Alternative Data Scoring

Alternative Data Scoring builds risk models on the data lenders actually have about thin-file and new-to-credit applicants: mobile top-up patterns, airtime and data usage, utility bill payment history, device and app behavior, and transaction flows from wallets and merchant accounts. Where a bureau file is empty or stale, these signals fill the gap with a live, high-frequency picture of financial behavior.

Each data source is transformed into engineered features and fed into models trained specifically to separate good risk from bad risk in populations that traditional scorecards were never built for. The result is a score that extends credit to previously invisible customers without asking a lender to guess, and without waiting months for a bureau history to accumulate.