Lenders face significant challenges when attempting to assess the creditworthiness of underbanked and credit-invisible individuals. Traditional credit scoring systems rely on formal credit histories, excluding millions of potential customers. This lack of data leads to higher onboarding costs, increased risk, and missed opportunities to serve a growing market segment.
The Solution
Access to Alternative Data: eSuSuSocial leverages telco usage, social connections, digital footprints, and transactional behaviors to build comprehensive credit profiles for individuals without formal credit histories.
Pre-Qualified Leads: The platform provides lenders with ready-to-engage, pre-qualified borrowers, reducing the time and cost of customer acquisition.
Expanded Customer Base: By addressing the needs of underserved segments, lenders can tap into a broader market and drive financial inclusion.
The Underwriting Process
eSuSuSocial’s underwriting leverages advanced AI and machine learning algorithms to analyze a borrower’s connections on the network, alternative data, including telecom usage, social media behavior, and transactional history.
The system integrates natural language processing (NLP) to assess public data, such as social media activity, to evaluate signs of trustworthiness and reliability.
Predictive analytics are applied to assess the borrower’s likelihood of repayment, going beyond traditional metrics like credit scores.
Real-time scoring ensures that lenders receive accurate, up-to-date insights during the decision-making process.
