Although everybody knows about garbage in, garbage out, I have often the feeling that the need to play along in the AI-sphere lets conveniently people forget this.
The financial industry has always been data-driven, relying heavily on historical data to make future predictions. The integration of AI promises to revolutionize this space, as highlighted in the recent Citi report on AI in finance. However, while AI offers significant potential, there are critical considerations to keep in mind.
Quality and Consistency of Data
One major challenge is the quality and consistency of historical data. Financial data often undergoes several iterations of different data providers and enrichments, especially as we look further into the past. This can lead to inconsistencies and gaps, undermining the reliability of AI models. Historical financial data can be plagued with issues such as missing values, changes in data collection methods, and discrepancies between data providers. These inconsistencies can significantly impact the performance of AI algorithms, which depend on high-quality, reliable data to make accurate predictions.
Additionally, the process of data enrichment, while beneficial for adding context and value, can sometimes introduce errors or biases. As data is transformed and aggregated from various sources, the potential for misalignment increases. This is particularly problematic, because data analysis always promises to crystal ball by extrapolating and interpreting data from the past - which in itself is questionable.
