Year: 2024
Type: article
Abstract: This study examines the merchant credit assessment model of Square Capital, a Block subsidiary, representing an innovative approach to small business financing in the fintech sector. Traditional credit assessment methods often fail to effectively serve small businesses due to information asymmetry and lack of collateral. Square Capital utilises real-time transaction data and advanced machine learning algorithms to provide more accessible and efficient financing solutions. The research methodology comprehensively analyses Block’s annual reports, industry data, and relevant academic literature. The study explores Square Capital’s unique data sources, assessment factors, and machine learning algorithms, highlighting the differences between this model and traditional lending practices. Results indicate that Square Capital’s model has demonstrated excellence in loan growth, risk control, and customer satisfaction, facilitating $1 billion in loans in 2023, an 18% year-over-year increase. This paper argues that Square Capital’s data-driven approach represents a significant innovation in small business financing, potentially setting new standards for the fintech industry. However, challenges remain in balancing innovation with regulatory compliance and risk management as the business scales. The study also discusses future research directions and implications for traditional financial institutions.
DOI: 10.62051/sk87w978
Author: Junyu Chen
Institution: University of Connecticut
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4405396204