Consumer credit

Get decision data AI-ready and examiner-readable.

Your credit policy, decision data, servicing records, and exceptions need to tell the same story. Novendor builds the operating layer that connects those records, governs the definitions, and gives risk, compliance, and operations teams answers they can defend.

  • Loan origination
  • Decision engine
  • Bureau & KYC
  • Servicing
  • Complaints
  • Examiner reporting

The situation

The data exists. The record is scattered.

Consumer credit teams already have the ingredients: application data, bureau attributes, credit policy, decision outcomes, servicing events, complaints, and exceptions. Each record lives in a different place. When an examiner, partner bank, or risk committee asks why a decision was made, the answer often requires screenshots, exports, and analyst reconstruction. AI helps once the underlying record is complete enough to inspect.

The operating layer

AI operating layerReads applications, decisions, servicing events, complaints, and policy rules; checks them against approved definitions; drafts risk and compliance outputs; and flags exceptions against records your team has approved.
human-approved

1. Source systems

  • Loan originationApplications, offers, approvals, declines
  • Decision enginePolicy rules, scorecards, overrides
  • Bureau & KYCAttributes, identity, fraud signals
  • Servicing platformPayment history, hardship, collections
  • Complaints & disputesCFPB, internal queues, vendor portals
Your data already lives somewhere.

2. Pipeline & integration

  • Pull from origination, decisioning, servicing, and compliance sources
  • Normalize applicant, account, decision, and event records
  • Track state, retries, exceptions, and source freshness
Records move reliably.
Owned end to end.

3. Data modeling & warehouse

  • Data model for application, decision, account, borrower, and servicing events
  • Warehouse on Databricks, Snowflake, Azure, or your existing cloud stack
  • Modeled around risk, compliance, examiner, and partner-bank questions
Structured. Modeled.
Built for decisions.

4. Semantic layer & governance

  • Policy & definitions
  • Decision logic
  • Access & PII
  • Data quality
  • Policy definitions tied to decision records, including which rule version applied
  • Approval, decline, adverse action, exception, and override logic defined once
  • Role-based access, lineage from output to source, audit trail for overrides
One definition.
Trusted by every team.

5. End users

  • Risk leadersPortfolio and policy performance
  • ComplianceExaminer-ready record, evidence, and partner-bank packages
  • OperationsQueues, exceptions, and SLA views
  • AnalystsTrusted self-serve decision data
  • AI agentsDraft, check, and summarize against governed records
Trusted data.
Used by people and agents.
SourceIntegrateModelGovernDeliver

Control · continuity · auditability

Who this is for

The lender preparing for examiner scrutiny

A regulator, partner bank, or internal audit team needs a clear record of decisions, exceptions, complaints, and policy logic. The data exists today, and the evidence trail takes too much manual work to assemble.

The team changing credit policy

Policy is moving faster than the reporting layer. Analysts are comparing vintages, score bands, exceptions, and overrides across exports, with no governed record of what changed and when.

The firm with too many manual reconciliations

Origination, decisioning, servicing, and compliance teams each trust their own export. Quarter-end and audit requests turn into spreadsheet archaeology.

Proof

Operating pattern

A specialty lender with application, decision, and servicing data split across the LOS, decision engine, and warehouse extracts builds a governed decision record that ties policy definitions to outcomes and feeds examiner-ready reporting views. Audit preparation moves from manual file pulls to reusable evidence packages.

Common failure mode

A lender changes policy rules across multiple products with no single governed decision record. Compliance can report outcomes, with no clean way to explain which rule version applied to each decision. A partner-bank review turns into weeks of reconstruction.

The issue is whether the decision record can be trusted.

If your credit decisions require reconstruction, let’s talk.

In 30 minutes, we’ll map your systems, decision records, policy definitions, and the gaps that keep risk and compliance teams working from exports.