Data Issues Still Hamper AI—But AI Can Fix Them
Instead of waiting for data quality to be sufficient to power AI models, those at the cutting edge are building models to bridge the gaps in the data, and apply it to more sophisticated use cases.
Dirty, incorrect, and incomplete data continues to pose barriers to adoption of artificial intelligence in key areas of financial firms’ workflows, such as development of trading algorithms. But there’s good news: the same AI techniques firms are using to create new trading models can also be used to fix the data issues that have traditionally hampered the effectiveness of data-hungry AI models for trading and analytics.
“All of these models are dependent on data, whether you’re using AI
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