Machine learning technology can be found in a variety of industries, including logistics, retail, and healthcare. The market for machine learning is continually growing, as more and more companies look to find new ways to simplify processes.

In fact, one of the most common day-to-day uses of machine learning (ML) can be found in our pockets. Facial and image recognition are used by smartphone users every day.

The machine learning market is expected to be worth around $200 billion by 2029. That’s a CAGR of 38%.

ML technology is able to assist with medical diagnosis in the healthcare industry, or for something as simple as a chatbot in the retailor eCommerce industry. However, while the technology continues to pick up traction, it has yet to find a solidified place in the investment sector.

So, what is preventing investor adoption of ML technology? What does ML need to offer in order to prove value in the financial industry?

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Challenges With ML in Finance

This might seem like stating the obvious, but there is a certain amount of risk involved with investing. An investor's main goal is to negate that risk as much as possible, while maximizing return. This is why people place their trust in advisors that know the market.

In an industry where trust and understanding are so important, making changes to processes can be a challenge. This is where ML technology has faced difficulties in adoption.

In the past, many financial machine learning and AI products have claimed to be some kind of magic solution. Ultimately, investors aren’t going to place their faith in something that they don’t understand, but claims to do their job better than them.

Previous solutions in this space have lacked transparency. It may be able to translate data into viable insights or forecasts, but advisors aren’t willing to believe in its legitimacy unless they can understand how that conclusion was reached by the technology. However, machine learning technology could be critical to the future of the investments sector as the world of big data continues to grow.

This is why the industry needs a machine learning solution that puts clarity first. Investors need a tool to drive their returns, not a technology that replaces them.

What is Explainable Machine Learning?

Explainable machine learning technology prioritizes that transparency. Effectively, explainable machine learning is a technology that makes its processes, data, or algorithms available to its users.

In the past, similar technologies that have been used in the finance space have been what is called “black box” technology. This means that data goes in, and a conclusion or answer comes out, but there is no way to know what happened in between.

Explainable ML takes away any uncertainty. Access to the calculations and data means that investors can evaluate the technology’s forecasts entirely objectively. Human contextualization is extremely powerful in the investment industry, and ML that removes this ability simply cannot perform.

The best machine learning tools for investors will make their data and algorithms clear in order to ease any distrust in the solution. Make informed investment decisions with explainable machine learning for data processing and forecasting.

Explainable ML Needs Structured Data

While explainable ML is the driver behind increased adoption in the finance space, it cannot achieve perfect augmentation on its own. Ultimately, AI and ML technologies are only as good as the data that they are running on.

Messy and unorganized data will provide messy and unorganized results, making the solution unusable for the advisor or investment manager.

The financial sector, as with a great many other industries, is seeing an increasing volume of data available. Big data is fuelling industries across the world, as digitization provides access to hordes of company and third-party data. This data is valuable in the right investors’ hands, but it needs translating.

Organization and structure is the key to making big data work for investments. This is where a deep factor framework is crucial.

Factor investing has been an increasingly popular strategy in recent years. However, this strategy has been implemented with a limited number of factors. 3AI’s deep factor framework analyses data based on 326 individual factors, breaking down the amount of available data into palatable and understandable pieces of information.

Over 20,000 stocks are analysed based on these factors, to provide investors with insights that can be evaluated easily. The combination of a deep factor framework like this, and an explainable machine learning algorithm, provides a tool that drives investors to achieve increased returns.

Increase Returns With 3AI Explainable ML Solutions

At 3AI, our main goal is to understand how the market works. Our experienced team has spent considerable time in the investment industry, and understands just how challenging it is to meet time constraints, while making valuable forecasts.

We started out to try and solve this problem, not to become an AI company. However, through extensive research and testing, we believe that artificial intelligence technology is the key to harnessing big data and unlocking alpha for investors.

However, we also understand that not just any AI technology is going to cut it. We know that around 80% of AI projects fail. We won’t be one of them. Explainable machine learning and AI technology provide the transparency that is so critical in the world of investments and finance.

We believe that we have succeeded in building a must-have tool that augments investors, and boosts their ability to improve returns. In fact, our AI-powered indices have consistently outperformed popular global indices like MSCI World.

Want to learn more? Get in touch if you’d like to see our performance data sheets, or to learn more about explainable machine learning technology for your investment portfolio.