The challenge
Alternative lenders and financing brokers rely on information scattered across banking, accounting, commerce and other operational systems. Collecting that information is often slow, inconsistent and dependent on manual interpretation.
Each source uses different entities, formats and definitions. Transaction records may require classification, financial concepts may be represented differently, and incomplete source data complicates comparisons.
For lenders, the problem is not merely accessing more data. It is creating a trustworthy and usable representation of a business. For merchants, the problem is repeated requests for information they have already supplied elsewhere.
Our approach
- 01
Design a common financial-data model
The platform was developed around the principle that financial-data acquisition and preparation should be reusable infrastructure rather than custom work for every financing application. We transformed heterogeneous records into structured financial entities and metrics, establishing a boundary between raw provider-specific data and downstream financial analysis.
- 02
Separate connectivity from financial interpretation
External connectors were designed as distinct components rather than embedding source-specific rules throughout the underwriting workflow. Normalization, transformation and aggregation could then evolve independently of individual providers.
- 03
Build for multiple consumers
The architecture considered brokers, lenders, underwriters and other financial applications as potential consumers of the same prepared data. Reusable services reduce dependence on a single end-user application or workflow.
- 04
Account for security, traceability and operational control
Financial-data processing requires careful handling of access, credentials, transformations and downstream usage. These concerns were treated as architecture responsibilities rather than left exclusively to application-level implementation.
The outcome
The work established a reusable product foundation for acquiring, structuring and transforming business financial data for financing workflows.
The platform's published value proposition describes a potential 60–80% reduction in data-collection time through automation and normalization; this is a stated product benefit rather than a separately verified measurement from a named customer engagement.
More importantly, the architecture created a separation between the underlying financial-data infrastructure and the applications that consume it.