Bureau API integrations
Experian, Equifax, TransUnion in UK, US and Canada — with the right contract endpoints for your use case and consent model.
06 — Credit bureaus
Credit bureau engineering — Experian, Equifax, TransUnion and beyond.
Credit bureau integrations look simple — three APIs, three contracts, three datasets. They aren’t. The fault lines are in the data: Experian’s CAIS doesn’t map cleanly to Equifax’s Insight which doesn’t map cleanly to TransUnion’s CallCredit. Your underwriting team makes decisions across files they can’t compare. Pulling the data is the easy 20%. Normalising it is the 80%.
FCA · UK CFPB · US FCAC · Canada
01 — Problem
Where credit bureaus builds usually go wrong.
Three bureaus, three schemas, three contracts, three regulatory regimes per region — and the same customer recorded slightly differently in each one. Underwriting teams end up making decisions across files they can’t directly compare; risk teams can’t reconcile bureau disagreements; and back-reporting to the bureaus (a regulated activity for UK lenders via CAIS / Insight schemes) becomes an annual project rather than a continuous control.
What we build is a canonical-schema integration platform: one query layer above the three bureaus, with normalisation handled at the adapter level and the canonical model stable across changes. Soft- and hard-search journeys share the same canonical model; the underwriting decision is made against a single comparable customer view; and back-reporting pipelines run continuously from the lending ledger rather than as a quarterly export.
Add alt-data (Open Banking, rental, telco, alt-scoring) into the same canonical model and the underwriting team has one view, not five.
02 — What we build
End-to-end, audit-ready, in production.
Soft-search journeys
Footprint-free eligibility checks, GDPR-compliant data minimisation and consistent customer-facing disclosures.
Hard-search orchestration
Multi-bureau routing for the underwriting decision, consent management and ID-verified search submission.
Bureau data normalisation
Canonical schema across providers — so your underwriting layer sees one customer model, not three different ones.
Score and indicator extraction
Credit scores, public records (CCJs, defaults), ID confirmations and behavioural indicators in a normalised feature set.
Bureau back-reporting
CAIS, Insight and CallCredit reporting pipelines — UK regulated requirement, built continuous rather than batch.
Alt-data integration
Open Banking, rental, telco and alt-scoring data integrated into the canonical model alongside bureau files.
Bureau-aware audit trail
Every pull, every consent, every decision and every footprint logged immutably — for FCA, FCRA and PIPEDA review.
03 — Patterns
Build patterns we ship from.
Multi-bureau integration platform
Vendor-abstracted adapter layer with canonical-schema normalisation above and bureau-specific transforms below.
Adapter patternInternal canonical DSLPostgres
Soft-search engine with footprint tracking
Footprint-free eligibility flow with consent management, GDPR-minimised data retention and audit trail.
TransUnion softExperian softAWS Step Functions
Bureau back-reporting pipeline
Continuous CAIS / Insight reporting from the lending ledger, with reconciliation, exception handling and scheme acknowledgment tracking.
dbtSnowflake / PostgresCAIS scheme adapter
Bureau data normalisation layer
Canonical customer-credit model with bureau-specific feature mapping, version management and explainability hooks.
Internal feature storedbtSHAP
04 — Regulations
What this work has to comply with.
UKFCA CONCUSFCRACAPIPEDAGlobalGDPR Art. 22GlobalGDPR Art. 14UKEquality ActUSECOA Reg BUKCAIS / Insight scheme rules
05 — Stack
What we build with.
Bureaus (UK)
ExperianEquifaxTransUnionClearScore
Bureaus (US & Canada)
Experian US / CanadaEquifax US / CanadaTransUnion US / Canada
Alt-data & tooling
Credit KudosPlaidAireInternal feature storeAWS Step Functions
06 — Recent build
Three bureaus, one decisioning surface — a credit-data integration platform that halved underwriting variance for a UK lender.
A UK consumer credit lender was pulling all three bureaus but using only one for the underwriting decision. The other two were “informational” — which in practice meant the underwriting team had three numbers for the same customer and no way to reconcile them. Variance in their first-payment-default rate across bureau-disagreement segments was ~38%.
We built a canonical-schema integration layer: three adapter clients beneath, one comparable customer model above, with bureau-specific transforms versioned and audit-trailed. The underwriting layer was rebuilt to make a single decision against the canonical view — with explicit bureau-disagreement signals as features.
Six months post-launch the lender’s first-payment-default rate variance across bureau-disagreement segments had dropped to ~18%, and the underwriting team had collapsed from five bureau-specific workflows to one canonical workflow.
10 weeks
Build to production
50%
Variance reduction
3 → 1
Underwriting workflows
07 — FAQs
Questions we get every time.
What’s the right architecture for multi-bureau credit data integration?
Three layers: per-bureau adapter clients (thin, stateless, no business logic); a canonical-schema layer above that owns the comparable customer model; and a feature store above that, where the underwriting layer consumes features by name rather than by bureau. The canonical schema is the part that takes the work — and the part that determines whether you can swap a bureau out later without rebuilding everything downstream.
How do you normalise Experian, Equifax and TransUnion into a single schema?
Field-by-field, with versioned mappings and explainability hooks. The hard cases are public records (CCJs are named differently and dated differently across bureaus), account histories (CAIS vs Insight vs CallCredit account-status codes don’t directly map), and behavioural indicators (each bureau has its own derived signals). We resolve these into a canonical model with explicit confidence scoring where the bureaus disagree, and we keep the underlying raw data so the underwriting team can drill down per-decision.
What’s the difference between soft and hard credit search, and how do you build both?
Soft search is footprint-free — it doesn’t appear on the customer’s credit file and doesn’t affect their score. Used for eligibility, pre-qualification and rate-presentation. Hard search is footprint-leaving — it appears on the customer’s file, can affect scoring, and is used at the underwriting decision point. The build pattern differs: soft search uses a different bureau endpoint, has different consent disclosure requirements, and typically returns a thinner data set. We build both with a shared canonical schema so the upgrade from soft to hard at decision-point is data-additive, not a re-pull.
How do you handle FCA CONC affordability rules with bureau data?
Bureau data alone isn’t enough for CONC affordability — it gives you the customer’s existing credit commitments and behavioural indicators, but not their income or disposable income. The defensible pattern is bureau + Open Banking + (optionally) declared income, with the bureau supplying the existing-commitments side of the calculation and Open Banking supplying the income side. Plus an evidence trail that retains the source data and the calculation logic per-decision.
What’s involved in CAIS / Insight back-reporting to the bureaus?
Continuous-ledger sourcing (CAIS / Insight schemes expect monthly contributions at the scheme level, but the right operational pattern is to source continuously from your lending ledger and stage), exact-match identity reconciliation against scheme-side records, exception handling for unmatched submissions, and acknowledgment tracking with reconciliation back to the lending ledger. The non-obvious bit is the exception workflow — a small percentage of submissions will reject every cycle, and unhandled rejections compound into compliance findings.
— Adjacent practices
Most builds combine two or three.
Scoping a credit bureaus build?
Send us a few sentences. We’ll come back inside three business days with a credible delivery plan and a date.