By Anand Chandra
A quick rewind: In 1956, a group of researchers gathered at Dartmouth College to discuss the possibility of creating machines that could “think” and “learn” like humans. This conference is considered to mark the birth of what we know today as “Artificial Intelligence (AI)”. Since then, AI has evolved from being an Expert System to Machine Learning to Deep Learning to present world Reinforced Learning. Overall, this evolution has been augmented by advances in computing power, the availability of large amounts of data, and breakthroughs in algorithms and techniques.
Capital markets have consistently cuddled revolutionary ideas and adaptation to new technologies, regulations, and market conditions. AI has been progressively used to improve informed investment decision-making, enhance risk management, generate data insights, and optimise trading strategies. In more recent times, the spectrum has been amplified to detect anomalies in trading avenues & booking patterns, predict market volatility, identify potential market crashes based on real-time feeds, fraud detection, anti-money laundering patterns, systemic monitoring of trade life cycle, real-time cross-border jurisdiction compliance and infrastructure innovation supporting trading services.
Staying ahead of the financial curve
Wealth & Asset Management has evolved as a business together with the advancement in AI, bringing in a more innovative way to acquire new customers by leveraging its finer aspects like Augmented/Virtual Reality (AR/VR), Internet of Things (IoT), and Sentiment Analysis. Corporate & Retail Banking has been on the leaderboard when it comes to AI usage for maximising wallet share & mind share via Contactless Payments, Digital Wallets, Biometric Authentication, Facial Recognition Payment Systems, and Biometric Card Payments, among others. Trade finance has had a stronger association with Blockchain technologies in comparison to AI/ ML as the use cases are more immutable customer references across Trade Services.
The evolution of AI models, deployment, and acceptability within the regulatory canvas has had a booming effect within the five listed functions. The realisation of the vision set in early 2000 in terms of frictionless payment transactions for micropayments by removing dependency on banks’ infrastructure is now a reality. Voice, NFC-based payments leveraging alternative authentication mechanisms have stronger acceptance at International Merchants at real-time forex rates. Large economies are now leading with Programmable Money with real-time settlements. Specifically, within Capital Markets functional landscape, the following have shown higher adoption:
- Payments, Clearing & Settlement
- Liquidity Fund & Cash Management
- Treasury Management
- Credit Origination
- Foreign Exchange
With this evolution of AI, there is increased emphasis on trust, security, risk & dispute management. AI evolution has enabled sharp acceptance of the technology as it enables scalability (horizontally scalable architecture), security (2-factor authentication, fraud monitoring, data localization, local data processing), reliability (100% uptime, self-healing systems, multi DC active-active setups), richer experience (reimagining authentication for frictionless payments, building trust, effective dispute resolution frameworks) & affordability (achieving unit economics through the cost of processing transactions).
Synergizing technology, driving results
Financial institutions have always been careful in evaluating and monitoring the use of AI in their operations to ensure it is being used effectively, ethically & within regulatory boundaries. AI evolution is pivoting speedier transformation of straight-through-processing (STP) around insights-based investment decisions, real-time fraud detections, new arbitrage & event-driven trading strategies, distressed portfolio optimization, reduced cost of acceptance & faster loan origination, credit risk assessment for merchants & customers and deriving multi-channel business intelligence in Mergers & Acquisitions. In addition to this, optimising margin financing and reducing the cost of financing for prime brokerage clients, identifying the most profitable securities to lend while contractually defining the optimal lending terms and pricing and analysing client data to provide customised recommendations based on their trading history, risk appetite, & investment goal are some of the changes driven by AI.
In a nutshell, the use of AI in capital markets has the potential to improve efficiency, accuracy, and returns, while reducing costs and risks significantly further. However, it would be interesting to see which evolution is at the driving seat, whether it’s Capital Market’s need for AI or further innovations in AI that can service Capital Markets’ customer experience. Whoever evolves faster, the equilibrium would be steered by the human capital to drive the synergy between the two, as AI is not a replacement for human judgement and expertise; some would surely debate it, though!
The author is mergers and acquisition leader, Accolite Digital
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