In the years since the global financial crisis, corporate treasury has become increasingly strategic in helping to manage risks across a company. Financial risk management complexity has increased and – together with the impact of regulation upon banking relationships – a strategic treasury view of enterprise risks is highly valuable for executive leadership and business line managers.
Technology advances now enable the use of artificial intelligence (AI) and predictive analytics at a scale and speed previously not possible.
Robotics and AI are already radically changing sectors of the financial services industry, such as wealth and investment management, as robo-advisors replace traditional consultants. A similar transformation is underway in many other sectors, which could either free up employees from routine tasks or, alternatively, mean that resource savings can be made.
This article looks at both the potential risks and benefits of robotics and asks what the implications are for corporate treasury departments.
Figure 1: Four elements of simulation and predictive analytics
The pace of advancement in analytics and computational capacity over the past decade has begun to offer the opportunity for executives to approach strategic decisions in new ways.
As outlined above in Figure 1, what happened; why it happened; what might happen; and what actions might a company or treasury take to safeguard its position are elements of simulation and predictive analytics. Lessons from the past can be highlighted and risk scenarios tested, so that better informed decisions can be made.
Justin Lyon, founder and chief executive officer (CEO) of simulation software company Simudyne, says: “Predictive analytics can be thought of as a three-legged stool. There are three disciplines that you need to bring together in order to do it properly.
“The first is machine learning; this is a statistical approach that involves trying to understand patterns in data. The second is computational simulation; this involves recreating physical environments in a virtual world and understanding how entities within these worlds interact. The third, network modelling, involves using graph theory to understand interconnectivity in complex networks.”
He continues: “The necessary computational power and resources to combine these technologies at pace and scale are now available, accessible and reasonably priced. The advent of commodity hardware available in the cloud – in combination with the widespread availability of open source cluster computing frameworks – means that large-scale predictive analytics is now possible.
“This kind of analysis helps us understand complexity. In an increasingly interconnected world, it is essential that we capture the feedback loops and contagion risk inherent in these complex systems that are emerging.”
Figure 2: Enterprise wide risk management inputs
Treasury practice is to manage risks within a company related to the financial well-being of the organisation. To achieve this, treasury applies models to credit, liquidity, currency, payments and funding risks, to name just a few of the elements. These models produce a significant amount of data that, when coupled with enterprise wide inputs from product development or client analytics, can deliver insights that might highlight risks or opportunities, which influence not only financial risk management but also corporate strategy.
Enterprise-wide data access used to just be a key part of compliance. Now that we have the ability to use that data to run simulations of the future, we can employ it to think of new ways to drive business as opposed to just managing business risks.
The opportunity for treasury is to apply their financial risk management discipline to company-wide strategy.
All too often a risk is not identified until it happens and the related impacts of an event can have drastic consequences for a company. While this can never be entirely mitigated, it is possible to spot behaviour patterns in a business or simulate events to raise awareness and propose actions to protect against them.
Justin Lyon comments: “There are a number of questions a company needs to answer when it comes to managing its risk and making decisions going forward. Principally, it needs to ask what happened historically and why did it happen? These questions tend to be answerable with standard statistical techniques.
“Descriptive statistics will tell you what happened and give you an idea of why it did. However, when it comes to answering the harder questions such as ‘what could happen?’ and ‘what should we do?’ you need to turn to computational simulation. Many of these techniques do not rely on statistical relationships.”
“As we’ve discovered, the past isn’t always a good guide to the future. When we’re caught off guard, it tends to be by events that have not previously happened. In order to forecast these events you need to understand the physics of them. This means mapping out the entire system you’re observing, recreating all the points of interaction and then shocking the system to see how interacting parts respond. This can help you spot looming risks, such as counterparty credit risk, contagion risks or marketplace threats.” The range of risk in which the corporate treasurer has a central risk management role is shown in Figure 3 below.
Figure 3: Treasury risks
The interrelated elements of a company beyond treasury form a broad risk management approach. While techniques applied in each of these areas commonly address business or risk specific approaches, what we show here is that the number of interrelated risks can be substantial and modelled.
Taking this a step further, one could take the complete supply chain across business divisions of a complex organisation and simulate how a supply chain shock of any type (foreign exchange, political, weather) could affect the entire organisation. Equally this could be reversed to simulate how a change in the buyer behaviour – or competitive landscape – of a core product produced by one division could impact the financial risks to others within a group.
The risk for an organisation of not adopting enterprise-wide risk management approaches is that being caught “off-guard” raises the possibility of fire-fighting rather than an organised and intelligent approach for executive decision making. Banks, energy companies and regulators are already adopting these approaches.
In an increasingly complex and rapidly changing world, the ability for treasury to provide enterprise-wide risk insights and strategic input has never been more critical. Establishing a data community inside your organisation and applying predictive analytics will allow treasury to assist executives and business owners to not only manage known risks more effectively, but to be a real strategic treasury.
The BoE launching digital currency on blockchain tech would be international payments ‘game changer’
The Bank of England may be the first major central bank to put its currency on a blockchain which would be a ... read more
The industry consensus is that the best way to select a treasury management system is by analysing the 'Requests for Proposals' criteria. But is it? Does this lead to the best solution? If you adopt this methodology will you have a TMS that meets your operational needs?
Despite their importance to the world economy, SMEs often face problems accessing credit when and where they need it. Their banking needs are often more complex than the usual retail banking customer and they don’t offer banks the revenue potential of larger corporations.
Whether responding to questions from other stakeholders or seeking to monitor the effectiveness of their risk management processes, treasurers and FX managers in growing companies with expanding foreign currency exposures need solid analytics to support their decision making.