Pepkor division looks to SAS®
for Real-time credit scoring

The retail sector in Southern Africa is becoming increasingly competitive. As many retailers embrace flexible and customer-centric credit offerings to attract more customers, managing the risk inherent in approving huge numbers of credit applications in a potentially fraudulent environment becomes a major business imperative.

One of Pepkor’s financial solutions divisions, Connect Financial Solutions (CFS), is responsible for helping Pepkor to stay competitive in the retail furniture and electronic goods industries by inter alia using Machine Learning and Advanced Analytics in automating its credit decisioning processes and decisions.

The right credit offer to the right customer at the right time

In a recent move signaling their desire to get the right credit offer to the right customer at the right time, CFS has opted to purchase and adopt SAS® Real-Time Decision Manager (RTDM) to allow it to effortlessly and quickly deploy changes and additions to their current credit decisioning models, which is used in real-time, allowing it to ensure that the right amount of credit is offered to the customer as and when the customer needs it.

A long relationship based on trust

JD Group via its different credit providing divisions has a long history with SAS, as the credit analysts used SAS to do its analytics, reporting and lately also to build credit scorecards. However, a major challenge was implementing these scorecards into customer-facing systems fast enough to be effective. Also integrating new sources of data (used in the new scorecards) into the credit decision process turned out to be a prolonged and complicated process. The trust built between CFS and SAS led CFS to look to the market leader in advanced analytics for a solution that could help to deploy these models and data sets in a quicker, more accurate and effective manner.

“We have historically encountered challenges around the ability to embed our advanced analytics into a real-life retail credit decisioning environment and, once SAS introduced us to SAS’s real-time decisioning engine, we knew we had found the right solution to this problem,” says Eugene Ehlers, CFS Credit Executive.

When asked what the deciding factor was that pushed the business to choose SAS as a solution, Ehlers said: “Internally, we had a big victory in one session where our CEO had asked how quickly we could integrate new data sets and scorecards into SAS RTDM, and he was told that we could do it within minutes and a demonstration of this was performed right in front of him. Another swaying factor was the practical demonstration of how a SAS website talks to RTDM, which created a big wow internally.”

He added that the value of RTDM was in its agility and flexibility and the fact that his team could implement the changes without having to look for additional people with rare skills to be able to carry out commands in the software.

Looking to the future

CFS’s expectations of RTDM include its ease of integration into their retail systems and its user-friendliness – namely the ability to change the decisioning processes within the business without complex IT or coding involvement. However, Ehlers observed that RTDM was more than just a credit engine and its biggest benefit was its potential to be used to automate even more analytical decisioning across the customer lifecycle.

“You shouldn’t see it as a marketing tool or a risk tool. We call it the “SAS as a brain” concept, anywhere you need to make a decision you can actually use RTDM to make the decision and that’s what we mean by potential, that’s what we’re trying to build with SAS.”

Moving from a product-centric to a customer’s needs approach

With its versatility, CFS is also looking to possibly use SAS RTDM, in future, to take the company from a product-centric approach to one more in tune with the customer’s needs – allowing it to gain a competitive edge in South Africa’s fast-paced and constantly innovating retail industry.

“We’re now bringing RTDM in for one purpose, but I think the real value and differentiation lies in how we use it, as the whole organisation progresses to being a real online player,” adds Ehlers. “Analytics is everywhere, and we see many future uses of analytics and Machine Learning in not only in the traditional sense but combining it with the analytics underlying voice recognition, facial recognition and biometrics – all within a “SAS Brain”.”