TMCnet Feature
October 19, 2021

How is AI Shaping the Future of the Stock-Market and Investment Banking?

Artificial Intelligence (AI) is being used alongside natural intelligence to pave the way for future generations, and certain industries such as investment banking are taking full advantage.

In just a few short decades, the stock market and investment banking sectors have become reliant on AI to power their data aggregation, security, authentication, and front-facing products and services.

These new AI technologies also help prevent investment fraud, predict the movements of the stock market and process trades automatically.

In this post, we’re going to describe these technologies in detail and give you some examples of exactly how AI is shaping the future of the stock market and investment banking.

In What Ways is Artificial Intelligence Shaping the Future of the Stock Market?

The primary function of AI technologies is to automate the simple tasks that would take analysts hours to perform, freeing up time for them to work on more complex tasks.

As machine learning-based AI technologies become more intelligent they can automate more complex tasks and might one day be able to trade stocks without brokers having to lift a finger.

We’re not quite at that stage yet, but the following are some of the ways AI is redefining the investment industry.

1. Improves Market Data Collection

Investment associates, Wall Street brokers and all types of market data analysts are expected to absorb news and data on their investments the minute they break. This takes up a lot of time and data is often rife with errors that could influence a trader to make an incorrect decision.

However, the implementation of a simple AI system removes the threat of information overload, by systematically collecting, analysing, and classifying news that a broker needs to be aware of at any given time.

AI systems being used in the investment banking industry today are advanced enough to autonomously verify and cross-reference data inputs to ensure that there are no data anomalies and that the data is valid, accurate and of the highest quality.

In terms of actually using this technology yourself, there are consulting services that offer AI market data collection to investment professionals. Sigmoidal, for example, uses web scrapers to rapidly aggregate data from several sources which is run through AI text classification software to isolate relevant data.

One example of a company using this technology is the market data firm Refinitiv, which partnered with the AI firm Squirro, to integrate proprietary customer data, market data and augmented intelligence. The company has claimed that this helped them generate useful real-time insights on their various investment portfolios.

2. Automates Trade Processing

Not only can AI help investment brokers save time collating useful information and eliminating errors, but it can also actually do a lot of the trading for them.

AI technologies such as Smart Chaser utilise predictive analysis to automate trade processing. When a trader places a buy or sells order, the system finds the best route, breaks the trade into smaller components and proceeds to move it in the most optimised way possible.

These trades are monitored in real-time by machine learning algorithms that automatically reject your trades in favour of speedier alternate trading routes. This can make the process slightly labour intensive as when the trade route fails, the issue must be settled manually.

Thankfully, most of the modern AI trading systems use a pool of historical data to identify why trades were rejected in the past. The technologies apply that logic to new instances, essentially cutting out the manual element as the system learns from its past mistakes.

With trading solely delegated to intelligent machine systems, more free time is available to the investment professional for portfolio management, strategy development and other more complex tasks.

You can create your own intelligent trading system using Python or hire the technology from an already existing company.

3. Predicts the Success Rate of Trades

Perhaps the most interesting use of AI technology in investment banking is its ability to predict what the stock market is going to do.

This technology uses historical data to predict future scenarios under certain specified conditions. Smart Chaser, the AI technology we mentioned in the last section, can use historical data, much the way it does to correct errors in trade routes, to predict bad trades.

It uses records of trades made through its client companies, identifies patterns that led to failures, and predicts the likelihood that a particular trade might need manual intervention in future. Interestingly, one of Smart Chaser’s primary clients, BNP Paribas, said the technology had a 98% prediction accuracy for all of its trades since introducing the AI.

Other AI technologies such as ING’s Katana tool, can learn from historical and real-time trading data to produce a statistical forecast that traders can use as a reference alongside their natural trading skills.

All of these predictive technologies are changing the shape of the stock market and investment banking and will continue to do so in the years to come.

4. Automates Regulatory Compliance Procedures

Solving the increasing regulatory compliance required when processing large data loads is a time-consuming manual process.

As with all the other menial, repetitive investment banking tasks we’ve discussed in this post, AI can automate it for you. With the application of natural language processing and robotic process automation, almost all reporting processes traders engage in can be simplified.

AI technology does this by finding duplicates, correlation, and disambiguation within data that a trader would’ve had to spend hours doing manually. The benefits of this technology to investment professionals are:

  • Improves risk management decision-making with real-time data
  • More cost-effective than manual workflows
  • More availability of compliance status and risk position from the main dashboard
  • Improves risk management decision-making with real-time data
  • Reduces the probability of security breaches
  • Less reporting mistakes
  • Better accuracy and compliance reporting
  • Performs uninterrupted verification of compliance requirements, manages third-party risks, and identifies potential vulnerabilities

All in all, this AI technology just makes your life as an investment professional a lot easier.

Will AI Continue to Influence the Stock Markets and Investment Banking Sectors of the Future?

In this post, we’ve discussed the various ways AI technology is improving processes within the investment banking sector and the stock market.

Despite being so successful in such a short space of time, these technologies are relatively new to these industries and as AI improves in the future, the adoption of these technologies will become more widespread within the industry.

This will inevitably lead to more complex tasks being taken over by machine learning AI systems and might eventually render the investment banking and stock trader professions obsolete.

Please be advised that this article is for general informational purposes only, and should not be used as a substitute for advice from a trained financial professional. Be sure to consult a financial advisor if you’re seeking advice on your finances. We are not liable for risks or issues associated with using or acting upon the information on this site.

Written by Nicholas Matthews

Nicholas Matthews is a freelance journalist specialising in fintech. With 15 years-experience, Nicholas enjoys writing about artificially intelligence, digital wealth management, data analysis for business decision making, and cryptocurrencies.

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