Peer-reviewed,
and put to work.

In brief

Develops tailored credit-scoring models for informal merchants who have no formal credit histories, evaluating six logistic-regression and twelve support-vector-machine models on transactional and loan data from a fintech lender in Lesotho.

Abstract

Access to working capital is critically limited in the informal retail sector due to the absence of appropriate financial products. Improved credit scoring can thus facilitate increased access to working capital, supporting the growth and sustainability of small businesses in the informal sector. This study addresses this challenge by developing tailored credit scoring models for informal merchants using supervised learning techniques. Leveraging data from a financial technology company in Lesotho (South Africa), we applied logistic regression and support vector machines to predict the likelihood of loan defaults among merchants. Our methodology involved the evaluation of six logistic regression models and twelve support vector machine models, assessing their effectiveness in default prediction. The results provide a robust tool for more accurate assessment of creditworthiness, reducing the risk of lending to potential defaulters. The study underscores the potential of supervised learning methods to create impactful financial solutions and suggests a pathway towards narrowing the financial inclusion gap in the informal African economy. This approach not only aids in risk reduction for lenders but also empowers a critical segment of the economy by enabling better financial support and growth opportunities for informal sector merchants.

In brief

Shows how Tausi's scoring engine folds alternative data (utility-bill statements and digital activity) into lenders' loan-management systems, giving thin-file borrowers real-time visibility into their credit scores and limits.

Abstract

This paper explores Tausi's pioneering credit risk scoring engine, which revolutionizes credit risk modeling and lending practices by harnessing alternative data sources. Traditional credit assessment methods often overlook segments of the population lacking established credit histories, particularly in regions like Africa. The aim of this paper is to showcase Tausi's approach that addresses this gap by integrating non-traditional datasets, including social media activity and utility bill statements, to provide lenders with a comprehensive view of borrowers' creditworthiness. This approach is particularly beneficial for borrowers without traditional credit histories, offering them opportunities to access financial services that were previously inaccessible. Through the integration of Tausi's credit risk scoring engine into loan management systems (LMS), borrowers gain real-time visibility into their credit scores and credit limits, fostering transparency and informed financial decision-making. Moreover, the system proactively monitors borrower behavior, enabling automatic adjustments to credit limits based on evolving risk profiles. Regular updates to the scoring model ensure adaptability to changes in borrower transactional and repayment behaviors over time, thereby reducing the risk of default or delinquency. The adoption of Tausi's innovative approach to credit scoring has far-reaching implications for various stakeholders, including credit risk teams, lenders, government regulators, and investors. By expanding access to credit and promoting financial inclusion, Tausi's platform not only enhances economic opportunities for individuals but also fosters sustainable economic growth and development across the African continent.