AI contribution to Trading and Shipping: a myth or reality?

There is a lot of hype around AI in business. Trading and Shipping are no exception. AI contribution ranges from trading operations to regulatory compliance, however there are downsizes too.

What is artificial intelligence?

AI is defined as “the science and engineering of making intelligent machines.”- John McCarthy, 1956. It is based on several foundations such as mathematics, statistics and neuroscience. In 1956, a programme called Logic Theorist was the first artificial intelligence programme created. It has proved 38 of the first 52 theorems in chapter 2 of the Principia Mathematica.

However, rapid advances in data storage and computer processing power have significantly increased the rate of acceleration of artificial intelligence. Recent examples of advances of artificial intelligence include: the development of AlphaGo by DeepMind, which defeated one of the best human players at Go.  Nowadays, most of us have encountered AI using devices and applications such as smart phones, Google assistant, Amazon Alexa and chatbots.

AI market value is expected to reach $60 billion by 2025 with the race between the USA and China being the most prominent. AI is expected to be used in different sectors such as:

  • Natural language processing for voice recognition.
  • Medical diagnosis and computational biology for cancer detection and drug discovery.
  • Finance applications such as stock trading and credit scoring.
  • Image processing and computer vision.
  • Automotive and manufacturing applications such as autonomous cars and predictive maintenance.

Machine learning is a subset of artificial intelligence and it is a data analytics technique that helps computers learn from experience. Machine learning algorithms find natural patterns in data, which helps generate insight and make better predictions and decisions.

Examples of AI and machine learning application in the financial services include:

  • Customer engagement: many banks have introduced digital assistant/ chatbots to enhance customer experience.
  • Fraud detection and risk management:  AI is being used to detect fraud before it takes place. This is usually done by predictive analysis based on machine learning.
  • Investment and stock markets prediction: AI is automating asset management with the introduction of robo-advisors. Also, AI is used to predict market movement and analysing the market impact of trading positions.
  • Credit assessment: AI and machine learning is used to asses credit risk for loans, mortgages and contracts.

So how can AI benefit Trading and Shipping?

With the huge amount of data available in both trading and shipping operations, there are ample opportunities to apply AI. AI contributions could be used in different areas including:

  1. Trading operations

Automated trading algorithms have been used for a while in trading. The introduction of AI has improved costs and speed further. One example of an AI driven investment platform is Wealthfront robo-advisor that is using AI to manage over $3 billion in assets. Another example of value enhancement is Sapien bots that can automate at scale several processes such as account payable processes.

Smarter contract is another area where AI can be applied. Contracts generation requires a lot of effort and review. AI can make contract tracking and monitoring more impactful and meaningful making it easy to operate within the contract scope and minimise legal risks.

  • Routes and supply chain optimisation

Trading and shipping operations are intensive in terms of process and administration.  AI allows for a proactive supply chain management, which helps identify least cost routes and anticipate supply disruptions. This could lead to significant savings as there are more than $4 trillion goods shipped every year.

One example of efficiencies driven by AI is the partnership between Hong Kong shipping online with Microsoft AI research centre which accumulated a $10 million USD cost saving. Another example is Sea machines solutions that uses sensors to enhance awareness on the environmental surroundings of ships. This is very useful for identifying potential conflicts.

  • Regulatory compliance and Financing

Compliance is another area where AI can bring significant benefits. The amount of regulations across the world is significant, having to go through every piece of regulation and legislation is time consuming. However, complying with regulation is paramount for trading business.  The AI learning capabilities helps reduce the margin error for spotting false positives and false negatives thus reducing the need for human review and interaction. With AI helping to review compliance it becomes easier for financial institutions to lend to traders as they are most of the time concerned about trade regulation compliance.

Above are some of the benefits of using AI in Trading and Shipping. However, there are some challenges and issues when it comes to applying AI. The urgency of tackling these challenges is critical to how quickly AI could penetrate the industry.

Addressing the elephant in the room

Despite all the promises of AI and its significant potential, AI is still in its infancy and there are several concerns that need to be addressed including:

  • Lack of transparency: Not all AI and machine learning techniques are interpretable. One specific example is deep learning that has in one hand dramatically improved the accuracy rates of AI applications but on the other hand has made it almost impossible to explain how outcomes are derived.
  • Data quality: AI and machine learning techniques can be heavily reliant on the quantity and quality of data at hand. Good quality data is needed to train the models. The level of accuracy depends on how good the data is. Furthermore, human knowledge expertise may still be needed to interpret some of the data. There are instances where the machine may find it hard to identify relationships between variables.
  • Learning process: AI and machine learning process is based on historical data. It is very good to project and predict outcomes based on historical data. However, it is not good at predicting unusual events and outcomes which did not take place in the past. Again, this is another area where human knowledge expertise could be useful.

There are other future issues to addressed as technology progresses such as market singularity. The idea gives rise to a super AI application across the board that will benefit a privileged class of investors. However, you will be pleased to hear that technology is not yet there!

 So, what is the future of AI in Trading and Shipping?

There are so many opportunities to leverage across Trading and Shipping. AI solutions could improve a range of activities starting from trading operations, routes & supply chain optimisations to regulatory compliance & financing. However, there are also shortcomings such as lack of transparency, data quality and historical learning process.

The future of AI in Trading and Shipping is bright, but we need to address the shortcomings now. Human knowledge expertise is critical to the process. The future looks like humans and machines working together.

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