As South Africa grapples with high levels of financial exclusion, the need for more inclusive, accurate, and dynamic credit risk assessment has never been more urgent. Traditional credit scoring methods often fall short in capturing the full picture of a borrower’s financial behaviour, especially for individuals with limited or no credit history. In this context, alternative data has emerged as a powerful tool that can reshape credit risk modelling, enabling lenders to make more informed and equitable decisions.
This raises the question – how can alternative data be used to enhance credit models in South Africa, and how can organisations implement these approaches responsibly and effectively?
What is Alternative Data?
Alternative data refers to non-traditional information sources that are not typically included in conventional credit bureau reports. These data points can include:
- Utility and mobile phone payment records.
- Rent payment history.
- E-commerce and social media activity.
- Geolocation data.
- Transactional data from mobile money and digital wallets.
- Psychometric data.
When used ethically and with appropriate consent, alternative data provides a more nuanced view of a borrower’s financial habits and reliability.
Why Traditional Models Fall Short
South Africa’s traditional credit models heavily rely on historical data from formal financial institutions, leaving a large portion of the population underserved. According to various reports, millions of South Africans are either unbanked or underbanked, meaning they have little to no interaction with traditional financial systems. As a result, many capable borrowers are excluded from credit access simply because they lack a conventional credit history. This not only limits consumer opportunity but also hampers lenders’ ability to grow and diversify their portfolios.
The limitations of traditional models are particularly evident in a country with a large informal economy. Many individuals earn income and manage their finances outside of formal banking channels, making their financial behaviour invisible to traditional credit bureaus. This gap in data creates a self-perpetuating cycle of exclusion, where a lack of credit history prevents access to credit, which in turn prevents the establishment of a credit history.
The Power of Alternative Data in Credit Scoring
Incorporating alternative data into credit models allows for a more comprehensive risk assessment. For example, consistent purchase of mobile airtime or payment of electricity bills may indicate financial responsibility, even in the absence of a credit card or personal loan. Analysing mobile money transaction patterns can reveal income consistency, spending habits, and even financial stress. These insights can help lenders distinguish between genuinely risky applicants and those who simply lack a traditional credit file.
Moreover, data from digital behaviour—such as online shopping frequency or app usage—can offer real-time indicators of financial stability. When combined with traditional metrics, these insights can significantly enhance predictive power and improve default prediction rates.
Psychometric insights, such as those available through Principa’s online platform ADMiT, can tell us more about a person’s intent and willingness to pay, or the entrepreneurial potential of a prospective small business owner.
Alternative data can also be used for real-time credit checks, providing a more dynamic and up-to-date assessment of a borrower’s financial health. This is particularly valuable for short-term loans or instant credit products. Furthermore, this data can help in creating more personalised loan products with tailored interest rates and repayment schedules, leading to better outcomes for both the borrower and the lender.
Implementation Considerations
While alternative data holds great promise, its integration must be handled carefully.
- Data privacy and consumer consent are paramount. Organisations must adhere to the Protection of Personal Information Act (POPIA) and ensure that customers fully understand how their data will be used. Transparency builds trust and ensures regulatory compliance.
- From a technical standpoint, incorporating alternative data requires investment in data infrastructure, analytics capabilities, and model validation processes. Lenders should collaborate with data science teams or specialised consultancies to develop robust models that can process, analyse, and interpret diverse data streams.
- It is also essential to continuously monitor model performance and update risk strategies in line with evolving market behaviour.
A Path Toward Inclusive Lending
Alternative data has the potential to transform lending in South Africa by opening access to millions of previously excluded individuals. For lenders, it offers an opportunity to expand their customer base, reduce defaults, and design more targeted products. For borrowers, it provides a fairer shot at financial inclusion based on actual behaviour rather than legacy systems.
In a country as economically diverse as South Africa, credit models must evolve to reflect the realities of its people. Alternative data is not a replacement for traditional metrics, but a valuable complement that adds depth and relevance to risk assessment. By leveraging these new data sources thoughtfully and ethically, financial institutions can drive a more inclusive, resilient, and data-driven credit ecosystem.
Principa have developed an online platform called ADMiT, allowing lenders to source and utilise alternative data such as psychometric credit scores. The platform provides an alternative distribution and collection mechanism, as well as low-cost lead generation with marketing consent for your brand.
Need guidance on integrating alternative data into your credit models? Need to hear more about ADMiT? Contact our team to explore tailored solutions that align with your risk strategy and compliance needs.
To learn more about ADMiT, visit ADMiT | Your ticket to credit.