Fintern utilizes AI and Open Banking to enhance credit decision-making by analyzing real-time bank transaction data, providing a more accurate assessment of consumers' financial situations beyond traditional credit scores. This approach enables underserved individuals to access affordable personal loans, significantly reducing default rates compared to market expectations.
Funding
$1B raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional credit scoring methods often rely on limited historical data, failing to accurately assess the financial health of individuals with thin credit files or those underserved by conventional financial institutions. This can result in higher interest rates or denial of loans for creditworthy individuals.
Solution
Abound offers a credit decisioning platform, Render, that leverages AI and Open Banking to provide a more comprehensive and real-time assessment of a consumer's financial situation. By analyzing bank transaction data, Render goes beyond traditional credit scores to understand a borrower's income, spending habits, and cash flow. This enables lenders to make more informed decisions, expand access to affordable credit, and reduce default rates. Abound also offers unsecured personal loans directly to consumers in the UK, utilizing the Render platform to provide fairer and more accurate loan options.
Target Audience
Abound serves both consumers seeking affordable personal loans and lenders looking to improve their credit decisioning process and expand their customer base.
Features
- AI-powered credit decisioning platform using Open Banking data
- Real-time analysis of bank transaction data for a comprehensive financial assessment
- Cash flow underwriting to determine affordability
- Alternative data analysis to expand access to credit for underserved populations
- Personal loans up to £10,000
- 70% reduction in default rates compared to market expectations