Artificial Intelligence (AI) technology is making waves in a variety of industries. Many of us don’t realize just how much AI is already integrated into our lives. Personal assistants such as Siri or Alexa are one of the most popular examples of widely integrated AI technology.
The growth is so sustained that the global AI market is expected to be worth $1,394 billion by 2029. Global economies are struggling post-Covid, so such exponential growth is extremely surprising and indicative of the potential that AI technology is understood to have in the modern world.
AI is starting to become a standard feature in industries, such as healthcare, retail, and logistics. However, it could be argued that one of the industries with the most potential is finance or investments. This space manages large volumes of data in order to understand the market.
Despite this, there have been challenges in the adoption of AI in finance. AI companies need to increase user adoption. How can this be achieved?
In many ways, investing is a risky business. Generally though, investors try to avoid as much risk as possible. For some, artificial intelligence technology poses too much risk. The fundamental reason that AI has struggled to pick up traction in the financial sector is a lack of trust in the technology.
Previously, financial AI products have pitched themselves as a kind of magic solution to investor problems. This sounds great, but ultimately investors are wary of anything that claims it can do their job better.
“Black Box AI” is a term used for AI solutions that lack transparency in their processes. Data goes in, and a conclusion comes out. Only the developers really understand what happens in between. This type of AI is common across industries. Needless to say, this type of AI is particularly unsuitable for the investment industry.
The sector needs an AI solution that is transparent about its processes, and allows for human contextualization. This is the only way to garner investor trust, and increase user adoption.
Explainable AI, or XAI, is exactly that solution. In simple terms, explainable AI is artificial intelligence technology that makes its processes accessible to users. The primary goal for this technology is to help users understand why the AI reaches the decisions that it does, and allow them to make informed decisions based on that.
There are two primary ways that explainable AI allows users to understand its decision-making:
1) NLG (Natural Language Generation): This technology is a type of programming that turns data into written or spoken narratives for the understanding of its end-user. The AI technology runs its analytical processes as normal, and then turns that into a clear outcome for the person using it. A common example of this is website chatbots, or automated assistants.
2) Data transparency: Just as it sounds, this technology makes its data accessible to the user. The user can access the data that the AI has used to make a decision, and can then use human reasoning to evaluate the validity of any outcomes.
The main ambition here is to make AI processes more accessible and clear for the user. Transparency like this eases the fear and distrust in AI, which is critical in the investment sector. Understandably, in a high-risk environment, it can be challenging to trust the unknown. XAI aims to remove that element, and provide investors with all the information they need to make informed decisions.
While XAI technology is a key driving force behind the integration of AI into the financial sector, it cannot achieve this alone. Effectively, as with all AI, explainable AI technology is only as good as the data it receives. Muddled and insignificant data is going to result in muddled and insignificant insights, making the technology unusable for investors.
The financial world is gradually starting to harness big data. As digitization increases, more and more data is made available by companies and third parties. It’s almost impossible for investors to keep up.
In its natural form, big data cannot be put to work for investors. Organized and structured data is the key to successful explainable AI integration in finance.
This is where factor frameworks are a valuable tool. Factor investing has been a popular investment strategy in recent years. However, many analysts base their analysis on around five factors.
At 3AI, we believe that it’s simply not possible to make educated investment decisions based on so few factors. AI technology allows us to harness big data. That’s why we use 326 individual factors to analyse over 20,000 stocks.
Combined with XAI technology, we can identify the individual factors that contribute to each AI decision or forecast. This allows investors to understand and evaluate each recommendation. We believe our AI technology achieves maximum transparency whilst harnessing the data processing power that artificial intelligence technology has to offer. Our deep factor framework translates big data into actionable insights to empower investors to achieve alpha returns.
Our team of investment professionals didn’t set out to build an AI technology company within the SaaS and tech industry. Our primary ambition was, and still is, to understand how markets work. We believe that harnessing big data is the solution to a thorough understanding of investment market performance.
We also understand that big data is overwhelming on its own. Investors simply cannot translate the hordes of data available into any actionable insights. This is why we decided that AI was the answer to our data processing challenges.
Our machine learning algorithm is trained on over 1 million years of cumulative historical data. This has allowed us to test the accuracy of its decision-making by running scenarios based on historical events.
Transparency is our priority. There is a reason that our technology has outperformed some of the top global indices. Curious to learn more? Get in touch to discuss our available data sheets, or our AI investment solutions!