The economic landscape is changing, and AI is starting to have an impact on our daily lives. There has never been a better time to invest in data analytics from your TMS or CTRM.
As the global economy falters in recovery from the pandemic, and central banks raise interest rates to levels not seen for 15 years, the need for accurate and reliable data is core to trading operations. For the treasury and commodity trading industries, leveraging advanced analytics can hold the key to unlocking significant value. This paper explores the potential of analytics in these sectors, highlighting how they can drive informed decision-making, enhance risk management, and optimize operational efficiency.
“We are surrounded by data but starved for insights.”
There is data everywhere: in our Treasury Management Systems (TMS), Commodity Trading Risk Management (CTRM) software, and Enterprise Resource Planning (ERM) systems; originating in external data providers like Bloomberg and Refinitiv; and scattered throughout a litany of off the shelf or bespoke systems in every organization.
Advanced analytics is the use of techniques such as data visualization, machine learning and predictive analytics to extract meaningful insights from these vast volumes of data. Enhanced data visualization through charts and dashboards can help identify trends or outliers. Machine learning can be used to analyze historical or real-time data to identify anomalies or detect fraud. Predictive analytics utilize the data to make forecasts, or attempt to identify potential future risks in advance. These techniques ultimately make the implications of data easier to grasp and organizations can gain a competitive edge by making data-driven decisions.
“It is a capital mistake to theorize before one has data.”
The use of analytics in treasury and commodity trading brings numerous benefits. Firstly, it enhances risk management capabilities by identifying and assessing risks, attempting to predict market trends, and suggesting comprehensive hedging strategies. As market volatility increases, being able to assess how this impacts valuations, cash flow forecasts, hedge positions, etc. enables better decision making across the treasury and commodity trading desks.
Secondly, data analytics can help optimize trading strategies. By leveraging historical data, market trends, and real-time insights, organizations can develop and refine trading strategies to maximize profitability and minimize risks. Real-time risk monitoring and predictive modeling also assist in effective risk mitigation strategies.
Lastly, the use of analytics can improve operational efficiency and help optimize processes. By automating manual processes and leveraging analytics-driven insights, organizations can streamline trade confirmation, settlement, and reconciliation processes. This not only reduces costs but also enhances overall operational performance.
“Data is like garbage. You’d better know what you are going to do with it before you collect it.”
Implementing advanced analytics comes with its own set of challenges. Data quality and availability, technology infrastructure, and resources are common hurdles faced by organizations. However, best practices can help overcome some of these challenges:
- Establish a robust data governance framework to ensure data quality, integrity, and accessibility;
- Invest in scalable technology infrastructure that can handle large volumes of data and enable real-time analytics; and
- Empower your team to become data and domain experts with a strong understanding of both analytics and treasury/commodity trading.
Building a successful analytics-driven culture is equally important. This involves fostering executive buy-in, promoting cross-functional collaboration, and prioritizing continuous learning and innovation.
“You can have all of the fancy tools, but if [your] data quality is not good, you’re nowhere.”
The future of data analytics is exciting as access to data and the ability to manipulate it becomes easier. Emerging trends and technologies, such as artificial intelligence, big data, and cloud computing, are reshaping every aspect of our lives. These advancements enable organizations to unlock new insights, make more accurate predictions, and drive innovation, but you need the right technology to capitalize on it.
If you are reliant on legacy technology like Endur/Findur’s Report Builder data extraction tool, Calypso’s reports, or Allegro’s class events, Lucido’s team can help you maximize the value of your existing infrastructure. These tools may not have the analytics you require, but they can be used to populate data warehouses / lakes or provide inputs to visualization tools like Power BI. Expert knowledge is in understanding the data model and knowing the best way to extract the data.
The future of analytics will be in more agile, cloud-native systems, and we have the skills and tools to help your organization grow in that space too. Reach out if you would like any support.
