Reconciliation as the Operational Backbone of Off-Balance-Sheet Trade Finance: A Case Study of Payment Advice Processing at Great Kapital (Ventura Trading Private Limited)

Abstract

Abstract: India's supply chain finance (SCF) industry exists to close the timing gap between when a corporate buyer pays its suppliers and when it collects from its own customers, a market valued at roughly USD 7.6–9.3 billion in 2025 and projected to nearly double by the early-to-mid 2030s. Most of the writing on this sector, in consulting reports and the trade press, focuses on financing structures, credit exposure and regulatory design. Comparatively little attention goes to the operational work underneath all of it: reconciling who owes what to whom across banks, group entities and document formats, so that a financing structure actually holds up when someone checks the numbers. This case study examines that operational layer through a ten-week internship placement in the Finance and Accounts function of Great Kapital (Ventura Trading Private Limited), a structured trade finance intermediary that takes principal risk directly in the trades it finances rather than operating as a matching platform. Drawing on direct participant observation, the study documents three reconciliation workflows built during the internship: a multi-entity bank reconciliation statement, an automated daily repayment report, and an invoice-level payment advice reconciliation. It finds that the company's central day-to-day operational risk was not credit or market risk but format risk: inconsistent column layouts, duplicate column labels, and unstandardised entity codes across group companies. Standardising import templates and replacing manual re-entry with live formulas measurably reduced false-flagged discrepancies and turnaround time. The case study argues that for a company whose business model depends on off-balance-sheet treatment holding up under scrutiny, disciplined reconciliation infrastructure is not a back-office afterthought but a determinant of how far the model can scale.