The Crux AI Credit Modelling Platform lets banks, finance companies and fintechs in Saudi Arabia build, validate and deploy production-grade credit scorecards in days, not months, with every decision explainable to risk, audit and regulators.
Application, behavioural and collection models are built, deployed and monitored from the same place, so the data, definitions and governance are shared across the credit lifecycle.
Assess new applicants instantly using many more data points than a traditional model: financial, behavioural, bureau and alternative data. Approve more creditworthy borrowers safely and at speed.
Re-score existing customers continuously using live transaction data. Spot rising stress early enough to act on limits, outreach and provisioning before losses appear.
Predict the chance each delinquent account will cure and assign the most cost-effective strategy automatically, so agents spend time where a conversation changes the result.
Illustrative output, not client data
A complete toolkit for every stage of the model lifecycle, usable without a large data science team or long IT sprints, and built around SAMA, IFRS 9 and Basel expectations.
Connect internal, bureau and alternative data. Imputation, bias checks and automated variable derivation come built in, with no manual engineering.
Build consumer and SME scorecards using ensemble ML, deep learning or traditional WoE logistic regression, with auto-derived features and automatic benchmarking.
Tune binning, PD calibration and variable-selection thresholds from a no-code interface, so the business team stays in control of the model.
Switch between traditional regression, ensemble ML or a hybrid. Interpretability for regulators and accuracy for the business, whichever the use case needs.
What used to take a long development cycle is compressed into a guided workflow, with validation, explainability and model documentation produced along the way instead of afterwards.
Connect any source. Automated validation and data-quality scoring.
Auto-clean, reduce bias and apply WoE transformation.
100+ variables derived automatically, no manual engineering.
Set parameters, choose the algorithm and generate the scorecard.
Cross-validate, explain with SHAP and produce regulatory documentation.
Deploy with one click as a REST API, then monitor drift with automatic alerts.
A zero-code interface backed by enterprise-grade modelling. Analysts with or without a data science background can configure, validate and deploy scorecards without writing code, and data scientists keep detailed control when they want it.
Every decision is transparent, documented and auditable for supervisory review.
Connects to your loan origination and core systems through a documented REST API.
From fintech start-up to tier-one bank, without switching vendors.
Deploy where your policy requires. Your model IP stays yours.
Traditional scorecard development relies on specialist data scientists, long manual feature engineering and a separate validation cycle. A modern platform automates the lifecycle so Saudi banks, fintechs and digital lenders can build IFRS 9-aligned, Basel-aware models at the pace the market demands.
Actual timelines depend on data readiness and your model-approval process. Every model is benchmarked against your current scorecard before it goes live.
Use one product or all of them. Credit Modelling is the foundation layer that powers every scorecard and decision around it.
Approve with confidence using richer data.
Explore → MonitoringBehavioural ScorecardCatch rising risk in your existing book.
Explore → RecoveryCollection ScorecardPredict cure and choose the strategy.
Explore → ExecutionDecision EngineRun scorecard outputs through no-code rules.
Explore → GrowthPropensity ModelsPredict who will buy, churn or upgrade.
Explore →It lets a lender build predictive scorecards using machine learning and statistical techniques. It automates feature engineering, model validation, monitoring and deployment, which shortens development from months to days while keeping every decision explainable to risk teams, auditors and regulators.
AI can evaluate many more variables at once, including behavioural, transactional, bureau and alternative data, and capture non-linear patterns that a traditional model built on a few dozen hand-picked variables can miss. The size of the improvement depends on your data, so Crux benchmarks every model against your current scorecard on a held-out sample before anything goes live.
They can be built to. The platform produces SHAP-based explanations, reason codes, validation reports and a model governance audit trail, which support IFRS 9 provisioning, Basel credit risk practice and SAMA expectations for automated credit decisions. Your risk and compliance teams remain responsible for model approval and regulatory interpretation.
No. The no-code interface lets credit analysts configure data, binning, variable selection and algorithms without writing code, and data scientists can still tune parameters in detail. Crux supports your team through onboarding and first model builds.
Approved models are published as REST APIs that your loan origination or core banking systems call for real-time decisions, or run in batch for portfolio scoring. Deployment can be on public cloud, private cloud or on-premise, and your organisation keeps ownership of the models it builds.
In a focused 30-minute session we look at your current scorecards and data, show how a model would be built on the platform, and outline what a first deployment could look like.