Credit Scoring on Sovereign Infrastructure
Train, deploy, and monitor credit scoring models on infrastructure you own — without sending customer financial data to a cloud provider. RBI model risk management compliant. Sub-100ms inference. Full audit trail.
Why credit scoring in the cloud is a regulatory landmine
Training a credit model requires bureau data, transaction history, account behaviour, and repayment records — all sensitive personal financial data. Sending this to AWS SageMaker, Azure ML, or Google Vertex AI means your customers' financial profiles exist on foreign-controlled infrastructure.
RBI's guidelines on model risk management (MRM) require that banks document model development, validation, and performance monitoring — with the ability to produce a complete model lineage for regulatory examination. Cloud-based training workflows often lack the reproducibility and audit trail MRM requires.
Models must be retrained periodically as economic conditions change. Every retraining cycle is a fresh exposure — your customers' latest financial data going to a cloud provider each time.
AravaliStack's ML platform (GPU-accelerated compute + distributed training framework) runs on your own hardware. Feature pipelines, training jobs, and model evaluation all execute within your network perimeter. Bureau data, transaction records, and customer profiles never leave your data centre.
Every training run is logged: dataset version, feature set, hyperparameters, evaluation metrics, model artefact checksum, and environment specification. The MLflow experiment registry provides the complete model lineage that RBI MRM examinations require — reproducible, tamper-evident, auditable.
Production models are served via KServe — a Kubernetes-native model serving framework. Loan origination systems, mobile apps, and internal credit tools query the inference endpoint with typical response times of 40–80ms for most gradient boosting and neural credit models.
All components run within your on-premise AravaliStack cluster. No data leaves your network at any stage.
Every experiment logged with full parameter set, dataset hash, and environment spec. Export to PDF for regulatory submission.
Shadow model deployments enable champion-challenger testing. Validation team gets read-only access to model artefacts and evaluation data.
Population Stability Index (PSI), GINI coefficient, and KS statistic tracked continuously. Automatic alerts when drift exceeds thresholds.
All model deployments, parameter changes, and data access events logged to tamper-evident audit storage. Full reproducibility guaranteed.
End-to-end data lineage from raw bureau data through feature engineering to training set. No black-box data provenance.
Pre-built report templates for RBI IT examination on model risk — covering model inventory, validation status, performance metrics, and incident history.
Customer financial data leaves your premises Training a credit model requires bureau data, transaction history, account behaviour, and repayment records — all sensitive personal financial data. Sending this to AWS SageMaker, Azure ML, or Google Vertex AI means your customers' financial profiles exist on foreign-controlled infrastructure. RBI model risk management requires full auditability RBI's guidelines on model risk management (MRM) require that banks document model development, validation, and performance monitoring — with the ability to produce a complete model lineage for regulatory examination. Cloud-based training workflows often lack the reproducibility and audit trail MRM requires. Re-training on fresh data is an ongoing data sovereignty problem Models must be retrained periodically as economic conditions change. Every retraining cycle is a fresh exposure — your customers' latest financial data going to a cloud provider each time. The AravaliStack Solution Sovereign ML infrastructure for credit Train entirely on-premise — data never moves AravaliStack's ML platform (GPU-accelerated compute + distributed training framework) runs on your own hardware. Feature pipelines, training jobs, and model evaluation all execute within your network perimeter. Bureau data, transaction records, and customer profiles never leave your data centre. Full MLflow experiment tracking for RBI MRM Every training run is logged: dataset version, feature set, hyperparameters, evaluation metrics, model artefact checksum, and environment specification. The MLflow experiment registry provides the complete model lineage that RBI MRM examinations require — reproducible, tamper-evident, auditable. Sub-100ms inference via KServe Production models are served via KServe — a Kubernetes-native model serving framework. Loan origination systems, mobile apps, and internal credit tools query the inference endpoint with typical response times of 40–80ms for most gradient boosting and neural credit models. Reference Architecture Credit scoring pipeline on AravaliStack 📊 Data Ingestion
Bureau, CBS, transaction history
Feature Store
Real-time + batch features
Training Cluster
GPU nodes — data stays on-prem
Model Registry
MLflow — full lineage
KServe Inference
Sub-100ms serving
Monitoring
Drift detection, performance
- Regulatory Compliance
- How AravaliStack satisfies RBI MRM requirements
- Model Development Documentation
- Independent Validation Support
- RBI Examination Ready
- P99 inference latency for GBM models
- Customer data egress to external systems
Deploy sovereign credit scoring in 30 days.
AravaliStack includes the complete ML infrastructure stack. Bring your models — we provide the compliant platform.