Insight-Driven Treasury: Managing Capital and Liquidity

Treasurers use data as a strategic asset

In today’s digital era, data is a treasury management team’s most valuable commodity. Treasurers must adopt a data-driven strategy to leverage diverse data for more than just reporting, enabling advanced risk management techniques. A centralized data management framework is the cornerstone for efficient data gathering and utilization, particularly for capital stress testing and liquidity risk modeling.

Data democratization has transformed the treasurer role into a leader of capital and liquidity oversight. With comprehensive data, treasury can advise on historical patterns, current exposures, and forecast risks through scenario analysis, enhancing capital and liquidity resilience.

What do treasurers need?

Treasurers should recognize that a modernized data infrastructure is their primary protection against capital and liquidity risk. The limitations of traditional, low-tech treasury processes are evident in their susceptibility to errors, rigid underlying infrastructure, and reliance on outdated instead of real-time data.

In the present financial environment, relying exclusively on historical data — even day-old data — can be a liability. It may lead to the misinterpretation of trends or present irrelevant information, leading to a false sense of security. Without up-to-date information, an organization may not be prepared for upcoming challenges.

Treasurers require a robust foundational data model supported by real-time data, which can be accessed and leveraged in a timely manner. The latter —accessibility —is paramount. Even the most comprehensive and current data is of no value if users are unable to retrieve it. Treasurers must be familiar with data sources, proficient in their use, and able to access information for decision-making. To enable this access, the data foundation requires centralization, quality assurance, and comprehensive data documentation.

Treasurers armed with data drive transformation

Properly empowering treasury and financial resource management teams with comprehensive, timely, and accurate data drives transformational outcomes, some of which are discussed below.

Data serves as the vital foundation for all treasury processes, so before diving into the outcomes it is worthwhile to highlight proper data management practices. This includes quality assurance rules, validations that run each time data is updated, and governance processes to deal with outliers or exceptions. Quality assurance involves setting up comprehensive protocols that not only validate data upon entry but also continuously monitor it for accuracy and consistency. Governance processes play a crucial role in managing anomalies and exceptions, ensuring that any deviations are promptly addressed. Such meticulous attention to data quality and governance enhances the integrity of financial data, but supports informed decision-making, ultimately driving better outcomes for the organization.

Real-time decision making and utilization

Enhanced treasury operations and strategic alignment result from the real-time acquisition of data. The traditional reliance on historical data had the potential to introduce blind spots and heighten financial risks. In contrast, the modern approach to treasury management is fortified through instantaneous data access, enhancing the decision-making process and reducing the time needed for such actions. This real-time integration further enhances transparency into cash flows, liquidity needs, and possible capital shortfalls due to market shifts.

Real-time data integration empowers treasury departments to make timely, well-informed decisions. Through advanced data analytics and real-time data feeds, treasurers can monitor cash flows, forecast liquidity requirements, and respond swiftly to market shifts.

"McKinsey reported that real-time data analytics in 2023 can reduce decision-making time by up to 30%, enhancing overall agility and responsiveness."¹

Investment in data analytics tools

Advanced analytics tools elevate treasury risk management by processing complex data to promptly identify impending risks. Predictive analytics can enable robust stress-testing scenarios, reliable cashflow forecasting, and factor analysis. Proper storage of complex data includes a high level of granularity and a robust audit trail. Pairing this with a user-friendly interface and thorough documentation, business users across the bank can utilize the full potential of their data.

Enhanced analytics are achieved through the integration of comprehensive data (including metadata and deep-level data) from multiple areas of the business, generating the 360-view of risk.

Enhanced visibility into sources and uses of cash

Aggregating sources and uses of cash across the bank would be transformational for treasurers. A firm-wide view of cash would give them the ability to see their balances across multiple balance sheets, track wholesale funding flows, and monitor cash balances in their custodian accounts. Treasurers lacking this comprehensive perspective are navigating their roles with significant obstacles. The aggregation of data to create this view creates a foundation for treasury to make optimal decisions.

Accurate forecasting of cash flows, a cornerstone of risk modeling for treasurers, can be difficult to get right. The data is often fragmented across various systems and locations and holdings information is kept separately, compounding the problem. Regulatory requirements for publishing liquidity coverage ratios only enhance the need for accurate data for potential cash outflows. The data-gathering process can be labor-intensive and complex, leading to an increased risk of inaccuracy in the forecasts. Inaccuracies can lead to liquidity risks, including potential shortfalls or an overabundance of idle cash.

"The vast majority (93%) of senior finance leaders say inaccurate cash flow forecasting has led to otherwise avoidable costs in the past three years [2020-2023]."²

Integrated risk management

Risk management, the firm-wide “what-if” analysis, measures the impact of market events on exposures on a bank’s balance sheet. The first and most important component of risk mitigation is data accuracy. This requires thorough quality assurance processes, strict governance controls, and regular stewardship practice.

A well-integrated risk management framework involves a comprehensive approach towards capital and liquidity risk, market risk, credit risk, operational risk, team risk, and interest rate risk. Scenario planning and stress testing analyze potential risks and align them with current market conditions and the bank's risk profile. This can be strengthened with real-time data integrations, where up-to-date information can provide greater accuracy than historical or static data. Compliance with regulatory reporting requirements is also essential, and high standards of data quality must be maintained. Reliable data is obtained from regional depots to ensure data integrity.

To maintain a strong standard of risk management across the bank, continuous training is generally provided to develop and strengthen the risk awareness culture at the bank and to keep up with evolving standards and practices.

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Streamlined regulatory reporting and compliance

Compliance is a critical control and process that firms need to manage continuously. Given the volume of regulatory change and the inflexibility of legacy systems, many of these processes tend to be manual, resulting in data quality issues, compliance breaches, and a lack of agility in the reporting workflow.

Quick and accurate data collection constitutes a pivotal aspect of the compliance process. Centralized and integrated data can enhance the efficiency of compliance and reporting operations. The availability of high-quality, pre-approved, and publish-ready data throughout the bank can significantly enhance compliance, as a considerable portion of reporting complications stem from data-related challenges. Data localization laws pose a challenge for banks implementing data mesh architecture, but modern data platforms address this through solutions like data masking and data fencing techniques.

For more proactive compliance measures, and to increase the ROI of the centralized data source, comprehensive data analysis can reduce the risk of losses and penalties. Predictive analytics and data mining can help identify potential risks and compliance issues, allowing teams to take appropriate measures to mitigate them. The seamless integration encourages collaboration, so teams maintain a holistic perspective while working to identify and mitigate risk.

Operational efficiency and automation

By leveraging data and automation, banks can reduce delays and improve their responsiveness to market changes. This can lead to a more streamlined and efficient workflow, ultimately benefiting both the bank and its customers. For instance, banks can automate tasks such as credit risk assessment, loan approvals, fraud detection, saving time and resources. This not only improves the speed and accuracy of these processes, but also allows employees to focus on more complex and value-added tasks like capital optimization. Overall, leveraging data analysis in banking can lead to improved operational efficiency, cost savings, and enhanced customer experience.

Data-driven culture and mindset

Encouraging a data-driven mindset within treasury teams is crucial for better decision-making, strategic alignment, and fostering data literacy. Seamless data integration and sharing across departments create a unified data ecosystem, supporting collaboration and comprehensive decision-making. A data-driven culture makes everyone feel accountable for the data and promotes risk-awareness. Having a data-driven culture also means promoting continuous learning and improvement within the team. This can be achieved through regular training and workshops on data analysis and interpretation, as well as providing access to the latest tools and technologies. By equipping team members with the necessary skills and resources, they can effectively use data to identify patterns, trends, and potential risks, ultimately leading to more informed and proactive decision-making.

Structured approach to data management

With a structured approach to data management, teams can set clear objectives and understand the opportunities presented by data. This allows for a more focused and strategic use of data, rather than just collecting and storing it without purpose. By setting clear objectives, teams can prioritize which data is most important and relevant to their goals, and then develop a plan for how to gather, analyze, and utilize it.

Embracing the future of treasury

Data is a strategic asset for treasurers, enabling them to make well-informed decisions, mitigate risks, and drive transformation within their organizations. By adopting a data-driven approach, treasurers can unlock the full potential of data and partner across the firm to achieve organizational goals. With a centralized data management protocol, treasurers can efficiently collect and deploy data for activities such as risk management and forecasting, leading to improved operational efficiency, compliance, and overall financial success. In today's digital landscape, treasurers who embrace data as a strategic asset will have a competitive advantage and be better equipped to navigate the ever-changing financial landscape.

In today's volatile market, managing Interest Rate Risk in the Banking Book (IRRBB) is critical. Are you equipped to navigate the industry changes with operational dexterity?