One common risk management platform across 17 factoring entities

• This European factoring leader enjoys a consolidated view of its clients’ risks by connecting all its entities’ applications and systems.

Many organisations make use of factoring, by which their customer invoices (= account receivables) are sold to a factor, usually a bank, so that the organisations directly receive the value of their invoices.

When this large European bank grew its factoring activity by consolidating 17 entities, a coordination centre was set up and asked to provide a holistic view of the risks borne by the 17 entities worldwide. But those entities didn’t use a common operating system and were applying different risk policies. There was an urgent need to align all the factoring entities on transversal concepts and risk definitions, and to have a system to consolidate the risk positions of all the entities.

We deployed a dataFaktory, to which the data sources of the 17 factoring entities were connected, and uploaded automatically every month, with quality checks performed on the data. Cross-entity reporting offers multiple views, including trend analysis, data quality reports and performance indicators.

In less than 3 months, the management was able to get a consolidated view of its clients’ risks. The local risk managers receive relevant risk reporting, taking into account the group’s guidelines as well as the local specific reality.

The Head of Global Risk said: “We needed to provide within 3 months’ time a global overview over the risks associated with the activities of our 17 worldwide factoring entities, each working with its own IT system.
Using dFakto’s methodology and platform, we aligned the risk definitions across our 17 factoring entities, and the 90 data sources of the local systems have been aggregated into a dataFactory, with 400 enrichments and calculation rules, producing 30 automated reports. We now apply one common risk policy throughout all our entities, enabling our local risk managers to make better decisions based on accurate and comparable data.”