set of tools for financial analysis. Al algorithms, particularly those based on deep learning, can sift through massive datasets to identify patterns and anomalies that would be impossible for human analysts to detect (Mienye et al., 2024). Al has the potential to revolutionise financial analysis, enabling more accurate predictions, more efficient risk management, and more informed investment decisions.

The problem with "black box" predictions