How Computers Became Better Wealth Managers Than Humans

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From dominating in chess to managing millions of dollars — the story of A.I. as you’ve never read it before.

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Danail Velchovski
August 12, 2022
Aug 12

Wealth management is a process that helps individuals invest their money in a way that meets their financial goals. It includes creating a financial plan, investing in crypto, stocks, bonds, and other assets, and monitoring progress to make sure the plan is on track.

For most people, wealth management is something they delegate to a financial advisor. But what if there was a better way? What if robots could do a better job of managing your money than humans? It may sound farfetched, but it’s not as crazy as it sounds.

In fact, there are already a number of ways in which robots are outperforming humans when it comes to managing money. For example, robots are much better at analyzing data and making investment decisions.

They can process large amounts of data much faster than humans can, and they’re not subject to emotions or other biases that can lead to bad investment decisions.

“How some people imagine a Robo advisor to look like” Image Source: Unsplash

Now, let us look at the history of chess engines as the development of technology there is really relatable to that of wealth management. What it took humans centuries to develop, computers did in a couple of years. And after that neural networks came into the scene and dominated everything within several hours of training time.

History Of Chess Computers

The history of chess computers is a long and complicated one. It took humans centuries to develop chess theory, and the first computer that beat a world champion didn’t come along until the 1990s. Since then, chess computers have come a long way.

The first fully-working chess computers were created in 1950. They were called mechanical chess players, and they were nothing more than simple robots that could make basic moves.

It wasn’t until the end of the 20th century that a chess computer was able to beat a world champion. In 1997, the computer Deep Blue beat the then reigning WC, Garry Kasparov, in a six-game match.

Man vs. Machine. Source:

Since then, chess computers have only gotten better. The latest generation of chess computers is powered by neural networks, which makes them far superior to traditional chess robots. Neural networks are able to learn and improve upon themselves, which means that they can get better at playing chess by studying the moves of grandmasters and other successful chess players.

In 2017 Deepmind, an AI research laboratory announced the first chess engine that runs on neural network programming.

For each move, AlphaZero searches only a small fraction of the positions considered by traditional chess engines.

In a 100-game match against Stockfish (the best traditional chess engine), AlphaZero won 28, drew 72, and lost ZERO games.

Algorithmic Chess Engines

Chess computers have come a long way since the early days of Deep Blue. The traditional chess computer relies on a brute force approach of trying every possible move and evaluating the position to see if it is good or bad. This is a very effective way to play chess, but it is also very slow.

A simplified decision tree of a chess engine, using a Minimax algorithm

AI Chess Engines Powered By Neural Networks

Newer chess computers are using neural networks to try to improve on this. Neural networks are a way of teaching a computer to recognize patterns. The machine learning technology allows modern chess engines to adapt to different types of play, and compute the highest accurate moves at a much faster pace.

AlphaZero for example trains entirely through reinforcement learning and self-play to avoid outside dependencies.

Another great feature of some AI Chess engines is that they can use natural language processing (NLP) to learn to perform more humanlike moves. Something that traditional chess engines are often criticized for.

An example of CharlesChessAI Architecture


One of the biggest differences in understanding between older and newer engines can be found in strategic middlegames which involve long-term improvements by one side. As shown in many of the AlphaZero — Stockfish games, the older engines sometimes fail to see dangers due to their limited foresight.

Relying solely on move-by-move calculation is not always enough to solve problems against the strongest opponents. This is because neural network engines excel at slowly building up pressure, making small improvements to optimize their winning chances, before gradually preparing the decisive breakthrough.

“How The AI Revolution Impacted Chess”. Source: ChessBase

Final Thoughts On Chess Engines

When chess engines became a powerhouse that could take out even the strongest grandmasters with no problem they were widely hated. One main reason for that is the fact that despite making highly accurate moves, their style of play was so off from what a human would play. The 2013–2023 world chess champion Magnus Carlsen even said that “playing against an engine is like playing an idiot, and then he wins.

In spite of that, chess computers became an inseparable part of every top player’s toolset and the constant development of new engines only improves the aspect of the sport as well. The most modern engines rely either on 100% neural network architecture or a hybrid of it with some traditional algorithms.

The biggest impact that AI made in chess is probably in efficiency. In a game where there are more than 1⁰¹²⁰ possible games (more than the atoms in the universe), having a method to effectively and efficiently find the best moves is everything.

The most modern AI engines can also learn to imitate the human style of play, so they can be a good partner to play against as well (opposite to old engines that just make you feel stupid.)

Google’s Breakthrough In Sentiment. Next Steps In AI.

In June 2022, Google made a breakthrough in Sentiment. Google says The Language Model for Dialogue Applications (Lamda) is a breakthrough technology that can engage in free-flowing conversations. But engineer Blake Lemoine believes that behind Lamda’s impressive verbal skills might also lie a sentient mind.

The fact that Google denied the later claims by their work and even fired him in July 2022, rises more questions and conspiracy theories than ever. We are wondering how far has AI reached in sentiment analysis exactly.

Other Breakthroughs In 2022 In AI And ML Include:

  1. Amazon: Amazon’s sentiment AI and ML analysis tools help businesses track and understand customer sentiment in real-time.
  2. The success of AlphaFold program could solve global problems such as famine and disease
  3. DALL·E 2 is a new AI system developed by OpenAI that can draw realistic images and art based on a text description you provide.

If sentiment analysis keeps developing at this pace, soon it can become an addition to wealth management firms that use AI for investing purely based on price action analysis (eg. One Button Capital). Adding a market sentiment AI on top will give the trading robots a whole new dimension of decision-making.

A.I Timeline or Understanding Artificial Intelligence. Source: David Alayon

Now that we know where we are at the development of AI technology it is time to look at how the wealth management industry has used the available tools in order to tame the market in its favor. At the time of research, the best AI trading firms rely solely on neural network architectures that analyze price data.

From our paper on the “Richest Gamblers (Investors) In The World That Used Nothing But Math” we know that this can be a very effective way of investing.

A.I. In Finance And Wealth Management

A.I. in finance is already providing huge benefits to banks and other financial institutions. It is being used to automate the tedious and time-consuming tasks of financial analysis, including the identification of trends, the assessment of risks, and the generation of predictions.

This is freeing up human resources so that they can be deployed to more strategic tasks, such as developing new products and services or providing better customer service. A.I. is also being used to develop new financial products and services. For example, it is being used to create “Robo-advisors” that provide personalized investment advice to individual investors.

Source: Million In$ights

Robo-advisors are able to provide this advice because they have access to vast amounts of data and can analyze it quickly and efficiently. They can also provide services at a lower cost than traditional human advisors. In the future, A.I. is likely to play an even bigger role in finance and wealth management. It will help financial institutions to become more efficient and to better serve their customers.

Source: Million In$ights

The Edge Of AI Robo-Advisors

The concept of an automated trading system was first introduced by Richard Donchian in 1949 when he used a set of rules to buy and sell funds. Then, in the 1980s, the concept of rule-based trading became more popular when famous traders, and in the mid-1990s, some models were available for purchase (sounds familiar?).

Now in the 2020s, we got AI Robo-advisors. What gives them the edge against humans and even traditional algorithmic automation tools is reinforcement learning. With its help, the most modern trading bots are capable of using historical data to create unique strategies and swiftly adapting to the constantly changing environment and finding patterns that no one has ever thought of before.

Neural network based vs. rule-based trading. Source OBC Medium

A New Way To Invest:

One Button Capital is the first company to provide a wide range of crypto investing strategies that are 100% based on machine learning and neural networks. Since its creation in 2020, the firm has outperformed the market by a steady +3.62% monthly and is currently working on adding two more investment vehicles (first in their class).

Performance of the OB Capital Fund. Source OBC Website

One Button Capital uses tested investment management frameworks from traditional finance and combines them with big data, artificial intelligence, and machine learning technology to gain an edge in cryptocurrency investing.

The platform is:

Fully Automated: asset management is fully managed by AI-driven tech with ZERO human intervention.

Scientifically Driven: developed by a team of technologists, scientists, and product managers, the firm uses a scientific approach to investing.

Consistent Alpha: contrary to the majority of crypto funds, we generate both absolute and relative returns higher than the market for our investors since 2020.

Read the full presentation.

How It Works

All the trading and portfolio management is done purely by the AI-backed models. The models are using recurrent neural networks and reinforcement learning for maximizing returns while trading cryptocurrencies.

Architectures used in the models include the ones also used by major tech companies Amazon, Google, and Facebook for data processing and analytics, such as LSTM, GRU, Performer (Transformer), GMLP, Filter, and others.

All the models are developed in-house by the One Button Capital research team.

Read the full Whitepaper.


Artificial Intelligence has come a long way and has already impacted countless industries. The real scope for the wealth management sector is yet to be seen, but we can safely look at the history of chess computers to see where we are at, and what we can expect to happen in the future.

While it took about 50 years for a chess computer (since the first one) to beat a world champion, it only took 20 more years for a neural network chess engine to take over the crown.

In finance, the concept of an automated trading system was first introduced around the same time and the development since is pretty consistent with that of chess engines. The 2020s will undoubtedly be the turning point for AI-driven wealth management.

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Danail Velchovski

Danail masterfully combines his deep knowledge of blockchain technology and his strong writing skills to deliver crisp, comprehensive content. With his early immersion in the web3 domain, he navigates the complexities of this revolutionary technology with ease, turning intricate concepts into engaging, digestible pieces. His research acumen and keen insight into the rapidly evolving world of decentralized networks make him an invaluable asset in educating audiences about web3's potential and its ever-evolving landscape.

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