Drip Capital Targets SME Trade Finance Gap With AI-Driven Underwriting

A LinkedIn post from Drip Capital highlights commentary by CEO Pushkar Mukewar at Davos on what the firm describes as the “SME squeeze” in global trade finance. According to the post, while global trade volumes appear resilient, the financing infrastructure, particularly traditional banking cycles, is portrayed as lagging behind the needs of small and mid-sized exporters.

The post suggests that Drip Capital is focusing on practical applications of artificial intelligence and machine learning to address this gap. By using what it calls “native machine learning underwriting,” the company indicates it aims to convert operational data from businesses into a bankable asset, effectively serving as “invisible collateral” to unlock working capital.

From an investor perspective, this emphasis on AI-driven underwriting could signal an effort to differentiate Drip Capital within the trade finance and fintech space. If successfully executed and scaled, such a model could potentially improve risk assessment, reduce underwriting costs, and expand the addressable market of underbanked SMEs in cross-border trade.

The framing of a $2 trillion opportunity in global trade finance points to a large target market and suggests ambitions for significant growth. However, the approach also implies exposure to credit, data quality, and regulatory risks that are typical for alternative underwriting models in trade finance, all of which investors may weigh when evaluating the company’s long-term positioning.

The focus on building a “more inclusive, predictive, and resilient supply chain,” as described in the post, aligns with broader industry themes of supply chain digitization and resilience post-COVID. For Drip Capital, sustained execution in AI-based underwriting and risk management will likely be key determinants of whether this strategy can translate into durable competitive advantages and scalable revenue growth.