Commercial Bank International (CBI) has announced its investment in, a fintech startup working with the application of generative AI in lending. The investment includes a post-seed bridge round investment into, alongside a group of global investors, ahead of the company’s planned Series A raise which is due to take place later in 2024.

Having crossed $1m in annual recurring revenue at more than 20% compounded monthly growth rate since the launch of sales, is already operationally break-even.

In addition to global expansion with major financial institutions across continents, the fintech is eyeing microfinance in Southeast Asia and credit unions in North America in particular, along with a foray into underwriting in the insurance industry.’s flagship software, zypl.score, enables financial institutions to optimise credit decision models across retail and SME portfolios through the generation of synthetic data. Over 35 banks across 12 markets in Eurasia have deployed’s proprietary AI-as-a-service software.

“We look forward to supporting’s growth both regionally and internationally”

Ali Sultan Rakkad Al Amri, CEO of CBI, said “Our partnership with, an innovative AI platform, signifies a strategic entry into using AI implementation for advanced credit scoring. By harnessing’s synthetic data AI powered algorithms, we’re confident we can further enhance our operational efficiencies to ensure a swift and seamless lending experience.

“Our work and investment with zypl,ai aligns well with our commitment to supporting innovation in the UAE and with our strategic ambition to partner with only the most innovative partners in the financial technology space. We look forward to supporting’s growth both regionally and internationally.”

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Azizjon Azimi, Founder of, added: “The $27bn credit scoring industry globally has been constrained to a vicious, legacy cycle of building decision models on historical data. This has repeatedly reproduced the same suboptimal results of high rejection and default rates across geographies, banks and segments. Our approach provides a radical alternative.”