Tausi: A Holistic Artificial Intelligence Approach to Credit Scoring Using Informal Data for a Sustainable Micro-lending African Economy

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 bills 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 and can be replicated across the African continent.