Artificial Intelligence (AI) and Automation have a spate of applications in the finance sector. Though the AI space in investment banking is in its nascent stage, there are inroads for this technology in this particular industry.
To mention a few-
- Man Groups, an investment management firm uses machine learning to optimize trade routings through internal execution algorithms, external dealer algorithms, or the firm’s trading desk.
- BNP Paribas Securities Services uses ‘Smart Chaser’, a trade matching tool. It uses predictive analysis for trade processing service automation. They provide treasury finance and advisory services for investment managers and firms.
- Dutch banking services provider ING uses ‘katana’ or predictive analytics. It helps traders to decide quote prices based on historical and real-time data while buying and selling bonds.
- JP Morgan Chase uses AI to interpret loan agreements. Contract Intelligence AI system has saved them 360,000 hours of mundane work of customer data management and supported staff to focus on high-value delivery.
If AI dominates the investment banking industry, it might create an impact on sales and trading, corporate finance, and research. It will improve efficiency and cost savings through data interpretation, ease of use, and economies of scale. Some of the benefits of AI in investment banking are as briefed below.
Benefits of AI in Investment Banking
Some of the techniques applied in the investment banking industry include machine learning, deep learning, data mining, and, analytics. AI can get away with these issues in the industry.
Investment banking professionals use a large volume of complex data involved in due diligence, risk assessment, and monitoring. Automation leads to effective lending practices.
AI enables investment banking firms to adapt to the changing environment. It provides various inputs in the finance and banking system.
The manual repetitive tasks can get replaced by AI. It adds value to the customers by reducing the cost, increasing the accuracy levels and speed.
AI implementation provides specificity and consistency in operations. It addresses customer queries with reduced costs.
AI improves the quality of decisions and ensures better forecasting by evading or reducing the errors.
Moving forward, let’s deep dive into AI applications in various aspects of investment banking.
AI Applications in the Investment Banking Industry
A few of the AI applications in the investment banking industry have been emphasized here as follows.
Market data collection and interpretation
Artificial Intelligence platform helps investment banking professionals by systematically collecting, analyzing, and classifying news and sentiment in the market. The AI bots are capable of pulling information from human interaction and proactively request new data. AI-driven online surveys can be plugged into an analytical system for further analysis and interpretation.
AI systems remove anomalies and maintains high data quality by ensuring accuracy and validity.
An AI algorithm attempts to predict future scenarios. It validates hypotheses, identifies patterns, correlates large and disparate data sets for a given condition. This technical approach helps the investment bankers to have a predictive score for customer satisfaction or product marketing.
Further, it influences processes like fraud detection, credit risk assessment, operations, and, law enforcement.
The routing algorithms find a match from stockbrokers, exchanges and trading systems when a trader places a buy or sell order. The AI system uses the historical data pool, identifies the reason for trade fails, applies the logic for new instances, and comes up with a conclusion.
Risk and compliance
Departments have large data loads and evolving regulatory requirements. AI algorithms can create an iterative learning process, perform tasks automatically. It reduces manual repetitive and time-consuming processes.
The adoption of Artificial Intelligence algorithms provides better customer service experience. Some of the AI trends like lean and augmented learning, deep reinforcement learning, and generative adversarial learning can enable investment bankers to improve customer service.
However, low levels of maturity, infrastructure, technical complexity, and fear for manpower attrition are preventing investment banking from adopting the technology.
We hope that this scenario would change soon and investment banking will adopt AI at a faster rate for business benefits.