Treasury Digitization - Market Perspectives

4 Treasury and Trade Solutions Figure 1: Treasury’s Key Goals Number of Respondents: 85 Improving the Timeliness and Accuracy of Cash Forecasts 75% 62% 72% 59% 66% 55% 62% 45% Enhancing Treasury Controls and Reducing Exposure to Operational Fraud Risks Digitizing and Automating Treasury Enhancing Group-wide Cash Visibility Optimizing Liquidity Pools/Sweeps and Minimizing Trapped Cash Re-engineering Processes for Cost and Efficiency Benefits Rationalizing Banking Relationships and Accounts Managing Impact of Interest Rate and Currency Volatility In parallel, digital technologies with potential applications in treasury and finance, such as RPA, APIs, machine learning (ML), artificial intelligence (AI) and distributed ledger technology (DLT) are also developing rapidly. Treasurers are increasingly exploring the potential applications of these technologies to enhance the efficiency of treasury processes, improve treasury function oversight, facilitate decision making and scale treasury activities. There is also growing interest and adoption of RPA, ML and AI-based solutions (collectively defined as “intelligent automation”) within shared service centers, given both the scale and number of manual or inefficient processes that typically exist. Our Treasury Digitization Survey results underscore these developments; nearly three-quarters of respondents have digitization and automation among their treasury goals (Figure 1) and nearly one-in-two respondents expect digitization to be the main catalyst for change in the treasury function over the coming years. Moreover, a recent review of 25 shared service center operations located in Asia has indicated that over 40% now use some form of intelligent automation solution. From Figure 1 it is also evident that the goal of digitizing and automating treasury is second only to the objective of achieving more timely and accurate cash forecasts. Since the challenge with cash forecasting tends to be the lack of end-to-end automation, as well as access to data from fragmented systems, the goal of digitizing and automating treasury may in many cases also entail a focus on forecasting. The early stage of a shift to an “always- on” economy is expected to drive the need for a more efficient, smarter and integrated treasury operation in 2020 and beyond.