By Rachel Carpenter
Why does the fintech industry need to make data and AI more accessible? In short, time, money and intelligence. Each day we are tasked with making financial decisions on matters ranging from personal finance to professional investments to managerial and governmental decisions.
The problem
International Data Corporation (IDC) predicts the world will be creating 163 zettabytes of data a year by 2025. The endless stream of information at our fingertips may seem like a tremendous benefit when it comes to making decisions. However, without having the data organized and provided to us in an efficient manner, it can actually lead to short-term thinking. In turn, this leads to poor decisions, such as buying the wrong stock or overspending.
AI can empower machines to help us understand the information. At Intrinio, we use AML to organize and “clean” the data, then provide our users with only the information they need, when they need it, in an easily digestible manner. This foundation frees up developers to build even more advanced AI on top of those data sets – AI that can help us make better decisions and understand the plethora of data surrounding us.
Possible uses for AI
As our information becomes increasingly digitized and available, it can begin to gather intelligence at the corporate and governmental levels. I’m not referring to decentralized autonomous organizations (DAOs).
Rather, existing businesses and government agencies will be able to leverage AI in decision making processes at the top level via easy to use tools.
Fintech companies in the U.S. are currently exploring the possibility of building artificial intelligence systems that drive decision making at a tactical, financial and strategic level in both organizations and governments. In the future, AI will be running our government. For those who doubt the feasibility of implementing AI within the government, they can look to China, which is already doing it.
The resulting efficiencies from implementing AI at the highest level of businesses and governments could have the greatest impact on global productivity in the next 50 years.
Decision making framework
In order for top-tier decision makers to harness the power of AI, multiple steps are required. The easiest way to understand the process is to view it as a pyramid. At the base of the pyramid is data, which stems from events, such as market movements. Then, this data advances to the next level – AI gives context to the data and it becomes information. When the technology advances, it can take the information, give it meaning and provide knowledge. When AI is capable of understanding this knowledge and providing humans with recommendations, we reach the top of the pyramid: wisdom.
Here are some examples of what machines leveraging AI are capable of at the four levels of the pyramid. They can:
- (Data) Regurgitate data, such as annual sales for Tesla or your bank account balance.
- (Information) Interpret data and spot patterns, trends and movements. For example, informing a user that Tesla’s stock price is rising or your bank balance is falling.
- (Knowledge) Make inferences by evaluating events and trends. The machine may tell you the oil industry is overvalued or that you spent too much at bars this month.
- (Wisdom) Mimicking human intelligence to provide guidance and recommendations. The machine may advise you to immediately short a stock, or perhaps tell you it’s time to quit making your own investments and hire an adviser.
Some types of financial data have advanced to the knowledge stage in a limited fashion. We have not yet seen AI provide true wisdom at the top of the pyramid.
How do we get there?
It all starts with the data industry, which is still heavily focused on the construction and collection of data, rather than on the consumption, usage, and amplification of that data.
The industry must begin to look at the user base: Who is using this data? Who is responsible for building AI for financial services? The answer: developers.
Developers hold the key to innovation. The more one looks at the actual consumption of data, they realize how difficult it is to work with given the current landscape – especially for developers focused on building AI-powered efficiencies.
Currently, companies such as Watson, Alexa, Siri, Deep Mind and Cortana are leading the way in AI. But, in order to spur rapid innovation, we need more companies and developers innovating. This requires smart humans and AI working together to build impactful AI-powered tools for financial services – many of those humans are not working at large companies and institutions.
Developers continuously struggle to create innovative solutions due to high costs and the level of difficulty involved in utilizing data. It must become more accessible and affordable, vital to creating a base for their pyramid.
Given the proper access, as well as tools that simplify the utilization of data, developers will be equipped to create AI that can generate wisdom, thus unlocking the full potential of AI. This is where the future of fintech lies.
If we ever wish to unlock the full potential of AI, we must ramp up innovation. This means democratizing data, which is the key to democratizing AI. If we democratize data and tools and get it in the hands of developers, we will see powerful AI technologies surface – greater than we could imagine now, given the current paradigm.
This will include the creation of specialized, democratized solutions and truly unlock AI within every aspect of our businesses and government. That is a future I’d like to see – and it all starts with data.
