October 1, 2015 at 10:38 AM
Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
October 1, 2015 at 10:38 AM
The South African IT community read about our use of machine learning and predictive analytics to predict a victory for South Africa over Scotland in this weekend's Rugby World Cup match between the two teams. Read more here.
During the COVID-19 crisis, the media has focused much on the weak economy and stressed South African consumers. Figures show an increase in unemployment and for those lucky to be employed, many suffered decreased earnings through salary cuts. All this points to a highly strained economic environment.
The South African credit bureau TransUnion recently released data on the performance of various different products within the bureau in their ”Quarterly Overview of Consumer Credit Trends” for the third quarter of 2020. With the COVID-19 crisis, 2020 was characterised by a severe reduction in account originations and payment holidays in Q2 with a high increase in non-performing accounts in Q3 as payment holidays ceased and stressed consumers failed to pay their accounts.
It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous).