How Societe Generale implemented a workflow platform for stock trading

The bank multiplied task completion and overall efficiency by working toward a workflow mind-set across the organization

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As an organization that sends thousands of emails per day to carry out client orders, financial services group Societe Generale required a system that told users what tasks needed to be completed and reasons a particular process was pending.

In less than three years, the business implemented its own workflow platform, SG workflow, onboarding more than 7,500 users, who deployed 180,000 tasks in the first quarter of 2020 alone.

Simon Letort, chief digital officer and head of innovation for the Americas at Societe General, explained at CamundaCon 2020 that “scaling the modelling process was one of the biggest challenges” for the organization.

By scaling process modeling, Societe Generale doubled the total count of implemented workflows, multiplied the total amount of tasks performed by six and increased the total number of active users on the SG workflow platform tenfold in just six months. Key to this success was a ‘DIY modeler’ implementation strategy aimed at high user traction.

Today 1,200 individual users, or DIY modelers, model workflows on the SG workflow platform, allowing for a workflow mind-set 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 uses these components for the workflows they want to create, without having to deal with actual code. These components include, for example, application user interface (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 as Letort notes, banks tend to be “experts for back ends but not for front ends”. In other words, only computer scientists could build workflows. Removing this obstacle saved the company time, because DIY modelers are now able to help themselves instead of waiting for the IT department to process change requests.

Unsurprisingly, it takes more time for the DIY modelers 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 explains that the important metric is not how much time the DIY modelers take to model a workflow, but how much time they save the experts, who then invest their extra time in focusing on deeply complex workflows that are 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 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 SG workflow have already been set. By the end of 2020, the whole platform will be migrated to the Azure cloud and prepared for process mining, with DIY modelling rolled out to more than 30,000 users.

This article was originally published as How Societe Generale scaled its workflow portfolio in record time.

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