Role of artificial intelligence in banking
AI enhances efficiency by automating routine tasks, providing quick responses, and reducing the workload on human agents. Traditionally, banking operations were manual, time-consuming, and prone to human error. AI technologies, including machine learning and natural language processing, have automated routine tasks such as data entry and transaction processing, significantly reducing operational costs and errors.
Growth in Trillions
AI-enabled digital platforms will surge to almost US$6 trillion by 2027, nearly double the figure for 2022. This trend signals a broader one across other aspects of the asset and wealth management (AWM) industry, with more managers experimenting with generative AI in the middle and back office, and using it to enhance trading strategies and analyse unstructured data.
Generative AI in Asset Management
Generative AI has revolutionized asset management by enabling the simulation of thousands of investment scenarios, allowing managers to design portfolios that align with each client’s risk profile and time horizon. It has also expanded the use of alternative data, enriching analysis and offering a more comprehensive market view.
This technology detects patterns in vast amounts of historical and current data, facilitating trend prediction and risk assessment. AI-driven automation generates significant economies of scale. While initial investment costs may be high, the reduction in human error, greater agility in analysis, and increased efficiency in internal processes make it cost-effective in the medium term. This enables asset managers to seize more investment opportunities without compromising decision quality.
Regulatory compliance
In a banking call centre, AI tools automatically monitor and analyse every interaction, identifying potential risks and flagging non-compliant behaviour. AI helps you stay ahead by keeping track of ever-changing regulations, ensuring your team follows the rules. This protects your organization from fines and penalties and builds trust with your customers. Using AI in banking, you can streamline compliance processes, reduce manual errors, and focus on delivering excellent service, knowing that your regulatory obligations are being met efficiently and accurately.
Trading bots
Trading robots, also known as algorithmic trading or automated trading, are computer programs that use mathematical algorithms to execute trades in financial markets. These programs are designed to analyse market data and make trades based on predefined rules and parameters, without the need for human intervention. They can be used for a variety of financial instruments, including stocks, bonds, currencies, and commodities. The use of trading robots has become increasingly popular in recent years, as they can provide faster and more accurate trading decisions, as well as the ability to execute trades 24/7.
What Are Trading Robots and How Do They Work?
Trading robots work by constantly monitoring market data, such as price and volume, and applying a set of rules to determine when to buy and sell. These rules can be based on technical indicators, such as moving averages or relative strength index, or on more complex mathematical models, such as artificial neural networks or genetic algorithms. Once a trade signal is generated, the trading robot will automatically execute the trade on the trader’s behalf.
The use of trading robots has become increasingly popular in recent years, as they can provide faster and more accurate trading decisions, as well as the ability to execute trades 24/7. They can also help to eliminate emotional biases that can affect human traders. Additionally, trading robots can scan multiple markets and identify opportunities that a human trader may miss, which can lead to better returns on investment. However, it is important to note that like any other investment, there are risks involved and past performance does not guarantee future results. Trading robots can also be subject to errors or malfunctions, which can lead to significant losses. Additionally, trading robots can be affected by market conditions that are not accounted for in the algorithms, which can lead to unexpected results.
Overall, trading robots can be a useful tool for traders looking to automate their trading strategy and increase efficiency. However, it’s important to be aware of the risks and limitations and to thoroughly test and backtest any trading robot before using it in a live trading environment. It’s also important to note that having a good trading strategy and risk management plan is crucial before using any trading robots.
Cryptography
Cryptography technology has become an essential component of the financial sector, underpinned by the principles of confidentiality, integrity and authenticity. This technology, which once primarily served military and diplomatic purposes, has evolved into a cornerstone of security in the digital age. Its historical roots trace back to ancient civilizations that used basic encryption methods to protect sensitive information. However, with the development of the digital economy and the expansion of financial markets, cryptography has undergone rapid and transformative innovation to meet modern demands for secure transactions. The development of cryptography in financial markets began to accelerate with the rise of electronic banking and online financial transactions in the late 20th century. As financial institutions transitioned from paper-based to digital transactions, they faced increased risks of data breaches, fraud and cyber-attacks.
Cryptographic techniques, particularly symmetric encryption, were initially adopted to secure communications within banking systems. However, as the volume and complexity of digital transactions grew, these methods alone could not withstand emerging threats. The introduction of asymmetric cryptography, also known as public-key cryptography, in the 1970s marked a critical advancement. This method allows secure communication between parties without needing a shared secret key, solving a major challenge for financial institutions by enabling encrypted transactions between different entities. With this capability, financial markets began implementing cryptographic protocols to secure data transfers and provide greater assurance of transactional integrity.
Digital Assets
A digital asset is generally anything created and stored digitally, is identifiable and discoverable, and has or provides value. Digital assets have become more popular and valuable as technological advances integrate into our personal and professional lives. Data, images, video, written content, and more have long been considered digital assets with ownership rights.
Most digital items, like a company's brand, can be assigned a value, monetary or intangible. Some digital items might only be valuable to the creator or one person, such as a family picture on your phone taken at a gathering. Others could be valuable to a much wider audience. In the past, digital assets such as data or scanned documents were owned and used by organizations to realize value. However, digital assets were again redefined when blockchain and cryptocurrency were introduced in 2009. Anything in digital form became something that could be used to createvalue via tokenisation on a blockchain.
Decision Making
Business leaders and managers face increasing pressure to make the right decisions in the workplace. According to research by Oracle and Seth Stephens-Davidowitz, 85% of business leaders have experienced decision stress, and three-quarters have seen the daily volume of decisions they need to make increase tenfold over the last three years.
Artificial intelligence (AI) has introduced many data-driven solutions in the publication process. AI tools can assist humans or automate tasks like journal selection, topic identification, reviewer suggestions, and statistical analyses. However, ethical concerns about AI emphasise the need for transparency, technical robustness, and rigorous data governance. It’s crucial to identify and address biases in data and tool design, with transparency regarding limitations.