A fast-growing Indian supply chain finance fintech relied on diverse alternate data sources to assess working capital risk and customer eligibility. These included digitized bank statements, director and entity information, business linkages, and internal customer records. While the signals existed, they were fragmented across vendors and formats. Business teams lacked a unified, trusted view of customer cash flows, relationships, and operating context.
Alternate data is valuable only when it can be trusted and reused. Fragmentation, inconsistent quality, and weak linkage increase risk rather than reduce it. The fintech needed a decision-ready foundation that could support underwriting, monitoring, and portfolio analytics without creating brittle, one-off integrations that fail as scale and use cases expand.
Dhurin designed a scalable data foundation that brought together bank statement data, director and firm-level attributes, relationship signals, and internal customer records into a harmonized, analytics-ready layer. Data was standardized, validated, and linked at customer and entity levels. Automation, reconciliation, and quality controls were embedded by design to ensure reliability as volumes grew. The foundation was built to support downstream models, BI, and future decision workflows without repeated reintegration.
The fintech gained a dependable decision foundation that converted fragmented alternate data into a reusable intelligence asset. Teams were able to use cash-flow, relationship, and entity signals consistently across risk and growth use cases, accelerating decisions and improving confidence while creating a scalable platform for future expansion.
