Matogen Applied Insights (MAI) used alternative data for credit scoring to create new scorecards for a financial services client.
Alternative data
Traditionally, when assessing a potential customer’s credit risk, financial services providers have had to rely on information pertaining to the individual’s past credit activities, such as type of credit obtained, and utilisation, generally obtained from credit bureaus. In contrast, “alternative data” is defined as any data not related to a client’s credit activity, yet provides crucial information about their habits, preferences, behavior, and character. It is important that alternative data originates from a source where it cannot be manipulated by the data subject. According to Experian, 65% of lenders already use alternative data to make lending decisions.
Substitute scorecards
Credit bureau data proved limited for the customers of one of MAI’s financial services client, especially in several key portfolios. However, MAI was able to build substitute scorecards using “alternative customer data” from other purchasing data accessible within the client’s data warehouse.
The MAI data scientist performed creative indirect matching to compensate for the large number of missing SA ID numbers of clients. In addition, purchasing data was analysed to reveal a plethora of attributes with good predictive strength. MAI created a framework using this novel data to generate new descriptive features and predictive models, which delivered additional insights into customers and their behaviour. These substitute scorecards were shown to perform very well compared to those produced by traditional credit bureaus.
Marketing and compliance
This alternative data credit scoring framework will be used internally on an ongoing basis to guide decision-making on marketing ethics and strategies to unlock new opportunities. For example, it would mitigate risk to the end-consumer due to over-indebtedness and incorrect product offerings. In general, the aim is to increase knowledge of South African consumers, and improve the predictive power of models, as well as boost compliance.
