AI and automation are becoming an increasingly vital aspect to explore in fixed income business as fully electronic trades are estimated to be 260 times cheaper as opposed to manually settled trades. The major banks have already made big strides in digitizing the stock market business.
Technological advancements in recent years can now facilitate automation of fixed income business by leveraging artificial intelligence and machine learning. Some big players in this area have already been experimenting and launching solutions in this field since 2016. Among others, a large European bank leverages predictive analytics to help traders in price discovery, and another leading American global bank leverages AI to provide real-time analysis of clients’ activity and recommend suitable trades.
The graph below shows the distribution of banks across different regions implementing AI-enhanced trading and pricing across different asset classes.
Until now, only the largest global banks have been pioneers in exploring ways to leverage AI and automation in different financial markets and asset classes. We believe, now is the time, for others to not only follow suit but to take the lead.
Bringing the AI-enhanced Fixed Income Trading to Life
The potential for AI and automation in this field is yet to be fully harnessed. The following graph presents our point of view on how robotics, AI and human traders can come together to streamline fixed-income trading.
Through the synergy of human traders, bots and AI, a large amount of the process may be automated and streamlined. Humans excel in non-standardized judgment as opposed to bots and AI. Bots are precise and fast in carrying out repetitive tasks and AI is best leveraged to process vast amounts of data and in mining accurate recommendations.
Furthermore, in price discovery and margin optimization, especially for illiquid bonds – one of the most challenging and time-consuming tasks in fixed income trading, AI can support in interpreting market sentiment by analyzing vast amounts of unstructured data, examining and predicting order situation, for example, estimating the demand and supply of certain securities and cross-checking with the trader’s current portfolio or interest. By receiving this information in a streamlined and comprehensive manner, traders can make faster, authoritative and more efficient decisions, free from guesses and instinctive choices.
In our view, 3 key objectives can be achieved by implementing this process:
- Facilitating price discovery, especially for illiquid assets
- Margin optimization through price recommendations and the customer willingness to deal with that price, as well as alerting the trader about potentially risky activities, such as checking long and short positions between two strongly correlated securities
- Automatic post-trade hedging strategies
The time is now ripe to ride the waves of digital disruption. Traditional banks can seize the opportunity thanks to the recent advancements in unstructured data analysis and the enrichment of their stochastic models with AI/ML-based frameworks.
Associate Partner, Infosys Consulting
Daniela has more than 18 years of experience in the banking industry and heads up our capital markets business in Germany. She joined Infosys Consulting in 2017 from Sopra Steria Consulting where she was a business unit manager and prior to that, she was a director at SQS. Daniela started her career as a banker, working for LBBW and Dresdner Kleinwort Wasserstein, before moving to consultancy.
Principal , Infosys Consulting
Elif has more than 10 years of experience in capital markets and leads the front office, treasury as well as the future technologies area of our capital markets business in our German Financial Services practice. Over the course of her career, Elif has had extensive experience in pricing interest rate derivatives, implementing regulatory requirements, and leading teams in strategic product development for the front office and treasury business. She specializes in advising financial institutions in defining their digitization strategy leveraging AI/ML and automation.
Gevorg has more than 8 years of experience in digital transformation and Consulting. He currently focuses on AI-powered digital banking platforms for our German Financial Services practice. He specializes in strategy and digital product design leveraging AI/machine learning and automation. He complements our financial projects with strategic approach development including our proprietary 32E innovation and design approach and supports our innovation hub team with use cases for “Next in Banking”. He holds an MSc. from the Goethe University in Frankfurt and a Licence from IAE Jean Moulin Lyon 3 University.