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The Artificial Intelligence (AI) industry is one of the fastest growing in the technology world. The AI market is expected to be worth over $1,394 billion by 2029, with a growth rate of 20.1% in the next seven years.
You can find the latest AI technology across a variety of industries, including healthcare, education, telecoms, and more. The primary goal is to streamline and automate more processes, to the benefit of the user.
The finance industry is one industry, in particular, that is already reaping the benefits of the latest AI technology advancements. There are a variety of use cases in the field, including reducing risk in loan underwriting and preventing money laundering.
In fact, the global AI FinTech market is expected to reach $22.6 billion alone by 2025.
However, despite this exponential growth in the space, the fact remains that around 80% of AI projects fail. There are a few reasons for this that vary depending on the industry, but there are generally two clear challenges that the AI industry faces.
1) Fear of AI still remains, despite the continued integration of technology into daily lives. Whether that is a fear that “AI will take over the world”, or simply a hesitancy to trust AI, this makes it particularly challenging for AI projects to pick up momentum.
2) Alternatively, people won’t blindly follow AI, just because it’s AI. In industries like finance, this simply isn’t a risk that investment managers can afford. Advisors need a reasoning process that they can validate. This means they need to see the data, they need to see the thought process, and they need to see the output. Too many financial AI solutions lack this offering.
So, what does the future of AI look like in the finance industry? The latest AI tech aims to eliminate these challenges and focus on transparency to drive the financial industry into the future.
You might have heard this term before, and possibly passed it off as somewhat of a buzzword. Used incorrectly, it definitely is. However, the real meaning of explainable AI has the potential to really shape the future of investment management.
Explainable AI, or XAI, is a series of technologies that seek to open up AI software, making the processes and reasoning more accessible to the person using it. The algorithm is transparent, and the user can understand how that algorithm or piece of software came to the conclusion that it did.
This can be done in a variety of ways, depending on how the software solution is programmed. However, in financial AI, there are typically two ways that XAI establishes transparency:
1) NLG: Natural Language Generation technology is AI programming that turns data into written or spoken narratives. Effectively, the AI turns the data into something palatable to a human user, and directly explains that to the user.
2) Data Transparency: Put simply, this means that the AI provides direct access to the data that it uses to reach a decision or outcome.
Often explainable AI is a mixture of these two aspects, with NLG being used to explain data that shows how a conclusion was reached.
But explainable AI requires developers to go further and actually build algorithms that present the data and the weighting of the data that resulted in the conclusion. This may seem obvious, but it’s not how the majority of machine learning systems are built. And with machine learning becoming the dominate form of AI, many AI tools remain black boxes, because of the structure of their underlying algorithms or programming.
Now, while XAI is a key concept for the financial AI space, it’s also important not to get too hung up on the term. Effectively, software that is transparent about a process that doesn’t make sense is entirely pointless. The algorithm and the calculations need to be right before explainable AI can really have an impact, especially when dealing with financial forecasts.
So, how do you make sure that AI technology is actually providing a valuable contribution to your business or portfolio, rather than simply being a good piece of PR?
The best Machine Learning (ML) algorithms need considerable amounts of training data in order to reach truly meaningful results. Statistically speaking, the more data, the more accurate the ML can be.
This is where too many AI software fail. That’s not to say that they aren’t trying to gather as much data as possible. Unfortunately, in many industries, there is a lack of access to data, especially data that is well-organized and relevant.
For instance, many industries have unclear data laws, or just a lack of homogenization across data storage or transfer. However, the best AI solutions rely on maximizing data for statistically accurate evaluation.
This premise is fairly simple. You get out what you put in. Or, put differently, if you put messy data into AI software, you’ll just get amess churned back out. The best AI technology thrives on well-organized data.
Therefore, in companies or industries that do have access to the data, and the right amounts of data, it’s unlikely that the data is in a palatable format for AI use. Structure and clarity are just as important to XAI technology. Without this, users cannot possibly be able to check the legitimacy of any forecasts, or predictions, resulting in a “black box” technology.
While access to data is important, big data isn’t the answer. Successful AI technology is about providing the right data.
What do we mean by that? We mean that you need to choose the right data to get the results that you need from AI technology, and the right understanding of that data. You cannot expect technology to do that for you. As mentioned, you get out what you put in.
For instance, at 3AI, we focus on 326 unique factors for each of the 20,000+ stocks that we track. In doing this, we turn big data into the right data for our AI technology.
Using our industry expertise and working with industry leaders, we determined which factors were actually the most important to stock performance in practice, and analysed those factors across a cumulative 1 million years of stock data.
XAI and finance AI performance comes down to a fine balance of three things:
- The right factors;
- The right data; and
- The right algorithm.
The power in the 3AI system is our understanding and analysis of years of stock data, focusing on transparency first.
As mentioned, explainable AI is the last step in successful AI implementation in the financial industry. The preparation and selection of data is the key to making an AI technology truly explainable.
We have explained how our Deep Factor Framework (see our home page for more details) drives our predictive technology. Ultimately, that framework is also what makes our technology an XAI solution. The framework provides our technology with the ability to explain its conclusions.
For each prediction or insight, we expose the individual factors that contributed to the information provided. Effectively, that allows the technology to say, “based on factor A and factor B, we expect this to happen in the future”.
Therefore, not only are users provided with their key forecasts and predictions, but the individual factors behind those decisions, too. This allows every investor to use their experience to assess the AI decision-making, and make changes accordingly.
The primary goal of our technology is to allow investors to make informed decisions quickly, while avoiding a passive investment approach. Homogenization isn’t always the best approach in the finance market, and we want to make sure that investors have every opportunity to maximize returns.
As mentioned, people are generally afraid to trust AI, typically due to their fear of the unknown or previous experiences with the technology. This is especially true in a market that is already managing risk, such as financial investments. This is why investors need to understand a reasoning process in order to trust the technology.
However, ultimately, explainable AI is so important, as it helps improve user adoption. The more people trust AI and understand AI, the more people will integrate the technology into their investment approach.
AI that isn’t used by those on the front line is useless. XAI is the answer to understanding and evaluating AI reasoning processes. The more comfortable users feel with this, the higher the uptake of the innovative technology. In the long run, the more variety on the market, the more potential for returns.
In all parts of our lives, data is increasing. The thirst for data ensures that every transaction we have, whether with companies, websites, apps, etc, all mine for data. As the digital world evolves, this only continues.
Not only that, but over time, more and more data will become available. The phenomenon of big data is only going to continue.
In the finance industry, this means that financial data is continuing to increase. There’s far too much data for one person, investor, or advisor, to sift through in order to make investment decisions. It’s not possible for people to assess bond or stock performance alone, with any real accuracy.
This is largely why the passive investment strategy has become the norm. But this can’t achieve real alpha. All investors following the same advisors, or all investors following each other, simply homogenizes the market. There’s no potential for growth individually, nor for the market as a whole. Outperformance becomes a dream.
The likes of Warren Buffet (‘the Sage of Omaha’) didn’t succeed by following the markets and using passive strategies, and he certainly didn’t get ahead of his competition by dumb luck.
The key was leveraging all the available data, and always asking more questions, to understand where the market was going. Now, we can’t harness Warren Buffet’s brain and put it to use for investors, but we can harness the ideology. Clean, organized, and voluminous data is the key to effective forecasting.
So, as the volume of available data continues to rise, AI is the only tool available that lets you handle and leverage large data sets at scale.
Further to that, explainable AI is the solution that lets you handle and leverage all of that data compliantly. XAI increases the adoption of the latest technologies in the investment world, and ensures that investors feel comfortable with the integration of AI technology into their stock performance forecasts.
We founded 3AI with a simple ambition; to understand how markets work. Our primary focus wasn’t to create an AI company, but to create a powerful software that drives investment potential for investment managers. AI is the best tool at our disposal right now that allows us to achieve that goal.
However, we recognize that not all AI tools are created equal. In fact, some AI technologies in the finance space lack so much transparency that they are almost impossible to adopt.
How does our software differ? We trained our technology on over 1 million years of cumulative data. Not only that, but our deep factor framework translates that data into something palatable for investors, and for the AI. Our 326 factors allow us to get to the bottom of what truly drives stock performance.
Want to learn more about how our AI solutions can help you to achieve positive alpha performance? Get in touch with us today!
