Digitally Enabling Risk Management Objectives: Accelerating to Smart Treasury

FX Risk Management Solutions Quarterly | Issue 127 | July 2020 | Trending 6 Determining actions needed by treasury to deliver risk management objectives The appropriate set of actions and instruments needed to mitigate currency exposure to risk-policy levels and ratios is determined by combining the forecast of currency exposure over time with those policy objectives. The effectiveness of the hedge put in place directly correlates with the accuracy of the forecasted exposure. Inaccurate forecasted exposures can lead to the frequent adjustment of placed hedges. Such adjustment actions can depend on corporate cash reserves and whether the adjustments themselves generate a positive or negative cashflow. 4 In the previous section, we discussed the opportunity to continuously monitor risk policy sufficiency through process automation. In this section, we discuss the increasing adoption of algorithmic forecasting and the auto-determination of required next action, where determining required next action incorporates forecasted currency position and forecasted variance within risk policy parameters into a prescriptive algorithm. The case for algorithmic forecasting Many corporates today face challenges in preparing accurate forecasts. Fractured data sets and technology infrastructure deficiencies are driving manually intensive processes. In a recent benchmark study at Citi of over 400 treasury professionals, approximately half report that their treasury management systems do not fully support financial risk management processes, with 63% noting that their TMS is not fully integrated with their ERP, and 77% lacking full integration of their ERP with banks. 5 Traditionally, and primarily for these reasons, forecasting has been a mostly manual process with people gathering, compiling, and manipulating data within spreadsheets. With more and more data available, this manual forecasting approach has become an unwieldy, time-consuming process that makes discerning what is important next to impossible. As a result, individuals tasked to execute this process often resort to their own intuition and judgement, which opens the door to unconscious biases and conscious sandbagging. 6 Corporates are shifting away from traditional techniques to forecasting processes that involve people working symbiotically with data-led predictive algorithms replacing the manually intensive spreadsheet-based aggregation of predictions from business units. Algorithmic forecasting solutions are becoming increasingly available in the market from technology companies, such as Citi Ventures-invested Cashforce, and those solutions tend to have the following attributes: • Statistical models best fit to past commercial activity that describe what is likely to happen in the future, and data science deducing models to predict based on historical flows and market data. • Machine learning algorithms incorporated to course- correct and improve forecast accuracy over time, learning from previous cycles.