In less than three years Societe Generale created its own workflow platform – SG workflow – onboarding more than 7,500 users who have successfully deployed an astonishing 180,000 tasks in the first quarter of 2020 alone.
Why implement workflows for stock trading? The answer is simple. Societe Generale, one of the leading European financial services groups, still sends thousands of emails per day to carry out client orders. What the bank needed was a system that told users what tasks needed to be completed and why a particular process was pending.
“Scaling the modelling process was one of the biggest challenges for us,” Simon Letort, Chief Digital Officer and Head of Innovation for the Americas, explained in his presentation at CamundaCon 2020.
But with this impressive scaling of process modeling, in just six months Societe Generale doubled the total count of implemented workflows, grew the total amount of tasks performed sixfold and increased the total number of active users on the SG workflow platform tenfold. Key to this success was a sophisticated ‘DIY Modeler’ implementation strategy aimed at high user traction.
Today 1,200 individual users – DIY Modelers – model workflows on the SG workflow platform, empowering a workflow mindset across the company. This was made possible by 30 expert modelers who work within several centers of expertise. Their job was, and still is, to model complicated processes and simplify the work of the DIY modelers. For example, they have integrated additional tools such as form.ui and drag and drop components on the central platform. Each DIY modeler can easily use these components for the workflows they want to create, without having to deal with actual code. These components include, for example, API calls for applications that have to be integrated into a given process. Only expert modelers are allowed to call REST APIs directly.
Providing a user interface was key to getting management support for the whole DIY concept because banks tend to be “experts for back-ends but not for front-ends” Letort said. In other words, if you weren‘t a computer scientist, you couldn‘t build workflows. Removing this obstacle saved the company significant time, because DIY modelers were now able to help themselves instead of having to wait for the IT department to process change requests.
Of course, the DIY modelers need more time to model a workflow than the expert modelers. While the 1,200 DIY modelers have so far created around 300 workflows for 30,000 tasks, the 30 expert modelers have produced 250 workflows for 100,000 tasks. However Letort explained that the important metric is not how much time the DIY modelers took to model a workflow, but how much time they saved the experts. The experts then invest their extra time to focus on deeply complex workflows that are absolutely critical for business success.
The centers of expertise also make work easier for the DIY modelers by providing best practices and entering metadata within the individual components that are used to automatically generate documentation. This auto generated manual already contains more than 400 pages that are always up to date and appealing to the supervisory authorities. In addition, because of these instructions, and the components tested by the expert modelers, the development team also receives fewer inquiries and can invest more time in programming.
The next big goals for SC workflow are already set. By the end of this year, the whole platform is going to be migrated to the Azure cloud and prepared for process mining, with DIY modelling rolled out to more than 30,000 users.