Data drives gains in transaction banking

Rationalization of legacy systems for better advisory services is achievable by harnessing big data analytics and algorithms to sift through huge databanks to propel change

The vogue for treasurers to stick with current banking partners offers an opportunity for banks to deepen their relationship with clients beyond just servicing to more of an advisory role. One method of doing this in transaction banking is by employing the effective use of data analytics.

Knowledge of this inertia is gleaned from firsthand research suggesting that, despite the variety of choices in the market and the prevalence of counterparty risk, CFOs and treasurers appear to be committed to working with their current small circle of banks.

That’s according to Asset Benchmark Research (ABR) data, where 40% of surveyed CFOs/treasurers were currently only using two to three banks for their cash/liquidity management needs in Asia, with a quarter satisfied by using just one bank for the region. A majority (63%) of respondents said they had no intention to either increase or decrease the cash management banks they work with.

“The data the bank sits on is phenomenal. However, the problem with banks is that they have multiple legacy systems, platforms and core systems where various types of information reside,” shares a transaction banker from an international bank.

While combining legacy systems and creating a robust data lake presents a challenge, the upside potential for banks is an attractive proposition. Utilizing better data analysis, banks could drive meaningful business discussions with clients by illustrating how their peers are performing in terms of working capital efficiency, and subsequently implement measures to improve days sales outstanding and cash conversation cycle.

Moreover, data can be used to facilitate a better client experience, particularly by onboarding suppliers onto a supply chain programme where credit worthiness can be vetted across the bank’s entire network rather than just in-country. This can bring certain advantages. For example, a supplier might be about to be denied financing by a bank’s Indonesia branch only for the branch to subsequently discover that the supplier is a long-term client in its Malaysia branch and approve the financing instead.

CFOs and treasurers are also utilizing data internally within their own businesses. According to ABR data, close to half of respondents were currently using emerging technologies such as big data analytics in their respective organizations, followed by artificial intelligence (17%).

“Analyzing data and discovering patterns is becoming more and more a focus for the treasury department. Systems using predictive analytic algorithms and big data are increasingly available for exercises, such as liquidity forecasting based on sales data and historical trends. This kind of analytics can only be applied if standardized and structured data is available and accessible through interfaces provided by the treasury management system and other data sources,” states a recent EY report. 

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Date

15 Mar 2019

Channel

Treasury

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