AI and ML should be commoditised for the financial industry, but implementation suffers from technology weaknesses, data inadequacies and human inertia
Big Data and Machine Learning have already changed the way we receive information. Advertisers no longer rely on billboards or television to reach us. The recent events surrounding Facebook, Twitter and Google have shed a very public light on the way those mammoths have been using the data of their users to serve the commercial and political purposes of paying third parties. By simply reading the user profile and applying Machine Learning tools, they can, more easily than ever, identify the user’s needs and wants, with an almost scary accuracy.
The pervasiveness with which Artificial Intelligence (AI) has come into our daily lives is indeed impressive. What if we could also apply this to trading and investing?
Actually, this is already possible. Some hedge funds and quantitative funds have already developed AI-driven trading bots. But how do we make it easily available for everyone?
In general, we would like to automate the following investing process:
- Idea generation
- Idea validation
- Trade execution
- Evaluate steps 1 to 3 to iteratively improve them. This is a very logical place for AI to help us improve our own trading biases.
Most of these processes could be automated with AI bots. We could implement bots that are able to identify changes in prices, volumes or even related news from reputable media outlets. Automation with bots will allow investors to be able to expand their trading universe without spending more time in front of a computer to find and analyse the information that an AI bot would be able to process more accurately and tirelessly. Sometimes we also need automation to ensure that we are not driven by our emotions especially when we have been monitoring a position closely for long periods of time.
Also Read: These 3 blockchain startups are building on AI to change our everyday lives
I believe that AI bots would be able to play a large role here to help our investors to be more reliant on automation and scalability of these bots to become better at their trade. It is almost like creating a trading team without hiring a team.
Next steps
For it to happen, there are still many challenges that AI will have to face before becoming commoditised and easily utilised by everyone.
The first challenge lies in ensuring the perfect understanding by the AI of the user’s request. To be used for trading, the AI bots would need to understand with 100 per cent certainty the demands of the trader. This 100 per cent capable AI bot is still far from being achieved. Even Siri is still struggling when you request the weather or the nearest gas station. Imagine letting Siri give trading orders to your broker…. I would still be afraid today.
The second main impediment for a good investing bot lies in the quality of the data and the data collection method. On the former, the current data available to the general public is limited and with minimal checks to ensure validity. If the source of data is not good or the data sets are corrupted, there is no chance for the AI bot to operate a successful investment strategy. In short: ‘garbage in, garbage out’.
On the latter, we replicate the data collection processes over and over again between the different data vendors, hedge funds, individual traders and big institutions. Wouldn’t it be to the benefit of all to have a single data collection and validation facility to be shared open source with all the market participants? I am appealing to all market participants to be “green” about how we collect and store our financial information. Currently, we are all doing it in a highly non-sustainable, non-environmentally friendly manner.
One of the main reasons invoked by market participants to run the data collection individually is the capability to differentiate themselves from the competition, by obtaining a “better” set of data. Well understood, but I think focusing on the raw information is the wrong battle. The focal point should be growing the capability to harvest the knowledge from the data. This would be akin to the commoditisation of computer power with cloud computing, so why not the data too? After all, data is merely oil within the engine of race car. A good driver is still necessary to ensure that you can reach the finish line.
New interfaces
The third obstacle resides in the user interfaces that currently exist. We see chatbots being used successfully in customer relationship management or as virtual assistants, but not yet in the area of finance. Why? First and foremost, we need a way to transform our financial industry’s reliance on outdated user interfaces such as spreadsheets, watch-lists and chart reading skills to identify signals.
Also Read: 7 fintech industry trends you should keep a close eye on
The next generation of traders grew up as smartphone users with apps and games that leverage touch screen interfaces and notifications. A simpler user interface that encourages idea generation, validation and sharing would be essential in the social media age. In general, most online brokers have perfected the user interfaces for execution but have neglected the investors’ discovery journey.
Once those challenges are solved, the next step would be to increase the complexity of the bots that one creates, by using Machine Learning tools to develop predictive capabilities and enhance simple regression analysis with neural networks
For now, what would make most sense is for everyone to get involved collectively and build community-based investing platforms so that all can benefit. Automation and machine learning is within the reach of many investors today because of the birth of cloud computing, open source code and pre-trained AI models. Let’s make it happen for all.
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The author T Kiang Tan is CIO of Grasshopper and Tilde Trading, and Founder of ChatQ
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