We effectively determine lending risk in applications with limited or no credit history
Nonlinear algorithmic modeling improves risk assessment in thin file clients and countries without credit modeling.
We serve countries with very limited or non-existent credit bureau utilization. In fact most of the world’s population does not have a FICO score.
We have worked extensively throughout Latin America and Asia. We developed a unique mobile lending technology for use in the Philippines cash-based economy that could efficiently disburse loans in cash across a nation of 7,640 islands.
This approach also makes us uniquely positioned to work with thin file and subprime populations. Our models have shown up to 50% of thin-file consumers will, with correct underwriting, mirror the performance of prime borrowers with overall defaults of 4%. This is the truly transformative effect of nonlinear machine learning underwriting models. A huge expansion of the playing field for lending.
Our client Tua Financial provides loans to recent immigrants to Canada who have no credit files, First Nation Canadians who have thin credit files, and through TuaPay™ provide merchants with instant customer financing options at lower cost than credit cards with no merchant fees. We created a model specific to thin-file FICO™ unscored Canadians for Tua's utilization. We also developed a voice driven loan origination process for Tua which allows an entire loan to be originated via voice on a mobile device or agent. Tua utilizes AI for all aspects of its operation, resulting in an extremely lean organization.
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