Singapore’s CrediLinq raises $8.5M Series A funding led by OM/VC and MS&AD Ventures

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1982 Ventures’ portfolio company CrediLinq, the Singapore-based artificial intelligence (AI)-powered embedded finance platform, announced last Saturday the close of its $8.5 million Series A funding round.

CrediLinq said in a statement the round was led by OM/VC and MS&AD Ventures.

New investors include Citi North America and Rustem Family office, while returning investors include 1982 Ventures, 500 Global, Epic Angels and Big Sky Capital.

According to the statement, the funds will be deployed to drive market expansion, strategic acquisitions and partnerships in the United States, United Kingdom and Australia; boost local presence in Singapore; hire senior commercial, product and tech talent and invest in further technology enhancements.

CrediLinq will immediately accelerate geographic expansion, starting with the United States and subsequently into the United Kingdom and Australia.

The company will look to partner with larger digital platforms in these markets to drive user growth, enabling digitally native businesses transacting on platforms to access capital.

CrediLinq will strengthen its leadership bench in sales, marketing, product and tech to support expansion in the target markets.

A significant portion of the funds will also be invested in enhancing the company’s technology stack — with continual improvement of AI-led credit algorithms that use the SME’s real-time digital footprint including platform data, unstructured data, bureau information and the like, to reduce non-performing loans (NPLs), improve ‘Default On’ collections, and build agentic workflows to drive efficiency.

“Today marks a pivotal moment for CrediLinq as we accelerate the growth of embedded finance globally, helping platforms empower digital native small and medium-sized enterprises (SMEs) with flexible, transparent and more seamless access to capital,

“With this new funding, we are excited to deepen our partnerships, expand into new markets, and invest in senior talent and technology infrastructure that will support the next phase of our growth,” said Deep Singh, Founder and Group Chief Executive Officer of CrediLinq.

Vikram Kotibhaskar, Co-Founder of CrediLinq, added the embedded credit revolution continues to gain momentum, and the firm is at the forefront of this innovation.

“By being integrated within platforms, our credit-as-a-service stack leverages API connectivity, transactional data and credit algorithms for quick decision-making at the point of need and offers easy checkout within the partner ecosystem,” he said.

According to him, this results in a fast, frictionless and transparent customer experience and more ways for platforms to monetize their business.

“Our agentic workflows also drive efficiency and continuous monitoring to keep loan losses low,

“Platform partners benefit with higher stickiness, trust, and scalability that drives up gross merchandise value (GMV),” he added.

CrediLinq is a technology infrastructure company with a proprietary Credit-as-a-Service stack.

Their API-centric approach enables business to business (B2B) platforms and banks to build financial products for merchants, enabling the financial empowerment of underserved digital native SMEs to build, grow and scale their business.

The firm’s solutions can be embedded across platforms that cover verticals of procurement, supply chain, e-commerce marketplaces, freelance platforms, banking, accounting, and payments.

In the e-commerce space for example, the firm has integrations with key marketplaces such as Amazon, Lazada and TikTok Shop.

“This Series A round, with the backing of global financial leaders like Citi, is a testament to CrediLinq’s vision and execution,

“We are proud to continue supporting Deep, Vikram, and the CrediLinq team as they scale internationally and empower more SMEs to thrive in the digital economy,” said Herston Powers, Founding Managing Partner of 1982 Ventures.

 

#EmbeddedFinance #SMEGrowth #FintechInnovation #AIinBanking #StartupFunding

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