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AI and cloud remove barriers to entry for real-time intraday liquidity

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 at the same time meeting increased regulatory reporting obligations.

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

Finextra spoke to Richard Morris, product manager, cash and liquidity management at SmartStream about their recent report ‘Intraday Liquidity Management: From a cost discussion to a revenue opportunity’ and how technologies such as cloud, artificial intelligence 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 as and when they arise. Financial institutions can leverage next-generation technologies like cloud, AI and ML to achieve real-time management of their global intraday liquidity.

Morris highlights the importance of managing the flow of liquidity but also the management of intraday counterparty exposure. 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. An invaluable task, but it is incredibly data intensive.

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

Morris continues to explore how ML allows FIs to predict the profiles of their intraday settlement and what their peak liquidity demand may be at any point during the day. Many banks lack this actionable intelligence, but using technology such as ML to predict fluctuations in cash flow, will allow FIs 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 ML to put the organisation in a much better position to be able to respond.

Morris adds that these AI and ML 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 ownership. Where the largest banks may 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 democratise these solutions and enable a much wider uptake across the industry, Morris concludes.

Read more about how AI and cloud can remove barriers to entry for real-time intraday liquidity here.

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