Expertise
Credit Risk Scoring
Credit risk scoring for regulated lenders — application and behavioural models, IFRS 9 ECL, decisioning, and bureau integration.
Credit risk scoring is not a data science problem with a regulatory wrapper — it is a regulatory discipline that happens to use data science. Every model has to be statistically sound, operationally stable, explainable to credit teams, defensible to model risk and regulators, and documented to model risk management standards. We work across the full lifecycle: application scoring for new customers, behavioural reassessment of existing books, collections and recoveries models, and IFRS 9 ECL provisioning. Our team has hands-on experience with Experian, Equifax, SAS, and FICO — the platforms most regulated lenders actually use to run this work in production.
What we build
Application scoring
Custom scorecards and ML models for credit decisions on new customers and new exposures — retail, SME, mortgages, motor finance, BNPL, and corporate. We develop models on traditional bureau data, internal application data, and (where appropriate) alternative data sources. Designed to integrate cleanly with origination platforms — FICO Origination Manager, Experian PowerCurve, in-house systems — so the model lives where decisions are actually made, not in an analytics environment that the front office cannot reach.
Behavioural scoring and portfolio management
Ongoing reassessment of existing customer credit risk based on transactional, repayment, and bureau behaviour. Supports credit limit management, pricing decisions, early-warning indicators, and trigger-based collections handoff. Built to refresh on a defined cadence with model performance monitoring built into the same pipeline so degradation is caught before it costs money.
Collections and recoveries scoring
Models that predict cure probability, optimal contact strategy, and expected recovery — driving collections segmentation and treatment decisions. We build for the operational reality of collections functions: queue prioritisation, agent routing, settlement-offer optimisation, and the long-tail of late-stage recovery decisions where small accuracy gains have direct P&L impact.
IFRS 9 ECL and provisioning models
PD, LGD, and EAD models for IFRS 9 staging and expected credit loss calculation, including the macroeconomic scenarios, lifetime-loss estimation, and stage-transfer logic that IFRS 9 actually requires. We also build the supporting infrastructure — scenario weighting, post-model adjustments, governance trail — that auditors and supervisors look for during the annual ECL review.
Model governance, validation, and documentation
The work that determines whether a model survives second-line review and regulatory scrutiny. We produce model development documentation aligned to expectations, build challenger models for ongoing validation, define monitoring thresholds with explicit escalation paths, and document the rollback procedures for the case when monitoring catches a problem. Includes support during model risk committee submissions where it helps.
Real-time decisioning and bureau integration
Implementation of the decisioning layer that sits between the model and the front-line — calling bureau APIs (Experian, Equifax, TransUnion, CRIF), applying scoring models in real time, executing strategy rules from platforms like FICO or SAS, and returning decisions within the latency budgets that origination flows require. We design for fallback behaviour when bureaus or models are unavailable, not just the happy path.
Why lenders work with us on credit risk scoring
Hands-on with the platforms lenders actually use
Our team has practical experience with Experian, Equifax, SAS, and FICO. That covers most of the decisioning and modelling stack that regulated lenders deploy in production.
Classical scorecards and machine learning, used where each fits
Logistic regression scorecards remain the right tool for many regulated credit applications because they are transparent, stable, and well-understood by supervisors. Modern ML approaches — gradient boosting, neural nets, ensemble methods — work better for some segments and applications. We are fluent in both and use whichever fits the business problem, regulatory context, and operational constraints — not whichever is more fashionable in the data science literature.
Built around model risk management standards
Every model we deliver ships with documentation aligned to model risk principles — development methodology, feature attribution (SHAP and traditional), challenger models, ongoing monitoring framework, and rollback procedures. Your second line and supervisors get a model file that is ready for review on day one.
End-to-end, not just the model
A credit risk model that lives only in a notebook is not a credit risk system. We cover the full stack: data engineering, modelling, decisioning integration, monitoring, retraining, and the operational handover. Where mandates need only one piece — for example, a validation review of an existing model — we deliver that, but we know how the piece fits into the larger machine.
Production-grade, not proof-of-concept
We measure success by models running in production with documented owners, monitoring dashboards being looked at, retraining cadence being honoured, and clean supervisor review. Engagements include the operational handover and the monitoring runbook.
Knowledge transfer to your model risk team
A credit risk model that only its consultants understand is a model risk liability. We design engagements so your internal model development, validation, and model risk management functions can own the model after we leave — through paired implementation, model documentation that matches your internal standards, and explicit handover workstreams.
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Rust development for the integration layer of banks and fintechs — ISO 8583 and ISO 20022 message processing, payment routing, and the components where memory safety and predictable performance map directly to operational concerns. A focused capability for the parts of the banking stack where Rust earns its place.
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