By Meggie Grimaud, Head of Analytics, Treasury & Capital Markets, Finastra
The applications for generative artificial intelligence (GenAI) are gaining traction across all industries, and the financial-services industry is no exception. From fraud prevention and chatbots to analyzing environmental, social and governance (ESG) data, the technology’s use cases are vast and reshaping the industry as we know it. In particular, it is showing great potential in bank treasury management.
Technology and automation are crucial in modern treasury functions, enabling banks to replace their manual processes with sophisticated software solutions, applications and automation tools to streamline operations, improve efficiency and ultimately enhance decision-making processes. With its ability to learn from data and produce new content, GenAI is becoming a major driver of efficiency and automation, creating new opportunities for treasurers to analyze risks better, evolve with changes, make more informed decisions and generate new growth opportunities.
Applications of GenAI in bank treasury
Major use cases for GenAI in a bank’s treasury department include analyzing, predicting and navigating market trends. Financial markets can move extremely quickly, and the window to take advantage of opportunities while mitigating risks can be small. Particularly as black swan events become more frequent, treasurers are navigating more hurdles than ever before, ranging from pension performances, interest rates and inflation fluctuations to global pandemics and major political unrest.
For example, when events such as COVID-19 occur, banks must understand sentiments and positions in the market quickly, assess potential impacts on liquidity and investments and make fast decisions, such as around hedging. Banks need real-time treasury to assess the market, navigate disruptions and take advantage of growth opportunities, and this is where AI, and GenAI in particular, plays a big role.
By ingesting large volumes of historical data and analyzing current events that may impact decision-making—including decisions made by the Federal Reserve (the Fed), trending news articles and social-media sentiments—AI can predict market movements in real-time with an increasing amount of accuracy. Treasurers can then make more informed decisions quickly about how to meet liquidity and cash-management requirements, adapt investment strategies (including meeting ESG criteria) and mitigate risks. It also provides better trend analyses about a bank’s customers, their behaviors and their cash-flow positions.
Embedding natural language processing (NLP) capabilities within interactive workflows and utilizing GenAI-powered assistants can further streamline processes and enhance user experiences. Large language models (LLMs) are quickly becoming essential tools for trading and analysis, providing quick, straightforward and user-friendly access to information. Traders can leverage these models to obtain real-time market-data quotes and transaction details directly within their existing platforms. Additionally, APIs (application programming interfaces) can be deployed to automate various tasks, including booking trades and calculating risk measures, to create seamless processes while boosting productivity and accuracy.
With GenAI, traders can receive tailored recommendations, such as suggestions for hedging strategies, investment opportunities and risk-mitigation tactics. These models can also anticipate the optimal times to roll over positions, ensuring that traders make informed decisions at the right moments. By integrating these advanced capabilities, banks can enhance their trading operations, reduce manual workloads and improve overall decision-making efficiency.
Another strong use case is fraud detection, which is a top priority for banks, particularly as services and functions increasingly operate in real-time, leading to increased risks of fraud. AI and GenAI can strengthen a bank’s ability to identify suspicious transactions and fraudulent activities in real-time. Through synthetic data, banks can also analyze and plan for future fraud scenarios that may not have happened before. Ultimately, this means they can minimize losses and ensure greater protection for their assets and their customers.
The technology is not without challenges
When using GenAI for real-time data analysis and decision-making, institutions face challenges. For one, GenAI is only as good as the data fed into it. The quality of the data will define its applications. For treasury traders and managers, this needs to be extremely high quality if they are to make timely and important decisions based on the insights shared by the GenAI tool. Banks also need teams of people—both externally and internally—who can understand the decisions made by AI, how it works and the patterns it identifies, and then test it on an ongoing basis to ensure continued accuracy.
There are other challenges around data governance, regulations and privacy, particularly regarding data flows throughout a bank’s treasury-management ecosystem. This starts with treasury, then flows through middle-office risk management and back-office processing, with IT (information technology) at the center of it all. In a highly regulated market, ensuring robust security and data privacy throughout all processes and departments is critical. This is why banks must work with technology providers that can help them do this safely and in compliance with both internal policies and external regulations.
Finally, we must remember that the market for AI is maturing, and regulators are still finding their feet. Especially if banks are implementing GenAI as a strategic direction for the treasury function, open conversations with regulators and compliance teams are crucial to ensure they do this in a safe way for themselves and their clients. The rollout of any new technology needs to be embedded in strong governance, and GenAI can’t be adopted as a “big bang” approach. As the market develops, new systems, software and regulations will continue to develop, too.
Collaboration is crucial for success
Collaboration is crucial for the successful adoption of GenAI in bank treasury functions, especially in markets with diverse regulations. To fully leverage GenAI, banks, technology providers and regulators must work together to balance customer-service excellence with regulatory compliance. Finding the right partners and secure technology solutions will help banks navigate this transformative era.
By working closely with technology providers and harnessing the power of open finance, banks can ensure that their GenAI tools are tailored to their specific needs and integrated seamlessly into their existing systems. This partnership can also facilitate continuous improvements in AI models, ensuring they remain accurate and relevant in a rapidly changing market environment.
Fostering a culture of collaboration within the organization is crucial. Encouraging cross-functional teams to work together can lead to more holistic and effective solutions. For instance, treasury departments can collaborate with IT, risk management and compliance teams to develop comprehensive strategies that leverage GenAI.
By embracing collaboration, banks can enhance operational efficiency and ensure their GenAI initiatives remain competitive and compliant in a complex financial landscape.