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Klarity: How Konfir verifies data

How Klarity turns raw data into verified results.

Jacob avatar
Written by Jacob
Updated over 2 weeks ago

Overview

After your applicant has consented to share their employment/income data with you via the Applicant Journey, Konfir applies Klarity to convert that raw data into a structured verification result you can trust.


How it works

The Verification Engine operates in three conceptual stages. Each stage exists to solve a specific problem that arises when working with real-world employment and income data.

Stage

What the engine does

Why it matters

Output

1) Cleaning

Evidence from different sources is standardised into consistent formats.

Enable processing

Clean data

2) Processing

(Klarity)

Related evidence is grouped into activities over time.

Removes the need for manual reconciliation.

Timeline

Activities

3) Decisioning

Consistent rules and tolerances are applied.

Produces simple, structured results for review.

Statuses

Alerts & insights

Tip: The Verification Engine runs and updates whenever new data is connected via the Applicant Journey. If you open a verification while the applicant is still in progress, you may see partial evidence or changes over time. This is expected.

Cleaning

Different data sources describe employment and income in different “shapes”. Cleaning standardises this evidence into a common internal format so it can be compared consistently. This can include:

  • extracting structured fields from uploaded documents

  • normalising dates, labels, and naming conventions

  • filtering out unrelated or irrelevant data

  • grouping and categorising income transactions by payer and type

Processing (Klarity)

Processing is where Konfir builds the timeline of activities you see in your verification result. This stage is driven by Klarity, Konfir’s proprietary model for evaluating employment & income data.

Employment and income data is messy. Klarity evaluates employer data and contextual information together to clean up originally messy data and build the strongest possible verification

Example

  • Applicant declares: Marks & Spencer, start 01/01/24

  • Banking shows payer: M&S, recurring from 28/01/24

  • Payroll shows employer label: Jaeger (group company), paid 30/01/24

Outcome: Klarity links these into a single employment activity

Decisioning

Decisioning is the final stage of the engine. At this point, Konfir applies consistent rules, thresholds, and tolerances to the processed timeline and assigns the outcomes used operationally, including:

  • Activity statuses - Verified, Connected, Not Verified

  • Alerts & insights highlighting discrepancies or risks

These outcomes are designed to support your decision-making, not replace it. For definitions and interpretation guidance, see: Understanding Results

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