In today’s market environment, it’s no surprise that CFOs and Treasurers alike have been faced with increasing pressure to optimize their working capital. There is no better time than now to shift from a more tactical approach to one that is proactive and strategic. Innovations today like Artificial Intelligence (AI) and machine learning can unlock real value.
We interviewed Stanley Tan, Head of Working Capital Advisory, Global Transaction Services, DBS Bank, on how we can leverage data and AI to make working capital work for you.
What is the most important input you have for a CFO trying to understand data analytics’ roles?
Stanley (DBS): Maximizing the value of data analytics first involves asking the right questions. While every corporate wants to leverage data to generate business insights, the most important (and often overlooked) part of the process is actually figuring out what the problem really is.
With the big data hype, it is easy for organizations to be overly-fixated on data collection without a business problem to be addressed, and end up looking at the wrong places for the wrong pieces of information. This bottom-up approach to data analytics exposes organizations to the risk of getting lost in voluminous amounts of data, over-complicating analyzes, and erroneous results.
This is especially relevant since CFOs today are fed information from a variety of sources, Therefore, CFOs need to identify specific business challenges before asking what data is required and where the data can be found.
Is it better for a company to develop a data team internally or look for outside solutions?
Stanley (DBS): There is no silver bullet when it comes to developing data teams. As with all insourcing or outsourcing decisions, individual companies need to carefully evaluate their individual business models and weigh the pros and cons of each option. While most would be inclined to develop a data team internally for the advantages in speed and flexibility, there are other factors to consider.
Given the scarcity of data scientists today, a good data team would likely command a premium in compensation. Though large multinationals and global giants have pockets deep enough to hire the best data scientists to develop proprietary algorithms in-house, smaller firms may find it more cost-effective to ‘leave it to the experts’ by outsourcing their data teams.
Furthermore, with Software-as-a-Service (SaaS) solutions gaining popularity, SaaS providers can potentially deploy solutions faster and cheaper than an in-house data team, especially after taking into consideration infrastructure, development and staffing costs. With cloud computing solutions on the table, some might also argue that outsourcing data teams could prove to be more secure than building up internal firewalls and cyber-defenses.
The challenge for CFOs who decide to develop their data teams internally then revolves around attracting, developing and retaining data scientists. This human capital problem is heightened by competition from startups who are hungry for talent as well!
Ultimately, CFOs need to strive to transform the entire organization to be digitally-connected, data “literate” and enabled. If that is done, it matters less if the data team is in-sourced or outsourced as either way, CFOs can be assured the output is well used.
How does a CFO weigh the risk of data exposure against the positives?
Stanley (DBS): A cybersecurity breach is not a matter of “if”, but a matter of “when”. As the risk landscape becomes increasingly complex, CFOs need to be cognizant of who might want to lay their hands on proprietary data and why they might want to steal it.
Hackers are becoming increasingly sophisticated, and they are now able to use specialized algorithms to plow through voluminous records and exploit identified weaknesses in IT systems. CFOs may even be unaware of advanced persistent threats (APT) lying dormant in the organization’s network, stealing data for months.
Data is also even more exposed given the fact that CFOs deal with an entire ecosystem of customers, suppliers, and contractors in the supply chain, where the lines between employees and external contractors are increasingly blurred. Therefore, CFOs not only need to guard against attacks from the outside but also breaches from within.
While some might argue that cloud computing has made data storage more secure and safer from breaches, data consolidated on cloud servers are still at risk to hackers. At the same time, this does not mean that data should be allowed to remain on legacy systems which have not been designed to deal with today’s complex threats and hacking attempts.
With no “unhackable” system in the world, the pertinent question CFOs need to ask themselves is then how to manage cybersecurity risk and consequently the level of risk they are willing to tolerate.
Bearing these risks in mind, there is now a greater impetus for CFOs to leverage data analytics (and even artificial intelligence) to manage fraud detection and cybersecurity. From a commercial standpoint, data analytics can help CFOs uncover patterns from unstructured data to generate insight.
Similarly, when used as a defense mechanism, data analytics can also be deployed to run stress tests for the purpose of identifying unusual patterns and anomalies that could hint at potential fraud. While it is impossible to eliminate cybersecurity risk, the exposure can definitely be managed.
How can a CFO prepare for the artificial intelligence (AI) revolution and what skill sets do finance professionals need to invest in?
Stanley (DBS): The artificial intelligence revolution is already here, period. In fact, artificial intelligence has already embedded in our daily lives without us realizing it, in the form of algorithms built into Google Maps, UBER or Facebook. It is imperative that CFOs be proactive in facilitating a paradigm shift from the traditional manual reconciliation role of the finance department to an algorithm-driven approach.
Consequently, teams that are eager and bold to embed AI into their work are likely able to become more efficient. The metadata available, coupled with massive computing power and extremely low cost of data storage essentially means that there is a goldmine of opportunity to be uncovered (by the CFO who asks the right questions). Armed with AI, CFOs could potentially model and predict the organization’s working capital requirements, identify new target markets or detect a possible risk of default.
Yet, people are also becoming increasingly concerned about the future state of the workforce. What skills would the future finance professional require if everything could be modeled by algorithms? As artificial intelligence begins to make decisions for us, the question to ask is how do we differentiate ourselves from robots, and what can humans do that a robot cannot? According to the World Economic Forum’s Future of Jobs Report, once-important skillsets such as negotiation will become less important as algorithms start to optimize decisions for us. But to be able to collaborate together on dynamic business problems, identifying the right issues to address, these are tasks that robot cannot be modeled to perform – yet. Consequently, finance professionals need to be able to think beyond the parameters of structured data as the finance function evolves beyond just a game of numbers.
STANLEY TAN, Head of Working Capital Advisory, Global Transaction Services, DBS Bank
Stanley Tan heads the DBS Working Capital Advisory Programme at the bank’s Global Transaction Services unit. He is responsible for the strategy, development, and operations of the programme globally.
Prior to joining DBS, he was a management consultant with McKinsey & Company based out of the New York and Seattle offices. While at McKinsey, he was a core member of the Corporate Finance, Private Equity and Financial Institutions practices and has served corporate clients in North America and Asia Pacific on matters pertaining to Strategy, Finance, Risk, and Operations.
He graduated Magna cum Laude from Columbia University with a BA in Economics and Anthropology and has passed Level III of the CFA Program.
DBS is a leading financial services group in Asia, with over 280 branches across 18 markets. Headquartered and listed in Singapore, DBS has a growing presence in the three key Asian axes of growth: Greater China, Southeast Asia and South Asia. The bank’s “AA-” and “Aa1” credit ratings, is among the highest in the world.
DBS is at the forefront of leveraging digital technology to shape the future of banking, and has been named “World’s Best Digital Bank” by Euromoney. The bank has also been recognised for its leadership in the region, having been named “Asia’s Best Bank” by several publications including The Banker, Global Finance, IFR Asia and Euromoney since 2012. In addition, the bank has been named “Safest Bank in Asia” by Global Finance for eight consecutive years from 2009 to 2016.
DBS provides a full range of services in consumer, SME and corporate banking. As a bank born and bred in Asia, DBS understands the intricacies of doing business in the region’s most dynamic markets. DBS is committed to building lasting relationships with customers, and positively impacting communities through supporting social enterprises, as it banks the Asian way. It has also established a SGD 50 million foundation to strengthen its corporate social responsibility efforts in Singapore and across Asia.