Plentina

Plentina is a Philippine fintech startup, publicly launched in October 2020, that offers buy-now-pay-later installment loans repayable through e-wallets, using machine-learning models to assess the creditworthiness of applicants who lack a conventional credit history. It was co-founded by Earl Valencia and Kevin Gabayan, both data scientists with prior experience at firms including Google and Charles Schwab, with a specific focus on helping unbanked and thin-file Filipino consumers establish a usable credit record over time rather than simply extending one-off loans.

The company built its early traction through startup accelerator programs rather than a large marketing budget, going through Techstars’ Western Union-sponsored program and Stanford’s StartX accelerator, and later partnering with e-commerce platform Shopee to offer BNPL financing at checkout. Plentina raised a $750,000 pre-seed round from investors including Techstars, Emergent Ventures, and the 500 Startups Vietnam Fund, followed by a $2.2 million seed round co-led by former Tableau/ClearGraph executive Andrew Vigneault alongside Unpopular Ventures and DV Collective, and a further $2.2 million round soon after — bringing its total disclosed funding since founding to roughly $5.7 million.

Plentina’s positioning — building a credit-scoring layer for consumers who have never had a formal credit product before, rather than only offering short-term financing — distinguishes it from purely transactional BNPL competitors in the same market.

A large share of Filipino consumers have no formal credit history at all, which locks them out of both traditional bank credit and many newer digital lending products that still rely on some baseline credit signal.

Unbanked or thin-file Filipino consumers who want short-term installment credit for everyday purchases and, over time, a usable credit history built from their repayment record.

Plentina's underwriting is built around establishing a first credit history for a borrower rather than treating each loan as a one-off transaction, using machine-learning-based creditworthiness scoring designed for applicants who have no prior credit file to draw on.