From Credit History to Operational Behavior: A Framework of Operational Creditworthiness for Thin-File Commercial Vehicle Borrowers in Emerging Markets

Abstract

Traditional commercial vehicle underwriting in emerging markets relies heavily on credit bureau histories, collateral values, and standardized asset-depreciation grids. This approach often excludes First-Time Users and First-Time Buyers who operate viable transport businesses but lack a formal credit record. Drawing on longitudinal field observation from the Indian Non-Banking Financial Company (NBFC) sector, this paper develops a qualitative, practitioner-grounded framework for assessing thin-file commercial vehicle borrowers through operational behavior rather than historical credit visibility.

The study contrasts two observed underwriting tracks within a single lending ecosystem: a conventional bureau-led approach and an alternative operational approach. It proposes evaluating repayment capacity through route stability, freight-contract continuity, digital transaction velocity, driver vintage, and asset-driver fit, and translates these signals into an Operational Underwriting Scorecard intended for field deployment by credit and risk teams.

The paper argues that operational creditworthiness offers a more context-sensitive reading of risk in informal logistics markets, particularly where bureau records are absent or uninformative. Stable corridors, recurring enterprise freight arrangements, consistent digital inflows, and experienced operators can signal repayment capacity that bureau history alone would miss. The framework also supports earlier risk detection by surfacing operational disruptions, such as route fragmentation or contract loss, before they appear as formal delinquency.

Positioned within the enterprise-development, financial-inclusion, and SME-finance literatures, the framework reframes thin-file borrowers not as inherently risky but as under-observed. By widening access to productive-asset finance, it also speaks to enterprise formation and livelihood mobility among informal-sector operators. Distinct from digital-footprint models that draw on personal device, social, or call-log data (Agarwal et al., 2019; Berg et al., 2020), the proposed approach relies only on consented, aggregate operational and transactional signals, aligning it with consent-based digital public infrastructure and contemporary data-protection standards