Author: Limina – limina.com
You are likely familiar with the allure of automated processes and workflows: complete operating model scalability. In a more realistic scenario, automation promises to save your team time, increase their efficiency, and reduce human errors.
However, when it comes to the asset management industry, automation can be challenging due to the many small tasks being performed, difficulty defining errors, and fragmented workflows. In this article, we’ll explore these challenges and explain how exception-based workflows can be a good way to overcome them.
Challenges to automation
One of the main challenges to automation in asset management is the sheer number of tasks performed, especially in the Middle Office. These tasks are often quite small, requiring just seconds or minutes each. Looked at individually, the reward for automation usually isn’t worth the cost.
Another challenge is knowing when an error occurs. In many cases, a machine can’t detect an error on its own. This means that asset managers must rely on manual checks to ensure accuracy, despite automation initiatives. This can be time-consuming and may result in errors being missed, if not deployed right.
Finally, workflows in asset management are often fragmented. A process that span multiple systems is difficult to automate, because any machine (virtual robot) would have to be able to navigate between systems. This is a very difficult (read: expensive) type of process to automate.
However, are ways to overcome these challenges, at least partially.
What are exception-based workflows?
Exception-based workflows are a way to automate processes while still allowing for human oversight. Instead of automating every step of a process/workflow, exception-based workflows focus on identifying issues while alerting a human when a potential exception occurs. An exception can be anything from a compliance violation to a data error.
When an exception occurs, a human can step in and make the necessary corrections (i.e. resolve the issue). This allows for greater efficiency and reduces the risk of errors while at the same time keeping the cost of an automation project down.
Finally, exception-based workflows can be designed to learn from the exceptions that occur. Over time, the system can become more accurate and require less human intervention.
Exception-based workflows can be particularly useful in asset management because they address many of the industry specific challenges discussed earlier. By automating tasks partially, asset managers can save time and increase efficiency. At the same time, they can ensure that errors are caught and corrected by humans who excel at exception resolution.