What Is Data Enrichment?
Data enrichment refers to enhancing existing data with new, useful attributes. In MCA and small business lending, raw packets often only provide the basics, such as applications, bank statements, and IDs.
Enrichment adds more context, such as identifying industry, highlighting unusual patterns, or connecting external data sources to strengthen the view of an applicant.
Data enrichment typically appears after scrubbing, once the basic fields are parsed and structured. Operators use it to improve accuracy, reduce manual research, and create packets that are more complete for underwriting decisions.
How Does Data Enrichment Work?
Data enrichment works by combining cleaned inputs with additional signals.
- Parsing baseline data: Values such as revenue, balances, and applicant details are extracted.
- Adding context: Additional data points such as industry codes, recurring expense detection, or seasonal patterns are layered on top.
- Signal generation: Risk flags or positive indicators are added to highlight trends.
- Structured outputs: Enriched fields are delivered in the CRM for underwriting review.
In Heron, enrichment is applied as part of the standard workflow.
- Captured submission: ISO packets or broker emails flow into Heron.
- Automated scrubbing: Data is cleaned, normalized, and validated.
- Enrichment step: Useful context is added, such as transaction insights, derived metrics, or cross-checks that improve underwriting readiness.
- CRM write-back: Both the core fields and enriched signals are written into the CRM.
This creates a more complete record without requiring underwriters to do extra research.
Why Is Data Enrichment Important?
For brokers and funders, data enrichment is valuable because raw data often leaves questions unanswered. By adding context, underwriting teams get a clearer, faster view of applicant health.
It also helps scale operations. Instead of hiring staff to research, compare, or calculate, enrichment builds those insights into the packet automatically. With Heron, enrichment reduces manual work, improves decision clarity, and speeds up funding.
Common Use Cases
Data enrichment strengthens submissions in multiple ways.
- Highlighting recurring debt payments from bank statements to reveal obligations.
- Adding calculated metrics such as average daily balance or true monthly revenue.
- Detecting transaction anomalies that suggest unusual cash flow.
- Connecting identifiers like business names to industry categories.
- Writing enriched signals back to the CRM to support underwriting.
FAQs About Data Enrichment
How does Heron apply data enrichment?
Heron enriches submissions by calculating financial metrics, adding risk signals, and highlighting context such as recurring payments. These are written into the CRM alongside core fields.
Why does data enrichment matter in MCA underwriting?
Enrichment gives underwriters a more complete picture of a business without extra research. This reduces errors, saves time, and improves funding confidence.
What outputs should teams expect from data enrichment?
Teams receive CRM fields that go beyond raw data, including derived financial metrics, transaction flags, and contextual signals that make underwriting faster and more reliable.