AI and Cloud Remove Barriers to Entry for Real-Time Intraday Liquidity

Financial institutions must actively manage their intraday liquidity, but getting to this point continues to be a challenge as banks are required to capture the information they need in real time while meeting increased regulatory reporting obligations.

However, for liquidity risk managers to have a truly relevant enterprise-wide, real-time view of their liquidity, financial institutions will need to consolidate siloed legacy systems into a single automated solution with predictive analytics layered on top. 

A report by SmartStream, Intraday Liquidity Management: From a Cost Discussion to a Revenue Opportunity, explores this in detail, as well as how technologies such as cloud, artificial intelligence (AI) and machine learning can help banks achieve higher levels of automation and reduce manual workload. 

Intraday volatility in reporting leads to volatility in decision-making. To manage intraday liquidity successfully in a financial institution, funding, liquidity and risk managers must be able to anticipate the peaks and troughs of the bank balance, and predict the liquidity demands that may occur throughout the day. 

Armed with that knowledge, a bank is in control of its own resources rather than responding to settlement demands when they arise. Financial institutions can leverage next-generation technologies such as cloud, AI and machine learning to achieve real-time management of their global intraday liquidity. 

Analysis of intraday usage has always been a historical analysis, but technology such as cloud, AI and machine learning can enable banks to take extra value out of the data that results from settlement activity

Richard Morris, SmartStream

The importance of managing the flow of liquidity as well as intraday counterparty exposure cannot be overstated. There is also an element of understanding the drivers of liquidity demand and who within the organisation is driving the demand for intraday liquidity, being able to spot anomalies as they arise and respond to unexpected events. 

Traditional systems address the operational burden of cash management and consolidating data from internal systems to provide an enterprise-wide view of liquidity demand throughout the day, and of positioning liquidity to meet settlement demands. It is an invaluable task, but it is incredibly data-intensive. 

To date, interpreting trends and metrics, and identifying behaviors and anomalies has been hampered by the volume of data being processed and the time it takes to analyze it. Analysis of intraday usage has always been an historical analysis, but technology such as cloud, AI and machine learning can enable banks to take extra value out of the data that results from settlement activity.

Machine learning allows financial institutions to predict the profiles of their intraday settlement and their peak liquidity demand at any point during the day. Many banks lack this actionable intelligence but—using technology such as machine learning to predict fluctuations in cashflow—will allow financial institutions to manage their flow of liquidity, reducing the liquidity buffer and, in turn, cost. 

Predictive analytics can also be used to identify whether the bank or the market as a whole will enter a stressed environment and, therefore, use machine learning to put the organisation in a much better position to respond. These AI and machine learning techniques can also be applied to the regulatory use of data, to help banks derive the maximum benefit from what is being reported. 

The implementation of cloud, on the other hand, enables more institutions to adopt solutions that might otherwise carry a large cost of ownership. Where the largest banks have the resources to develop and operate these advanced solutions, it has always represented a significant investment. The lowering of upfront investment and ongoing costs—driven by the advent of cloud computing—will democratize these solutions and enable much wider uptake across the industry.  

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