Principles for effective automation in post-trade processing - Part 1: Beware the ‘bot’ army

Each generation in operations management seems to rediscover ‘automation’ as a new solution to the challenges of cost, control and customer service. While the set of technologies available to automate post-trades processing changes over time (not always in positive ways), the fundamental set of principles for successful automation have been largely established since the 19th century. This paper reviews the historical evolution of automation in both manufacturing and services to identify the principles for successful automation. It then looks more specifically at the progress of automation of post-trade processing in capital markets over the last 50 years — something that has brought vast improvements in efficiency. Trying to increase automation in the post-trade world without understanding how complex the environment has become can lead to the misdirection of investment, use of inappropriate tools for automation and potentially make infrastructure and business processes worse. Drawing on the lessons learnt in the operations department of a major global bank, this paper describes how understanding the basic principles of automation can help ensure investment in automation is focused on the areas where it is most likely to be successful. It also describes the tools and techniques for gathering the data required to apply those principles correctly. While the lessons described are hopefully clear, the paper also explains why the relevant decision makers need to also understand the fundamental nature of operational processing, how to cost effectively acquire the right data, and how to ask the right questions of technology providers. Ultimately, any automation program, no matter how good the technology or how well funded, can fail without understanding the business context and asking the right questions. This is the first of a two-part series. Part 2, to be published in issue 13.1 of this journal, will focus on how using the right levers for increasing automation depends as much on human factors as the technology options.