Digitally Enabling Risk Management Objectives: Accelerating to Smart Treasury

FX Risk Management Solutions Quarterly | Issue 127 | July 2020 | Trending 7 • Combining with human intelligence to evaluate machine conclusions providing another feedback loop to further enhance the algorithms. Auto-determination of next action Forecasted currency risk position with a variance measure by currency over time arising from the algorithmic techniques described above provides one set of inputs. The second set of inputs is a measure of currency risk profile/appetite for the treasury: namely, risk management instruments allowed and stated treasury objectives such as cost optimization, VaR tolerance, and/or best rate achieved. With these two sets of inputs, logic creates a prescriptive algorithm to deduce the necessary hedging actions. The output of such algorithms goes beyond simply offering an opinion and extends to providing an evidence-based set of outputs, providing the rationale as to the why the recommendations were made, offering full transparency for human/machine trust creation. Historical back-tested data and instrument profiles are used to suggest optimal trade recommendations. An important component of this prescriptive algorithm is its ability to incorporate forecasted error, which, fed into the algorithm at the same cadence as the algorithmic forecasted currency positions, produces an optimum outcome. This, we expect for some, may approach a near-real-time feedback loop of forecast error, enabling a high-frequency validation of the hedging policy and resulting in a possibility for a near-continuous tracking of hedge effectiveness through market disturbance events. Currency movement forecasting models that provide the necessary signals to risk managers to adjust their hedging programs form part of an extensive suite of emerging decision-support tools. The availability of tools that forecast the direction of specific currencies within a specific time horizon has increased, providing needed supports for individuals making more refined hedge decisions. With the combination of algorithmic forecasting (as more specialized vendors enter the market), advanced currency movement forecasting models, and algorithms to determine the best next action, coupled with the automation of policy validation practices, the advancement of decision-support tools for risk managers to determine and prescribe next action is accelerating. The next step for corporate treasury, which we cover in our next and final section, is to identify the most cost-efficient means of realizing currency risk management objectives through the auto- execution of prescribed next actions. Realize the recommended actions via intelligent automation of trade execution Perhaps the most common form of automation in currency risk management is the integration of a company’s TMS with an electronic FX execution venue. Most companies tend to be satisfied executing through FX liquidity aggregators (e.g. FXALL, 360T), citing key reasons such as consolidated reporting, competitive pricing, and the ability to efficiently distribute their FX wallet among their banking partners. Direct connectivity with banks tends to happen only when there are gaps with these aggregators. Typically that’s due to unavailability in specific markets (such as highly regulated countries), specific instruments (such as complex options), or channels (such as algorithmic execution). Therefore, only the most global of banks have direct connectivity with clients, who tend to be the most global of corporate treasuries.