Capital Market has generally been dependent on intuitive prediction. Leveraging AI may help in transforming this archaic model.
Artificial Intelligence (AI) has been a trending buzzword for a decade. And it is here to stay. In these years, AI has successfully, transformed different segments of industries like self-driving cars, manufacturing and healthcare and so on. Doing so, it has opened up many avenues of our lives to indefinite, previously unknown possibilities. Although AI is an umbrella technology, comprising of many technical applications acting as catalysts for innovations and discoveries for the advancement of science. And now AI is set to revamp the financial industry, including the capital market.
AI offers a string of benefits to financial institutions, for instance, improving operational efficiency, cost reductions, enhancing data and analytics, increasing revenue generation, updating legacy IT systems, and augmenting client services. AI in the capital market can help in data mining, quick and efficient data analysis, and ultimately deliver insights to customers to provide them with a competitive edge. This will be quite different from the previous traditional model that relied on the knowledge and intuition of the professionals in the field. This prediction has based on data in the public domain, i.e. cash flows, growth rates, multiple earnings, and historical trading ranges. Thus culminating in the sub-optimal matching of investors and investments, more prone to errors, lesser efficiency of capital markets, and performance, both for investors and corporates.
The main drivers of the capital market have always been, technology, regulation, and financial policy. While regulations and monetary policies often fluctuate with market demand trends, technology can push for consistent growth. This is where AI can step in and step up the game! It can automate the procedures so that it aligns pricing within the market context. By doing so, exotic pricing models, firm desk broker hierarchy, and derivatives trading are eliminated. Using machine learning application of AI, one can extract any shreds of price information relevant to a particular bond that may be embedded within other sources such as CDS index prices. Other than that, AI can also analyze data on external factors such as social trends, regulatory changes, and global economic impacts for higher accuracy.
Further, AI can introduce new facets like automating deal matching, investor matching processes, automated trade reconciliation, fraud detection, and improve the accuracy of financial models. By automating sales and trading activities, it empowers customers and traders to take control of stocks by purchasing and selling them at a predefined price. Besides, AI can carry out the trading process autonomously, independent of the user’s preferences. Even risks can be minimized by designing an AI program for a maximum amount of stop-loss at a decided percentage.
According to a study published by research and consultancy Opimas LLC., capital market firms are forecast to spend US$2.8 billion by 2021, globally. Cognitive analytics and machine learning solutions will form a significant share of this investment. Banks like Deutsche Bank are already deploying AI to cut costs and increase the accuracy of its compliance efforts. It is focusing on analyzing voice and video data, especially in client interaction. Major players like Goldman Sachs and S&P Global are employing Kensho’s AI model, which scans vast data sets much more quickly and accurately than analysts and sells the information to banks and other financial institutions. San Francisco based hedge fund uses AI for encryption of trading data. It also allows contributors to present them with a successful AI algorithm, and get paid in Bitcoin later.