A credit brain your risk team owns and your regulator accepts.
Credit decisioning is the single most defensible AI investment a lender makes. We build firm-specific scorecards that combine open banking, bureau, alt-data and application signals — with explainable adverse-action notices, live A/B champion-challenger, and full audit trail for the FCA and your risk committee.
Why generic scorecards are a compounding disadvantage.
Most lenders under £500m book still run one of three shapes: a bureau-driven scorecard the vendor tunes once a year; a Provenir/Zoral rulebook that the risk team has been meaning to overhaul for two years; or a hand-crafted set of policy rules stitched together in the LMS. All three under-price good risk and over-price bad risk relative to what an open-banking + bureau + alt-data model tuned on the firm's own book can achieve.
The compound cost is enormous. Every basis point of adverse selection costs the loan book. Every falsely-declined good borrower is a customer who will now originate somewhere else. Every borderline case that lands in manual review costs an underwriter's time and slows the funnel. The lenders that have built a firm-specific credit brain in the last three years are extending an increasingly uncatchable per-loan-economics gap.
What a modern credit decisioning system looks like.
A firm-owned brain, not a vendor black box. It should produce:
- A single score per application, combining bureau, open-banking cashflow patterns, application signals, device/graph signals and (where the product allows) alt-data like HMRC, VAT filings, e-commerce revenue or trade-payment data.
- An explainable adverse-action notice for every decline — regulator-defensible, borrower-comprehensible, in production not on a Confluence page.
- A champion-challenger harness the risk team runs live — new models shadow-scored against production, with lift and stability metrics tracked per cohort per product per channel.
- A monotonic-and-bounded architecture so the risk committee, the auditor and the FCA can all follow the logic.
- Full retention of model version, training data version and decision inputs per application — for regulator inspection years later.
What KJ Capital ships.
A bespoke credit decisioning service that sits above your LMS and your bureau/open-banking vendors. We deliver:
- A production scorecard trained on your own historical book, evaluated against a hold-out cohort with published Gini, KS, PSI and calibration metrics.
- An adverse-action notice generator with regulator-defensible explanations, versioned and audit-logged.
- A champion-challenger platform your risk team owns and runs.
- A model registry, evaluation harness and drift monitor.
- The full source, deployed in your cloud tenancy. No black box, no lock-in.
What lenders actually see after go-live.
What buyers ask us about this build.
Q01Do you replace our Provenir / Zoral / LendingMetrics rulebook?+
Q02How is this FCA / Consumer Duty compliant?+
Q03What alt-data sources do you support?+
Q04How do you handle model risk management?+
Q05Price?+
What lenders ship alongside this one.
Application & origination
Every abandoned application is an acquisition cost wasted. We build AI-native application flows that pull open-banking, run KYC/KYB across Sumsub/Onfido, extract documents automatically, decision in seconds and hand a fully-packaged case to your underwriter or straight to drawdown.
Fraud & first-party risk
Every basis point of undetected fraud is a basis point of loan pricing your good borrowers subsidise. We build fraud brains that combine identity, device, graph, behavioural and post-drawdown signals into a single defence — for consumer lenders, SME lenders, bridging shops and BNPL alike.
Treasury & loan book intelligence
Most lender leadership teams cannot answer 'what is the true unit economics of the loans we originated in April, by channel, product and cohort' inside a quarter. We build the loan book intelligence layer that answers it inside a coffee.