Our latest interview with Pamela Cytron, President of The Founders Arena WealthTech Accelerator, revealed how, while at her last tech company, she uncovered the underlying requirement for AI: a concept she calls “authentic intelligence.”
What does authentic intelligence mean? It starts in 2010, when Cytron was focused on integrating slower existing back-office solutions with new technology. As she was merging them, she discovered that much of the data wasn’t clean. Because of this, the existing tech stack was impacted, and the implementation couldn’t be completed.
“I originally thought that if I could clean all the data, we could implement it much faster. It was a great theory but bigger than just one problem. Essentially, we started going into the existing technology and taking the information historically held hostage in these institutions on paper from the single source of truth – the original documents. The outcome was creating what I call “authentic intelligence” from these paper trails,” says Cytron.
Making Dirty Data Clean Again
In financial institutions, when a new client is put into a system, and then that same client is replicated into another system, if the data involved human error, it is then considered dirty. Now there are thousands of terabytes of this data across systems, which means before integrating new solutions, dirty data must be removed, and then the rules around that data must be rewritten to make it accurate.
Only once this dirty data is removed can the accurate data or what Cytron calls “authentic intelligence” be left. Now you can begin integrating across solutions.
“I always say data doesn’t die and data doesn’t lie. So, if we think about AI, you can only have artificial intelligence once you have authentic intelligence, which comes from all the clean data compiled in the institution.
The problem is that we already have issues with all data, not by intent. Much of the existing data, particularly on Wall Street, has embedded bias, not because they set the rules that way, but because of human error years ago,” she adds.
Authentic intelligence, which is comprised of clean, scrubbed data, is significant to wealth management because it determines the following when inputted into automated systems:
- Decision-making
- Personalized service
- Regulatory and compliance,
- Efficiencies and cost reduction, and
- Fraud and prevention.
To effectively automate these systems through AI, it’s essential first to ensure that decades of data are clean and authentic intelligence is applied across these five key areas of an organization’s data strategy.
“If we think about all of these places of data silos, no matter how big or small of an organization, the accuracy and the consistency of one’s technology systems must rely on authentic intelligence through clean data, or it will create biases in these five areas,” comments Cytron.
Emerging Wealth Tech and AI
Cytron says that wealth tech startups must implement AI in their solutions. However, she cautions that a new AI model can create biases once it starts training if authentic intelligence isn’t top of mind for their models.
For example, in open AI models where data is just being put in, it’s unknown if there is any influence on where the data comes from or if extremes on either side are being trained accurately within the AI model.
She says another important point to consider for any wealthtech that is entering the marketplace is what they mean by AI, as the confluence of terms can be confusing. For instance, tracking and aggregating data is not AI. It is a steppingstone to developing AI systems but should not be considered AI on its own.
Solutions for the wealth industry must center on transparency and communication, performance, trust, and reliability, but beyond all of this, clean data, aka “authentic intelligence” underpins all of this. Cytron believes that if you consider the impact of the data across those four fundamentals, plus personalization, spending habits, investment strategy, and future goals, a wealth tech implementing clean data and training AI to act upon it, alert to inconsistent or changing data, can then send actionable insights back will be an industry game changer.
The foundation this AI is built on in the end, and what will be key for those building the next generation of wealth tech solutions, will be clean data.
