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

Agent-Based Simulation of CBDCs

Agent-Based Simulation of CBDCs: A Research Paper by Amanah Ramadia, Marco Galbiati and Kimmo Soramäki Central Bank Digital Currencies (CBDCs) have seized global attention, featuring broadly in central bank strategies.  Today, 87 central banks are currently looking to introduce digital currencies in the near future, however the impacts of CBDCs are still relatively unknown.  Many […]

The CBDC Broadcast

About the series The introduction of CBDCs is getting closer, with 63 central banks looking to introduce digital currencies in the near future. However, there are still many outstanding questions concerning topics such as financial stability, fee income, deposit outlays, disintermediation, monetary policy and financial stability. Covering the latest CBDC related news, events and input from […]

Network Sensitivity of Risk

By Amanah Ramadiah, Domenico Di Gangi, D. Ruggiero Lo Sardo, Valentina Macchiati, Tuan Pham Minh, Francesco Pinotti, Mateusz Wilinski, Paolo Barucca and Giulio Cimini.   The paper, ‘Network Sensitivity of Systemic Risk’, explores how propagation dynamics depend on the topological details of an underlying network. It provides detailed understanding of the interplay between systemic risk […]
Copyright FNA © 2021 | Privacy Policy