Bank of America Cuts Through AI Hype: Data Quality is Key
Bank of America’s Matthew Davies, a seasoned pro in AI, is setting the record straight: success with artificial intelligence isn’t about flashy tech, but rather about having access to high-quality data.
Davies makes this point during his ‘Summer School’ series, aimed at finance chiefs struggling to make sense of the AI landscape. With so many options available, it’s easy to get lost in the noise – but Davies is clear: the challenge now is less about finding the right technology, and more about navigating the abundance of choice.
What Makes Good AI Data
Davies stresses that good AI data is about more than just having a large amount of it. It’s about having data that’s accurate, consistent, and relevant to the task at hand. This isn’t always easy, especially when working with complex financial systems that involve multiple variables and stakeholders.
The Financial Industry Regulatory Authority (FINRA) has estimated that 70% of all data used in AI models is of poor quality. This not only leads to inaccurate results but also wastes valuable resources on data cleaning and processing.
The Benefits of Good Data
So, what happens when you get AI data right? According to Davies, the benefits are significant. With high-quality data, you can build AI models that provide actionable insights, automate repetitive tasks, and even spot potential risks before they materialize.
“The right data is what makes AI truly effective,” Davies emphasizes. “It’s not about using the latest tool or technique – it’s about using the data that actually works.”
What this means for finance chiefs and businesses looking to harness AI is that data quality should be their top priority. Investing time and resources into ensuring data accuracy and consistency will yield far better results than trying to shoehorn subpar data into existing AI solutions.
