Artificial Intelligence Machine Learning

How Artificial Intelligence can augment decision-making in capital markets

With more than 75% of the worldwide trades being handled by algorithmic trading systems, the question that traders need to ask is not “Whether to use AI?”, but “How to effectively use AI for financial investments & trading?”

If used in the right way, Artificial Intelligence can help maximize RoI at the lowest risk level possible. Also, traders have definite trading hours. The time spent on data gathering and analyzing can be freed up for high-value decisions based on insights.

How? You might wonder. Capital markets are basically, high-volume data banks. The stocks in dematerialized form, insurance products, debt bonds, mutual funds, accrued interest, capital appreciation, loans and almost everything else related to the capital market exists in virtual form.

The capital market is a gigantic data bank that is made up of data about investors and their financial assets. And that data creates tremendous potential for Artificial Intelligence to drive better decision making in the industry.

How can Artificial Intelligence transform capital markets

AI technologies like Robotic Process Automation (RPA), Machine Learning (ML), and Natural Language Processing (NLP) can help ‘solve the stock market’ for traders. By solving the stock market we mean, harnessing the large volumes of data that capital markets churn out to build AI algorithms.

These algorithms can do a variety of tasks like predicting stock prices, automating routine processes that are done manually or even simplifying the chore of fetching data and preparing it for analysis. According to PwC’s Finance Effectiveness Benchmark Report 2017, “40% of finance effort be aligned with more value-driven activities through automation.”The report depicts 17 best opportunities in finance to automate that can save time, effort and improve efficiency.

From a bird’s eye view, all these best opportunities would help traders spend less time to do routine tasks and devote themselves to priorities that demand their intense focus and attention.

Here are some real-life examples that show how AI can be an ally for traders and investment bankers:

Document Review Using NLP

In a global bank like JP Morgan, lawyers and accountants would spend anywhere from few hours to days to review and approve a financial deal. A data-trained ML system can look through these documents in a matter of seconds with higher accuracy than its human counterparts. Bloomberg reports that the AI system saved 360,000 hours for the bank as well as helps put across more than 12,000 new wholesale contracts per year.

Chatbots For Account Balance Checks

Traders and investors want a real-time update of their stock prices to make the right move that will make them a fortune or avoid losses. Under the manual process, the trader picks up stock prices from the stock market indices uses a combination of mathematical and financial formulae to arrive at investment decisions. This process is time-consuming and is often to human errors. AI can fill the gap here by providing real-time stock alerts.

A fine example of is TD Ameritrade’s Facebook Messenger bot allows users to move funds, receive alerts about their stock performance or even access live customer support from online brokers.

Additionally, AI and Robotic Process Automation will help traders to automate their calculation process to arrive at conclusions quickly. This would also remove the errors that used to be rampant earlier. The result would be faster number crunching and informed decision making that would lead to higher RoI.

Financial prediction using Artificial Neural Networks

How the stock market will behave shortly is uncertain even for the seasoned traders. But, studies in Stock market index prediction using artificial neural network prove that Artificial Intelligence and machine learning can predict the stock market indices with impressive accuracy.

This would be a game-changer for asset management companies and public financial institutions who do not want to make big bets with public funds. For private traders, this will facilitate taking calculated risks that will maximize their RoI.

Financial Reports Using NLP

Traders rely on financial reports of enterprises to decide on their buy, hold or sell strategies. But, financial reports are sophisticated documents and are hard to read through. For the layman, it is a maze of numbers printed in neat rows and columns. Further, these financial statements are prepared and presented in adherence to global accounting standards, regulatory requirements and according to tax reporting procedures.

This rigidity in preparation and presentation makes financial reports a perfect dataset that can be analyzed with the help of NLP (Natural language processing) and predictive analytics. The AI system can be trained with data models to come up with forecasted figures of net earnings, earnings per share, profit margin and much more. Trader and investors would be able to craft a future-oriented investment strategy based on this projected figures.

Artificial Intelligence is changing the face of every industry and the capital markets are no exception to it. PwC reports that top performers in the finance industry who leverage technology spend 20% more time on analysis than data gathering, operate at 36% lower costs and achieve 35% to 46% reduction in inefficiencies. Of course, new entrants have lessons to learn from early ML adopters that will advance their plans.

As Richard Craib, Founder of Numerai says, “The promise of artificial intelligence in finance is total efficiency and the freeing up of human capital to advance other fields or do whatever they want.”