If the FinTech era has elevated any idea to a maxim it may be that we cannot improve what we do not measure. Financial institutions across sectors and jurisdictions have successfully applied advances in analytics technology to capital markets trading and to risk management, and more recently to various aspects of liquidity and cash management. From our experience, incorporating these advances to measure and optimize liquidity can reduce intraday liquidity requirements by up to 25% and significantly reduce payment delays by up to 90%. But what do financial institutions need to do to operationalize and make the most of these emerging opportunities?  In this segment, we focus on two pillars (or axes) that are key in examining the maturity levels of GSIB banks in intraday liquidity management:  infrastructure & process maturity, and analytics maturity. 

Infrastructure & Process Maturity

How an organization is able to capture, process, and manage its payments and liquidity data, and its processes for liquidity management, reflects its maturity from an operational perspective. To that end we identify four characteristics to consider:

  1. Data management and visibility
  2. Operations technology
  3. Operational model
  4. Process and business integration

Data Management and Visibility

Organizations understand that good decision-making requires access to high-quality and timely data. From a data management and visibility perspective, it is important to understand to what extent the organization has adopted global data standards and accessibility (some of which have been outlined by the Basel Committee in BCBS248), as well as examine how they make these available to management and stakeholders in the form of operational dashboards. A key differentiating factor here is the ability to access this data in near real-time.

Operations Technology

How technology supports liquidity operations is another key element of a well-developed liquidity management organization. Capabilities such as configurability of payment systems (i.e. ability to automate when payments are made, adding and adjusting credit limits, and netting amounts) and liquidity automation (i.e. to what extent liquidity may be balanced automatically without the need for time-consuming manual overrides) are important characteristics of an effective operations technology program.

Operating Model

Global operations require increasingly centralized models to minimize redundant account and currency management. Additionally, operational systems need to support operations teams in optimizing intra-group payments with capabilities such as netting and offsets.

Process and Business Integration

From a process and business integration perspective, it is of course important to automate regulatory reporting, and many GSIBs have focused on this as their first priority. As the operational tasks associated with collecting and processing the data necessary for regulatory reports are intended to be consistent and repeatable, they are well suited for automation. Additionally, it is important to understand if contingency funding plans are well defined and can be swiftly implemented in times of distress, enabling recovery and resolution. Further, banks need to consider customer payment policies and a method of incorporating liquidity transfer pricing at a global scale for products. Only organizations with good management information, effective controls and transparency of costs in place will be able to do so efficiently.

Analytics Maturity

Having the right operational infrastructure is like having a robust chassis to support the various functions of a high-performance car, but you still need a high-performance engine to really get the most out of your system. The driving force behind analytics capabilities includes the following:

  1. Liquidity Saving Mechanisms (LSMs)
  2. Sophisticated Liquidity forecasting capabilities 
  3. The ability to incorporate operational analytics to ‘tune’ policies

Liquidity Saving Mechanisms

One of the key features of a mature liquidity analytics engine is the ability to incorporate LSMs. These mechanisms should be enabled by data-driven simulations that can examine the impact of different actions to minimize liquidity requirements and payment delays in a settlement network. LSMs are varied and include queueing policies (i.e. FIFO or bypassing payments), netting, payment splitting, and different multilateral queueing optimization methods to improve the overall liquidity profiles of individual legal and clearing entities. 

Liquidity Forecasting

An advantage of an effective liquidity analytics engine is its ability to run simulations to forecast liquidity needs, giving treasury and operations teams a clear view of forward-looking needs related to inbound and outbound payments. This, in turn, allows management to prepare and distribute funds in order to avoid unnecessary payment disruptions and be proactive with regards to liquidity usage.

Operational Analytics

From an operational perspective, an advanced liquidity analytics engine enables banks to model liquidity and operational event stresses (i.e. payment delays, distressed entities) and provide greater confidence in their models to regulators and payment system operators. Additionally, the analytics engine should have the ability to fine-tune intraday liquidity operations – informing policy overrides and enabling dynamic intraday payment prioritization.


The marriage of a robust operational infrastructure and a strong analytics capability provides the foundation for a mature intraday liquidity organization. These cornerstones enable practitioners to dynamically determine policies to better reflect the changing characteristics of payment flows, keep operations running efficiently by minimizing liquidity usage, and aid in developing proactive policies to mitigate liquidity risks in stressed environments. Global banks, including most GSIBs, have made substantial progress over the last decade in building their intraday liquidity capabilities. Nonetheless, based on our interactions with more than 15 GSIBs this year, a very wide range of maturity levels still persists across the industry. 

Mohsen Namazi (mohsen@fna.fi) is a Managing Director of FNA based in North America, and Phillip Straley (phillip@fna.fi) is FNA’s President. FNA’s intraday liquidity optimization solution is based on more than 20 years of relevant experience, initially with national payments systems and more recently with banks.

More News

What is Intraday Liquidity Optimisation?

Author: Will Towning, Central Banks and Academia Programme Manager   What is intraday liquidity?   Intraday liquidity is the funds financial institutions can access during the business day in order to make payments in real time.    Acute liquidity stresses during the Global Financial Crisis (GFC) highlighted the need for banks to improve intraday liquidity […]

FNA Talks Data Science in Economics and Finance with Nordic Investment Bank

Data Science in Economic and Finance for Decision Makers with David Bolder, Director of Model Development and Economic Capital at Nordic Investment Bank  In this latest episode of the FNA Talks podcast series, David Bolder, Director of Model Development and Economic Capital at Nordic Investment Bank. In this latest episode of the FNA Talks podcast […]

FNA Co-Hosts CEMLA’s Suptech and Regtech Training

FNA recently co-hosted a Suptech and Regtech training series with CMELA, the Centre for Latin American Monetary Studies.    The interactive training sessions introduced Central Bankers to the key foundations and new applications on innovative technologies including AI and Machine Learning, Big Data, DLT, Cloud as well as Network Analytics and the opportunities and challenges […]
Copyright FNA © 2021 | Privacy Policy