Banks can boost performance with Intraday Liquidity Management
Financial firms need an ability to meet payment and settlement commitments—not just at the end of every day, but at any point throughout each day. That ability, known as Intraday Liquidity Management (ILM), has been a priority since 2008’s financial crisis.
But achieving intraday liquidity is not an easy feat, as we describe in our paper, Improve performance and competitiveness through Intraday Liquidity Management. Failing to meet obligations exposes a firm to liquidity risk.
Capturing the right data
A preferred way for firms to effectively navigate intraday liquidity is via timely sourcing of relevant trade economics data, cash flow information and exact time stamps for given cash flows. Capturing real-time data is essential. But we’re not talking about just any data. Banks need an ability to leverage appropriate data that is connected and traceable across all systems.
If they can establish this capability, banks should see better decision making, improved operational capabilities and more tangible, data-driven results.
Benefits of real-time intraday liquidity reporting
Three steps to transformation
Banks can work toward building a robust ILM but they should understand their starting point first. The ILM process can be viewed in terms of three broad steps:
- Assess current state liquidity management and identify the root causes of volatility: How mature are current functions and business processes related to ILM? What new processes should be added? Where is volatility in intraday liquidity?
- Design a business and data operating model: Based on a bank’s current state, as identified in the first step, a target operating model and supporting data architecture can be designed.
- Implement ILM group wide: The final step is to implement the newly designed systems and processes across all functions and entities. This might include training or hiring new resources.
Challenges and choke points
Good data is essential to a strong ILM but may not necessarily come easily. Banks should capture structured and unstructured data from multiple sources across businesses and regions. This already far-flung challenge is compounded for financial firms that are spread across a variety of regions, each subject to different compliance requirements.
Many banks are likely to encounter some data choke points:
- Multiple systems with multiple formats, carrying volumes of data at varying levels of quality, make it difficult to identify what’s driving intraday liquidity volatility at a group- or business-area level.
- Poor flow of settlement information means less certainty around funding requirements.
- Mapping internal cash accounts to upstream/incoming flow from trade capture and risk management systems can be difficult.
- Straight-through processing throughout front-to-back flow might conflict with ILM requirements.
- Poor information flow to self-clearing and nostro accounts might reduce ILM efficiency and decrease the reliability of funding requirements.
Technology can solve some of these data challenges. Big data, machine learning and rtificial intelligence capabilities can help capture and gather meaning from appropriate data accessed across the organization, and streaming technologies can feed that new-found meaning into real-time dashboards.
Is your bank ready for ILM?
Building a robust ILM model may not come easily for many financial firms, but banks that can access and use wide-ranging data in real time should see tangible benefits. See how Accenture’s ILM specialists can help.