Kenyan lending apps employ various alternative methods to assess borrowers’ creditworthiness without relying solely on CRB data. Some common approaches include:
Alternative Data Sources: These apps often analyze alternative data such as mobile money transaction history, utility bill payments, airtime usage patterns, and other financial behaviors to gauge an individual’s creditworthiness.
Employment Verification: Verifying an applicant’s employment status and stability by requesting employment information, payslips, or using employment verification services.
Bank Statements: Applicants might be required to provide bank statements or allow access to their bank accounts for scrutiny of financial transactions and balance history.
Social Media and Online Behavior: Some apps may consider social media profiles, online presence, or digital footprint as indicators of an individual’s financial responsibility and behavior.
Behavioral Analysis: Analyzing the borrower’s behavior within the app, such as repayment patterns for previous loans (if applicable), timeliness of payments, and interactions with the app’s features.
Machine Learning and Algorithms: Advanced algorithms and machine learning models might be employed to analyze various data points and predict creditworthiness based on historical patterns.
Psychometric Assessments: In some cases, psychological and behavioral assessments are used to determine the applicant’s propensity to repay loans based on their responses to certain questions or scenarios.
By using a combination of these alternative methods and technologies, lending apps can form a more comprehensive view of an applicant’s creditworthiness beyond traditional CRB data, enabling them to make informed lending decisions.
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